Apkon, SD, Alman, B, Birnkrant, DJ, Fitch, R, Lark, R, Mackenzie, W, Weidner, N & Sussman, M 2018, 'Orthopedic and Surgical Management of the Patient With Duchenne Muscular Dystrophy', Pediatrics, vol. 142, no. Supplement_2, pp. S82-S89.
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Orthopedic care is an important aspect of the overall management of patients with Duchenne muscular dystrophy (DMD). In addition to progressive muscle weakness and loss of function, patients may develop joint contractures, scoliosis, and osteoporosis, causing fractures; all of these necessitate intervention by a multidisciplinary team including an orthopedic surgeon as well as rehabilitation specialists such as physio- and occupational therapists. The causes of these musculoskeletal complications are multifactorial and are related to primary effects on the muscles from the disease itself, secondary effects from weak muscles, and the related side effects of treatments, such as glucocorticoid use that affect bone strength. The musculoskeletal manifestations of DMD change over time as the disease progresses, and therefore, musculoskeletal management needs change throughout the life span of an individual with DMD. In this review, we target pediatricians, neurologists, orthopedic surgeons, rehabilitation physicians, anesthesiologists, and other individuals involved in the management of patients with DMD by providing specific recommendations to guide clinical practice related to orthopedic issues and surgical management in this setting.
Bai, F, Vidal-Calleja, T & Huang, S 2018, 'Robust Incremental SLAM Under Constrained Optimization Formulation', IEEE Robotics and Automation Letters, vol. 3, no. 2, pp. 1207-1214.
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© 2016 IEEE. In this letter, we propose a constrained optimization formulation and a robust incremental framework for the simultaneous localization and mapping problem (SLAM). The new SLAM formulation is derived from the nonlinear least squares (NLS) formulation by mathematically formulating loop-closure cycles as constraints. Under the constrained SLAM formulation, we study the robustness of an incremental SLAM algorithm against local minima and outliers as a constraint/loop-closure cycle selection problem. We find a constraint metric that can predict the objective function growth after including the constraint. By the virtue of the constraint metric, we select constraints into the incremental SLAM according to a least objective function growth principle to increase robustness against local minima and perform χ 2 difference test on the constraint metric to increase robustness against outliers. Finally, using sequential quadratic programming (SQP) as the solver, an incremental SLAM algorithm (iSQP) is proposed. Experimental validations are provided to illustrate the accuracy of the constraint metric and the robustness of the proposed incremental SLAM algorithm. Nonetheless, the proposed approach is currently confined to datasets with sparse loop-closures due to its computational cost.
Best, G, Faigl, J & Fitch, R 2018, 'Online planning for multi-robot active perception with self-organising maps', Autonomous Robots, vol. 42, no. 4, pp. 715-738.
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© 2017, Springer Science+Business Media, LLC, part of Springer Nature. We propose a self-organising map (SOM) algorithm as a solution to a new multi-goal path planning problem for active perception and data collection tasks. We optimise paths for a multi-robot team that aims to maximally observe a set of nodes in the environment. The selected nodes are observed by visiting associated viewpoint regions defined by a sensor model. The key problem characteristics are that the viewpoint regions are overlapping polygonal continuous regions, each node has an observation reward, and the robots are constrained by travel budgets. The SOM algorithm jointly selects and allocates nodes to the robots and finds favourable sequences of sensing locations. The algorithm has a runtime complexity that is polynomial in the number of nodes to be observed and the magnitude of the relative weighting of rewards. We show empirically the runtime is sublinear in the number of robots. We demonstrate feasibility for the active perception task of observing a set of 3D objects. The viewpoint regions consider sensing ranges and self-occlusions, and the rewards are measured as discriminability in the ensemble of shape functions feature space. Exploration objectives for online tasks where the environment is only partially known in advance are modelled by introducing goal regions in unexplored space. Online replanning is performed efficiently by adapting previous solutions as new information becomes available. Simulations were performed using a 3D point-cloud dataset from a real robot in a large outdoor environment. Our results show the proposed methods enable multi-robot planning for online active perception tasks with continuous sets of candidate viewpoints and long planning horizons.
Biesenthal, C, Clegg, S, Mahalingam, A & Sankaran, S 2018, 'Applying institutional theories to managing megaprojects', International Journal of Project Management, vol. 36, no. 1, pp. 43-54.
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© 2017 Elsevier Ltd, APM and IPMA This paper contributes to Rodney Turner's initiative to develop a theory of project management from practice. Organizational scholars studying strategy suggest that more attention needs to be paid to practices involved in organizing, as well as the institutional contexts in which these practices are embedded. Taking a cue from strategy-in-practice approaches, it is proposed that institutional theories can be used to address some questions that have not been answered adequately regarding megaprojects. Institutional theories also seem to be gaining the attention of scholars investigating large, global, infrastructure projects as reported in engineering, management and construction journals. Increasingly, it is evident that the problem areas attached to these projects stretch beyond technical issues: they must be considered as socio-technical endeavours embedded in complex institutional frames. The authors suggest that studying how to deal with institutional differences in the environment of megaprojects has both theoretical and practical implications.
Clegg, S, Killen, CP, Biesenthal, C & Sankaran, S 2018, 'Practices, projects and portfolios: Current research trends and new directions', International Journal of Project Management, vol. 36, no. 5, pp. 762-772.
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© 2018 Elsevier Ltd and Association for Project Management and the International Project Management Association Project portfolio management (PPM) bridges strategy and project management. Traditional research in PPM has primarily investigated the rational, top-down and structural aspects of strategizing. By doing so, it has failed to focus on the underlying practices that are triggered by the strategy and how these practices frame strategy implementation. Practice-based research provides a methodological lens to explore the reality of strategic enactment through the project portfolio. Practice-based perspectives are under-represented in PPM research; therefore the aim of this paper is to provide an agenda for further practice-based research in PPM. Central to this agenda is a concern with various aspects of practice, including its discursivity, representation, dynamic capabilities, leadership and materiality.
Cliff, O, Prokopenko, M & Fitch, R 2018, 'Minimising the Kullback–Leibler Divergence for Model Selection in Distributed Nonlinear Systems', Entropy, vol. 20, no. 2, pp. 51-51.
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The Kullback–Leibler (KL) divergence is a fundamental measure of information geometry that is used in a variety of contexts in artificial intelligence. We show that, when system dynamics are given by distributed nonlinear systems, this measure can be decomposed as a function of two information-theoretic measures, transfer entropy and stochastic interaction. More specifically, these measures are applicable when selecting a candidate model for a distributed system, where individual subsystems are coupled via latent variables and observed through a filter. We represent this model as a directed acyclic graph (DAG) that characterises the unidirectional coupling between subsystems. Standard approaches to structure learning are not applicable in this framework due to the hidden variables; however, we can exploit the properties of certain dynamical systems to formulate exact methods based on differential topology. We approach the problem by using reconstruction theorems to derive an analytical expression for the KL divergence of a candidate DAG from the observed dataset. Using this result, we present a scoring function based on transfer entropy to be used as a subroutine in a structure learning algorithm. We then demonstrate its use in recovering the structure of coupled Lorenz and Rössler systems.
Cliff, OM, Saunders, DL & Fitch, R 2018, 'Robotic ecology: Tracking small dynamic animals with an autonomous aerial vehicle', Science Robotics, vol. 3, no. 23.
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Estimation algorithms for wildlife tracking with an autonomous aerial robot are supported by field validation with wild swift parrots.
D’Urso, G, Smith, SL, Mettu, R, Oksanen, T & Fitch, R 2018, 'Multi-vehicle refill scheduling with queueing', Computers and Electronics in Agriculture, vol. 144, pp. 44-57.
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© 2017 We consider the problem of refill scheduling for a team of vehicles or robots that must contend for access to a single physical location for refilling. The objective is to minimise time spent in travelling to/from the refill station, and also time lost to queuing (waiting for access). In this paper, we present principled results for this problem in the context of agricultural operations. We first establish that the problem is NP-hard and prove that the maximum number of vehicles that can usefully work together is bounded. We then focus on the design of practical algorithms and present two solutions. The first is an exact algorithm based on dynamic programming that is suitable for small problem instances. The second is an approximate anytime algorithm based on the branch and bound approach that is suitable for large problem instances with many robots. We present simulated results of our algorithms for three classes of agricultural work that cover a range of operations: spot spraying, broadcast spraying and slurry application. We show that the algorithm is reasonably robust to inaccurate prediction of resource utilisation rate, which is difficult to estimate in cases such as spot application of herbicide for weed control, and validate its performance in simulation using realistic scenarios with up to 30 robots.
Drouin, N, Müller, R, Sankaran, S & Vaagaasar, AL 2018, 'Balancing vertical and horizontal leadership in projects', International Journal of Managing Projects in Business, vol. 11, no. 4, pp. 986-1006.
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PurposeThe purpose of this paper is twofold: to identify how horizontal leaders (within project teams) execute their leadership task in the context of balanced leadership; and to pinpoint scenarios that can occur when horizontal leaders are identified and empowered by the vertical leader (senior or project managers) and a project task is handed over to them to lead. This research is based on the concept of balanced leadership, which conceptualizes leadership as a dynamic, situation-dependent transition of leadership authority from a vertical leader (like a project manager) to a horizontal leader (a project team member) and back again, in order to contribute positively to a project’s success. Balanced leadership consists of five events (nomination, identification, empowerment, horizontal leadership and its governance, and transition). This paper focuses on the fourth event, and its specific aspect of leadership distribution between horizontal and vertical leader. This event begins when a team member(s) accepts the empowerment to assume the role of horizontal leader. This paper explicitly links the leadership style of the vertical leader based on Frame’s (1987) leadership styles and the nature of decisions taken by both the vertical and horizontal leaders to deliver the project.Design/methodology/approachThe method used for this paper is the qualitative phase of a sequential mixed methods (qualitative-quantitative) study. Data were collected through case studies in four different countries, using a maximum variety sampling approach. Data collection was through interviews of vertical leaders (senior leaders who were often sponsors of projects or members of senior management or project managers) and horizontal leaders (team leaders or members) in a variety of industry sectors. D...
Fitch, R, Isler, V, Tokekar, P & Scaramuzza, D 2018, 'Guest editorial: Special issue on active perception', Autonomous Robots, vol. 42, no. 2, pp. 175-176.
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Ghaffari Jadidi, M, Valls Miro, J & Dissanayake, G 2018, 'Gaussian processes autonomous mapping and exploration for range-sensing mobile robots', Autonomous Robots, vol. 42, no. 2, pp. 273-290.
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© 2017, Springer Science+Business Media, LLC. Most of the existing robotic exploration schemes use occupancy grid representations and geometric targets known as frontiers. The occupancy grid representation relies on the assumption of independence between grid cells and ignores structural correlations present in the environment. We develop a Gaussian processes (GPs) occupancy mapping technique that is computationally tractable for online map building due to its incremental formulation and provides a continuous model of uncertainty over the map spatial coordinates. The standard way to represent geometric frontiers extracted from occupancy maps is to assign binary values to each grid cell. We extend this notion to novel probabilistic frontier maps computed efficiently using the gradient of the GP occupancy map. We also propose a mutual information-based greedy exploration technique built on that representation that takes into account all possible future observations. A major advantage of high-dimensional map inference is the fact that such techniques require fewer observations, leading to a faster map entropy reduction during exploration for map building scenarios. Evaluations using the publicly available datasets show the effectiveness of the proposed framework for robotic mapping and exploration tasks.
Gracia, L, Perez-Vidal, C & Valls-Miro, J 2018, 'Advanced Mathematical Methods for Collaborative Robotics', Mathematical Problems in Engineering, vol. 2018, pp. 1-3.
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Gracia, L, Solanes, JE, Muñoz-Benavent, P, Esparza, A, Valls Miro, J & Tornero, J 2018, 'Cooperative transport tasks with robots using adaptive non-conventional sliding mode control', Control Engineering Practice, vol. 78, pp. 35-55.
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© 2018 Elsevier Ltd This work presents a hybrid position/force control of robots aimed at handling applications using multi-task and sliding mode ideas. The proposed robot control is based on a novel adaptive non-conventional sliding mode control used to robustly satisfy a set of inequality constraints defined to accomplish the cooperative transport task. In particular, these constraints are used to guarantee the reference parameters imposed by the task (e.g., keeping the load at a desired orientation) and to guide the robot using the human operator's forces detected by a force sensor located at the robot tool. Another feature of the proposal is the multi-layered nature of the strategy, where a set of four tasks are defined with different priorities. The effectiveness of the proposed adaptive non-conventional sliding mode control is illustrated by simulation results. Furthermore, the applicability and feasibility of the proposed robot control for transport tasks are substantiated by experimental results using a redundant 7R manipulator.
Gracia, L, Solanes, JE, Muñoz-Benavent, P, Valls Miro, J, Perez-Vidal, C & Tornero, J 2018, 'Adaptive Sliding Mode Control for Robotic Surface Treatment Using Force Feedback', Mechatronics, vol. 52, pp. 102-118.
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© 2018 Elsevier Ltd This work presents a hybrid position-force control of robots in order to apply surface treatments such as polishing, grinding, finishing, deburring, etc. The robot force control is designed using sliding mode concepts to benefit from robustness. In particular, the sliding mode force task is defined using equality constraints to attain the desired tool pressure on the surface, as well as to keep the tool orientation perpendicular to the surface. In order to deal with sudden changes in material stiffness, which are ultimately transferred to the polishing tool and can produce instability and compromise polishing performance, several adaptive switching gain laws are considered and compared. Moreover, a lower priority tracking controller is defined to follow the desired reference trajectory on the surface being polished. Hence, deviations from the reference trajectory are allowed if such deviations are required to satisfy the constraints mentioned above. Finally, a third-level task is also considered for the case of redundant robots in order to use the remaining degrees of freedom to keep the manipulator close to the home configuration with safety in mind. The main advantages of the method are increased robustness and low computational cost. The applicability and effectiveness of the proposed approach are substantiated by experimental results using a redundant 7R manipulator: the Rethink Robotics Sawyer collaborative robot.
Hassan, M, Liu, D & Paul, G 2018, 'Collaboration of Multiple Autonomous Industrial Robots through Optimal Base Placements', Journal of Intelligent & Robotic Systems, vol. 90, no. 1-2, pp. 113-132.
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© 2017, Springer Science+Business Media B.V. Multiple autonomous industrial robots can be of great use in manufacturing applications, particularly if the environment is unstructured and custom manufacturing is required. Autonomous robots that are equipped with manipulators can collaborate to carry out manufacturing tasks such as surface preparation by means of grit-blasting, surface coating or spray painting, all of which require complete surface coverage. However, as part of the collaboration process, appropriate base placements relative to the environment and the target object need to be determined by the robots. The problem of finding appropriate base placements is further complicated when the object under consideration is large and has a complex geometric shape, and thus the robots need to operate from a number of base placements in order to obtain complete coverage of the entire object. To address this problem, an approach for Optimization of Multiple Base Placements (OMBP) for each robot is proposed in this paper. The approach aims to optimize base placements for multi-robot collaboration by taking into account task-specific objectives such as makespan, fair workload division amongst the robots, and coverage percentage; and manipulator-related objectives such as torque and manipulability measure. In addition, the constraint of robots maintaining an appropriate distance between each other and relative to the environment is taken into account. Simulated and real-world experiments are carried out to demonstrate the effectiveness of the approach and to verify that the simulated results are accurate and reliable.
Kim, J 2018, 'Robot Navigation and SLAM', Robots and Human: Special Issue on Robot Navigation and SLAM Technology, vol. 15.
Liu, W, Yan, X, Huang, S, Yang, C & Wang, G 2018, 'Advanced Control for Singular Systems with Applications', Mathematical Problems in Engineering, vol. 2018, pp. 1-2.
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Ma, H, Xiong, R, Wang, Y, Kodagoda, S & Shi, L 2018, 'Towards open-set semantic labeling in 3D point clouds : Analysis on the unknown class', Neurocomputing, vol. 275, pp. 1282-1294.
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© 2017 Elsevier B.V. There has been a growing interest in the research of semantic labeling on scenes represented by 3D point clouds. A fundamental issue that has been largely ignored is the unavoidable presence of unknown objects and the lack of effective ways of dealing with them. Traditional methods usually label unknown objects as one of the pre-trained classes which is either a meaningful target class or a defined unknown class that collectively refers to all uninterested objects. Due to the fact that the class of unknown in essence is a collection of many unseen or uninterested classes, in which the in-class variation is significant and less manageable. It is challenging to solve the unknown problem in a pre-trained manner. In order to advance the research on semantic labeling with the presence of unknown objects, this study investigates the feasibility of adopting an open-set approach, i.e. train a model without unknown objects and reject them accurately in the test. In this paper, we propose a method that exploits the conflict of different labeling results in order to withstand the negative effect of unknown objects. The proposed framework relies on a Conditional Random Field (CRF) to capture inherent spatial relationships and appearance similarities between objects, and employs a Probability of Inclusion Support Vector Machine (P I SVM) to estimate an unknown likelihood for each training class. The probabilistic outputs from both CRF and P I SVM are then proposed to be combined under the Dempster Shafer theory for conflict measurement and unkno wn rejection. The novelty lies in that the method encodes both contextual constrains and unknown likelihood for performance enhancement. Comprehensive experimental results on publicly available data sets are presented to show the negative effects of unknown objects and the improvements on labeling accuracy achieved by the proposed method.
Müller, R, Sankaran, S, Drouin, N, Vaagaasar, A-L, Bekker, MC & Jain, K 2018, 'A theory framework for balancing vertical and horizontal leadership in projects', International Journal of Project Management, vol. 36, no. 1, pp. 83-94.
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© 2017 Elsevier Ltd, APM and IPMA This paper develops a framework for understanding the interaction between person-centered leadership by project managers (a.k.a. vertical leadership (VLS)) and team-centered leadership by individuals in the project team (a.k.a. horizontal leadership (HSL)). It builds on Archer's Realist Social Theory and its morphogenetic cycle, which describes the interaction of structure with agency for task fulfillment and the resulting reshaping (morphogenesis) or continuation (morphostasis) of structure for subsequent iterations of the cycle. Data were collected globally in 33 case studies with 166 interviews and analyzed using Alvesson's Constructing Mystery technique. A theory about the cycles and events that shape the interaction between VLS and HLS is developed, which includes events such as nomination, identification, selection, execution and governance, as well as transitioning. Managerial and theoretical implications are discussed.
Patten, T, Martens, W & Fitch, R 2018, 'Monte Carlo planning for active object classification', Autonomous Robots, vol. 42, no. 2, pp. 391-421.
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© 2017, Springer Science+Business Media New York. Classifying objects in complex unknown environments is a challenging problem in robotics and is fundamental in many applications. Modern sensors and sophisticated perception algorithms extract rich 3D textured information, but are limited to the data that are collected from a given location or path. We are interested in closing the loop around perception and planning, in particular to plan paths for better perceptual data, and focus on the problem of planning scanning sequences to improve object classification from range data. We formulate a novel time-constrained active classification problem and propose solution algorithms that employ a variation of Monte Carlo tree search to plan non-myopically. Our algorithms use a particle filter combined with Gaussian process regression to estimate joint distributions of object class and pose. This estimator is used in planning to generate a probabilistic belief about the state of objects in a scene, and also to generate beliefs for predicted sensor observations from future viewpoints. These predictions consider occlusions arising from predicted object positions and shapes. We evaluate our algorithms in simulation, in comparison to passive and greedy strategies. We also describe similar experiments where the algorithms are implemented online, using a mobile ground robot in a farm environment. Results indicate that our non-myopic approach outperforms both passive and myopic strategies, and clearly show the benefit of active perception for outdoor object classification.
Pitsis, A, Clegg, S, Freeder, D, Sankaran, S & Burdon, S 2018, 'Megaprojects redefined – complexity vs cost and social imperatives', International Journal of Managing Projects in Business, vol. 11, no. 1, pp. 7-34.
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PurposeThe purpose of this paper is to provide a brief overview from the literature on how best to define megaprojects in contemporary contexts. There is a need for a definition that encompasses a complex matrix of characteristics, inclusive of positive and negative aspects, which are not necessarily industry or sector specific. Whilst megaprojects have often been described and defined in terms of cost, they are more accurately delineated by their convolutions. Intricacies arise from political intrigues surrounding funding of such projects and managing and governing complex social and organizational relations. Points for future research are also identified.Design/methodology/approachAn analysis of international megaproject literature over the past five years combined with seminal works was undertaken, drawing on the broad literature of project and program management combined with elements of organizational theory. Whilst some examples are cited, in-depth case analysis has not been covered.FindingsAlbeit that the scale of some megaprojects is comparable to national GDPs, seven more characteristics beyond size have been identified, which distinguish megaprojects from large projects. These include: reach; duration; risks and uncertainties; widely disparate actors; areas of controversy such as dispute resolution; and legal and regulatory issues.Research limitations/implicationsThe paper takes a broad overview and whilst some examples are cited, in-depth case analysis has not been covered. The overview does however provide a good synopsis of the future research areas that warrant...
Pollack, J, Biesenthal, C, Sankaran, S & Clegg, S 2018, 'Classics in megaproject management: A structured analysis of three major works', International Journal of Project Management, vol. 36, no. 2, pp. 372-384.
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The paper explores three texts in the field of megaproject management that intersubjectively, in terms of community sentiment, might be
considered ‘classics’. We deploy four criteria for a structured analysis that determines if the status of the works in question may be considered
classic. The works examined are Megaprojects and Risk: An Anatomy of Ambition by Flyvbjerg, Bruzelius and Rothengatter; (2003) The Anatomy
of Major Projects by Morris and Hough (1987) and Industrial Megaprojects by Merrow (2011). Based on these works we conclude with aprospectus for future research that will serve to develop the field of research into megaproject management.
Quyet Le, N, Er, M & Sankaran, S 2018, 'The Implementation of Building Information Modelling (BIM) in Construction Industry: Case Studies in Vietnam', International Journal of Engineering and Technology, vol. 10, no. 4, pp. 335-340.
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The research introduces the combination of Diffusion of Innovation Theory and Activity Theory to investigate the current implementation of BIM in Vietnamese context. Data was collected through three large AEC firms (Architect, Engineering, and Construction) to present the comprehensive understanding of BIM practices in the construction industry. Qualitative approach was employed with the use of semi-structured interview to examine respondents’ perspectives of their daily BIM activities. Interpretive abductive strategy was used to interpret and transform everyday beliefs and meanings into social scientific knowledge that matches Diffusion of Innovation Theory and Activity Theory. Key findings were mediating forces affecting the BIM implementation including tools, rules, division of labour and contradictions among elements in the activity system
Rahman, S, Quin, P, Walsh, T, Vidal-Calleja, T, McPhee, MJ, Toohey, E & Alempijevic, A 2018, 'Preliminary estimation of fat depth in the lamb short loin using a hyperspectral camera', Animal Production Science, vol. 58, no. 8, pp. 1488-1488.
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The objectives of the present study were to describe the approach used for classifying surface tissue, and for estimating fat depth in lamb short loins and validating the approach. Fat versus non-fat pixels were classified and then used to estimate the fat depth for each pixel in the hyperspectral image. Estimated reflectance, instead of image intensity or radiance, was used as the input feature for classification. The relationship between reflectance and the fat/non-fat classification label was learnt using support vector machines. Gaussian processes were used to learn regression for fat depth as a function of reflectance. Data to train and test the machine learning algorithms was collected by scanning 16 short loins. The near-infrared hyperspectral camera captured lines of data of the side of the short loin (i.e. with the subcutaneous fat facing the camera). Advanced single-lens reflex camera took photos of the same cuts from above, such that a ground truth of fat depth could be semi-automatically extracted and associated with the hyperspectral data. A subset of the data was used to train the machine learning model, and to test it. The results of classifying pixels as either fat or non-fat achieved a 96% accuracy. Fat depths of up to 12 mm were estimated, with an R2 of 0.59, a mean absolute bias of 1.72 mm and root mean square error of 2.34 mm. The techniques developed and validated in the present study will be used to estimate fat coverage to predict total fat, and, subsequently, lean meat yield in the carcass.
Sankaran, S 2018, 'Megaproject management and leadership: a narrative analysis of life stories – past and present', International Journal of Managing Projects in Business, vol. 11, no. 1, pp. 53-79.
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PurposeThe purpose of this paper is to glean leadership lessons of megaproject managers through the life stories of four purposefully selected managers from two contemporary and two landmark megaprojects.Design/methodology/approachA narrative inquiry approach applying thematic analysis is used to capture lessons learnt from these stories with a focus on leading megaprojects. Narrative analysis has been used in organization studies and this paper is an attempt to use it in project management research.FindingsCommon strategies used by all four megaproject managers to be successful include: selecting the right people and building their capability; building trust with stakeholders; dealing with institutional power and politics effectively; and having the courage to innovate. There were also some differences in the approaches used by these managers due the times in which these projects were implemented.Research limitations/implicationsThe use of narrative inquiry is new to project management literature. As the life stories were not presented in the same way it was difficult to analyze them in the same manner, and further data had to be collected. This could have been avoided if it were feasible to collect narratives directly from the megaproject managers. This is being planned in future research emerging from this paper.Practical implicationsThis study helps megaproject managers to exhibit leadership attributes that would be required to execute such large complex projects that ha...
Shi, L & Miro, JV 2018, 'Towards optimised and reconstructable sampling inspection of pipe integrity for improved efficiency of non-destructive testing', Water Supply, vol. 18, no. 2, pp. 515-523.
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Abstract This work proposes a sampling inspection framework for point measurement non-destructive testing of pipelines to improve its time and cost efficiencies. Remaining pipe wall thickness data from limited dense inspection are modelled with spatial statistics approaches. The spatial dependence in the available data and some subjective requirements provide a reference for selecting a most efficient sampling inspection scheme. With the learned model and the selected sampling scheme, the effort of inspecting the residual part of the same pipeline or cohort will be significantly reduced from dense inspection to sampling inspection, and the full information can be reconstructed from samples while maintaining a reasonable accuracy. The recovered thickness map can be used as an equivalent measure to the dense inspection for subsequent structural analysis for failure risk estimation or remaining life assessment.
Shiozaki, T & Dissanayake, G 2018, 'Eliminating Scale Drift in Monocular SLAM Using Depth From Defocus', IEEE Robotics and Automation Letters, vol. 3, no. 1, pp. 581-587.
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This letter presents a novel approach to correct errors caused by accumulated scale drift in monocular SLAM. It is shown that the metric scale can be estimated using information gathered through monocular SLAM and image blur due to defocus. A nonlinear least squares optimization problem is formulated to integrate depth estimates from defocus to monocular SLAM. An algorithm to process the output keyframe and feature location estimates generated by a monocular SLAM algorithm to correct for scale drift at selected local regions of the environment is presented. The proposed algorithm is experimentally evaluated by processing the output of ORB-SLAM to obtain accurate metric scale maps from a monocular camera without any prior knowledge about the scene.
Solanes, JE, Gracia, L, Muñoz-Benavent, P, Esparza, A, Valls Miro, J & Tornero, J 2018, 'Adaptive robust control and admittance control for contact-driven robotic surface conditioning', Robotics and Computer-Integrated Manufacturing, vol. 54, pp. 115-132.
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© 2018 Elsevier Ltd This work presents a hybrid position/force control of robots for surface contact conditioning tasks such as polishing, profiling, deburring, etc. The robot force control is designed using sliding mode ideas to benefit from robustness. On the one hand, a set of equality constraints are defined to attain the desired tool pressure on the surface, as well as to keep the tool orientation perpendicular to the surface. On the other hand, inequality constraints are defined to adapt the tool position to unmodeled features present in the surface, e.g., a protruding window frame. Conventional and non-conventional sliding mode controls are used to fulfill the equality and inequality constraints, respectively. Furthermore, in order to deal with sudden changes of the material stiffness, which are forwarded to the robot tool and can produce instability and bad performance, adaptive switching gain laws are considered not only for the conventional sliding mode control but also for the non-conventional sliding mode control. A lower priority tracking controller is also defined to follow the desired reference trajectory on the target surface. Moreover, the classical admittance control typically used in force control tasks is adapted for the proposed surface contact application in order to experimentally compare the performance of both control approaches. The effectiveness of the proposed method is substantiated by experimental results using a redundant 7R manipulator, whereas its advantages over the classical admittance control approach are experimentally shown.
Solanes, JE, Gracia, L, Muñoz-Benavent, P, Valls Miro, J, Carmichael, MG & Tornero, J 2018, 'Human–robot collaboration for safe object transportation using force feedback', Robotics and Autonomous Systems, vol. 107, pp. 196-208.
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© 2018 Elsevier B.V. This work presents an approach based on multi-task, non-conventional sliding mode control and admittance control for human–robot collaboration aimed at handling applications using force feedback. The proposed robot controller is based on three tasks with different priority levels in order to cooperatively perform the safe transportation of an object with a human operator. In particular, a high-priority task is developed using non-conventional sliding mode control to guarantee safe reference parameters imposed by the task, e.g., keeping a load at a desired orientation (to prevent spill out in the case of liquids, or to reduce undue stresses that may compromise fragile items). Moreover, a second task based on a hybrid admittance control algorithm is used for the human operator to guide the robot by means of a force sensor located at the robot tool. Finally, a third low-priority task is considered for redundant robots in order to use the remaining degrees of freedom of the robot to achieve a pre-set secondary goal (e.g., singularity avoidance, remaining close to a homing configuration for increased safety, etc.) by means of the gradient projection method. The main advantages of the proposed method are robustness and low computational cost. The applicability and effectiveness of the proposed approach are substantiated by experimental results using a redundant 7R manipulator: the Sawyer collaborative robot.
Solanes, JE, Gracia, L, Muñoz-Benavent, P, Valls Miro, J, Girbés, V & Tornero, J 2018, 'Human-robot cooperation for robust surface treatment using non-conventional sliding mode control', ISA Transactions, vol. 80, pp. 528-541.
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© 2018 ISA This work presents a human-robot closely collaborative solution to cooperatively perform surface treatment tasks such as polishing, grinding, deburring, etc. The method considers two force sensors attached to the manipulator end-effector and tool: one sensor is used to properly accomplish the surface treatment task, while the second one is used by the operator to guide the robot tool. The proposed scheme is based on task priority and adaptive non-conventional sliding mode control. The applicability of the proposed approach is substantiated by experimental results using a redundant 7R manipulator: the Sawyer cobot.
Song, J, Wang, J, Zhao, L, Huang, S & Dissanayake, G 2018, 'Dynamic Reconstruction of Deformable Soft-Tissue With Stereo Scope in Minimal Invasive Surgery', IEEE Robotics and Automation Letters, vol. 3, no. 1, pp. 155-162.
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© 2016 IEEE. In minimal invasive surgery, it is important to rebuild and visualize the latest deformed shape of soft-tissue surfaces to mitigate tissue damages. This letter proposes an innovative Simultaneous localization and mapping (SLAM) algorithm for deformable dense reconstruction of surfaces using a sequence of images from a stereoscope. We introduce a warping field based on the embedded deformation nodes with three-dimensional (3-D) shapes recovered from consecutive pairs of stereo images. The warping field is estimated by deforming the last updated model to the current live model. Our SLAM system can incrementally build a live model by progressively fusing new observations with vivid accurate texture; estimate the deformed shape of unobserved region with the principle as-rigid-as-possible; show the consecutive shape of models; and estimate the current relative pose between the soft-tissue and the scope. In-vivo experiments with publicly available datasets demonstrate that the 3-D models can be incrementally built for different soft-tissues with different deformations from sequences of stereo images obtained by laparoscopes. Results show the potential clinical application of our SLAM system for providing surgeon useful shape and texture information in minimal invasive surgery.
Song, J, Wang, J, Zhao, L, Huang, S & Dissanayake, G 2018, 'MIS-SLAM: Real-Time Large-Scale Dense Deformable SLAM System in Minimal Invasive Surgery Based on Heterogeneous Computing', IEEE Robotics and Automation Letters, vol. 3, no. 4, pp. 4068-4075.
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© 2016 IEEE. Real-time simultaneous localization and dense mapping is very helpful for providing virtual reality and augmented reality for surgeons or even surgical robots. In this letter, we propose MIS-SLAM: A complete real-time large-scale dense deformable SLAM system with stereoscope in minimal invasive surgery (MIS) based on heterogeneous computing by making full use of CPU and GPU. Idled CPU is used to perform ORB-SLAM for providing robust global pose. Strategies are taken to integrate modules from CPU and GPU. We solved the key problem raised in the previous work, that is, fast movement of scope and blurry images make the scope tracking fail. Benefiting from improved localization, MIS-SLAM can achieve large-scale scope localizing and dense mapping in real time. It transforms and deforms current model and incrementally fuses new observation while keeping vivid texture. In-vivo experiments conducted on publicly available datasets presented in the form of videos demonstrate the feasibility and practicality of MIS-SLAM for potential clinical purpose.
Thiyagarajan, K, Kodagoda, S, Ranasinghe, R, Vitanage, D & Iori, G 2018, 'Robust sensing suite for measuring temporal dynamics of surface temperature in sewers', Scientific Reports, vol. 8, no. 1.
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AbstractSewerage systems are paramount underground infrastructure assets for any nation. In most cities, they are old and have been exposed to significant microbial induced corrosion. It is a serious global problem as they pose threats to public health and economic repercussions to water utilities. For managing sewer assets efficaciously, it is vital to predict the rate of corrosion. Predictive models of sewer corrosion incorporate concrete surface temperature measurements as an observation. However, currently, it has not been fully utilized due to unavailability of a proven sensor. This study reports the feasibility of infrared radiometer for measuring the surface temperature dynamics in the aggressive sewer conditions. The infrared sensor was comprehensively evaluated in the laboratory at different environmental conditions. Then, the sensor suite was deployed in a Sydney based sewer for three months to perform continuous measurements of surface temperature variations. The field study revealed the suitability of the developed sensor suite for non-contact surface temperature measurements in hostile sewer conditions. Further, the accuracy of the sensor measurements was improved by calibrating the sensor with emissivity coefficient of the sewer concrete. Overall, this study will ameliorate the present sewer corrosion monitoring capabilities by providing new data to models predicting sewer corrosion.
Thiyagarajan, K, Kodagoda, S, Van Nguyen, L & Ranasinghe, R 2018, 'Sensor Failure Detection and Faulty Data Accommodation Approach for Instrumented Wastewater Infrastructures', IEEE Access, vol. 6, no. 1, pp. 56562-56574.
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© 2013 IEEE. In wastewater industry, real-time sensing of surface temperature variations on concrete sewer pipes is paramount in assessing the rate of microbial-induced corrosion. However, the sensing systems are prone to failures due to the aggressively corrosive environmental conditions inside sewer assets. Therefore, reliable sensing in such infrastructures is vital for water utilities to enact efficient wastewater management. In this context, this paper presents a sensor failure detection and faulty data accommodation (SFDFDA) approach that aids to digitally monitor the health conditions of the sewer monitoring sensors. The SFDFDA approach embraces seasonal autoregressive integrated moving average model with a statistical hypothesis testing technique for enabling temporal forecasting of sensor variable. Then, it identifies and isolates anomalies in a continuous stream of sensor data whilst detecting early sensor failure. Finally, the SFDFDA approach provides reliable estimates of sensor data in the event of sensor failure or during the scheduled maintenance period of sewer monitoring systems. The SFDFDA approach was evaluated by using the surface temperature data sourced from the instrumented wastewater infrastructure and the results have demonstrated the effectiveness of the SFDFDA approach and its applicability to surface temperature monitoring sensor suites.
To, AWK, Paul, G & Liu, D 2018, 'A comprehensive approach to real-time fault diagnosis during automatic grit-blasting operation by autonomous industrial robots', Robotics and Computer-Integrated Manufacturing, vol. 49, pp. 13-23.
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© 2017 Elsevier Ltd This paper presents a comprehensive approach to diagnose for faults that may occur during a robotic grit-blasting operation. The approach proposes the use of information collected from multiple sensors (RGB-D camera, audio and pressure transducers) to detect for 1) the real-time position of the grit-blasting spot and 2) the real-time state within the blasting line (i.e. compressed air only). The outcome of this approach will enable a grit-blasting robot to autonomous diagnose for faults and take corrective actions during the blasting operation. Experiments are conducted in a laboratory and in a grit-blasting chamber during real grit-blasting to demonstrate the proposed approach. Accuracy of 95% and above has been achieved in the experiments.
Ulapane, N, Alempijevic, A, Valls Miro, J & Vidal-Calleja, T 2018, 'Non-destructive evaluation of ferromagnetic material thickness using Pulsed Eddy Current sensor detector coil voltage decay rate', NDT & E International, vol. 100, pp. 108-114.
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© 2018 Elsevier Ltd A ferromagnetic material thickness quantification method based on the decay rate of the Pulsed Eddy Current sensor detector coil voltage is proposed. An expression for the decay rate is derived and the relationship between the decay rate and material thickness is established. Pipe wall thickness estimation is done with a developed circular sensor incorporating the proposed method, and results are evaluated through destructive testing. The decay rate feature has a unique attribute of being lowly dependent on properties such as sensor shape and size, and lift-off, enabling the method to be usable with any detector coil-based sensor. A case study on using the proposed method with a commercial sensor is also presented to demonstrate its versatility.
Valls Miro, J, Ulapane, N, Shi, L, Hunt, D & Behrens, M 2018, 'Robotic pipeline wall thickness evaluation for dense nondestructive testing inspection', Journal of Field Robotics, vol. 35, no. 8, pp. 1293-1310.
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AbstractThis paper addresses automated mapping of the remaining wall thickness of metallic pipelines in the field by means of an inspection robot equipped with nondestructive testing (NDT) sensing. Set in the context of condition assessment of critical infrastructure, the integrity of arbitrary sections in the conduit is derived with a bespoke robot kinematic configuration that allows dense pipe wall thickness discrimination in circumferential and longitudinal direction via NDT sensing with guaranteed sensing lift‐off (offset of the sensor from pipe wall) to the pipe wall, an essential barrier to overcome in cement‐lined water pipelines. A tailored covariance function for pipeline cylindrical structures within the context of a Gaussian Processes has also been developed to regress missing sensor data incurred by a sampling strategy folllowed in the field to speed up the inspection times, given the slow response of the pulsed eddy current electromagnetic sensor proposed. The data gathered represent not only a visual understanding of the condition of the pipe for asset managers, but also constitute a quantative input to a remaining‐life calculation that defines the likelihood of the pipeline for future renewal or repair. Results are presented from deployment of the robotic device on a series of pipeline inspections which demonstrate the feasibility of the device and sensing configuration to provide meaningful 2.5D geometric maps.
Waldron, KJ 2018, 'Bernard Roth: The early days of the design division at Stanford, and the beginnings of research in robotics', Mechanism and Machine Theory, vol. 125, pp. 45-51.
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© 2017 This paper comprises a review of Bernard Roth's technical contributions and contributions to his professional community. Particular attention is paid to his role in the establishment of the unique design program of the Department of Mechanical Engineering at Stanford University. Another theme is the creation of one of the very first research programs in digitally controlled robotics in the Stanford Artificial Intelligence Laboratory. No review of Roth's career would be complete without touching on the numerous fundamental contributions to research in linkages and robotics. At the same time it is not possible in a work on this type to examine every one of his publications and other contributions. We have endeavored to select the most important, but that is, of course, a personal judgment.
Wang, H, Huang, S, Yang, G & Dissanayake, G 2018, 'Comparison of two different objective functions in 2D point feature SLAM', Automatica, vol. 97, pp. 172-181.
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© 2018 Elsevier Ltd This paper compares two different objective functions in 2D point feature Simultaneous Localization and Mapping (SLAM). It is shown that the objective function can have a significant impact on the convergence of the iterative optimization techniques used in SLAM. When Frobenius norm is adopted for the error term of the orientation part of odometry, the SLAM problem has much better convergence properties, as compared with that using the angle difference as the error term. For one-step case, we have proved that there is one and only one minimum to the SLAM problem, and strong duality always holds. For two-step case, strong duality always holds except when three very special conditions hold simultaneously (which happens with probability zero), thus the global optimal solution to primal SLAM problem can be obtained by solving the corresponding Lagrangian dual problem in most cases. Further, for arbitrary m-step cases, we also show using examples that much better convergence results can be obtained. Simulation examples are given to demonstrate the different convergence properties using two different objective functions.
Wang, M, Liu, Y, Su, D, Liao, Y, Shi, L, Xu, J & Valls Miro, J 2018, 'Accurate and Real-Time 3-D Tracking for the Following Robots by Fusing Vision and Ultrasonar Information', IEEE/ASME Transactions on Mechatronics, vol. 23, no. 3, pp. 997-1006.
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© 1996-2012 IEEE. Acquiring the accurate three-dimensional (3-D) position of a target person around a robot provides valuable information that is applicable to a wide range of robotic tasks, especially for promoting the intelligent manufacturing processes of industries. This paper presents a real-time robotic 3-D human tracking system that combines a monocular camera with an ultrasonic sensor by an extended Kalman filter (EKF). The proposed system consists of three submodules: a monocular camera sensor tracking module, an ultrasonic sensor tracking module, and the multisensor fusion algorithm. An improved visual tracking algorithm is presented to provide 2-D partial location estimation. The algorithm is designed to overcome severe occlusions, scale variation, target missing, and achieve robust redetection. The scale accuracy is further enhanced by the estimated 3-D information. An ultrasonic sensor array is employed to provide the range information from the target person to the robot, and time of flight is used for the 2-D partial location estimation. EKF is adopted to sequentially process multiple, heterogeneous measurements arriving in an asynchronous order from the vision sensor, and the ultrasonic sensor separately. In the experiments, the proposed tracking system is tested in both a simulation platform and actual mobile robot for various indoor and outdoor scenes. The experimental results show the persuasive performance of the 3-D tracking system in terms of both the accuracy and robustness.
Wang, S, Kodagoda, S, Shi, L & Dai, X 2018, 'Two-Stage Road Terrain Identification Approach for Land Vehicles Using Feature-Based and Markov Random Field Algorithm', IEEE Intelligent Systems, vol. 33, no. 1, pp. 29-39.
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© 2001-2011 IEEE. Road terrain identification is one of the important tasks for driving assistant systems or autonomous land vehicles. It plays a key role in improving driving strategy and enhancing fuel efficiency. In this paper, a two-stage approach using multiple sensors is presented. In the first stage, a feature-based identification approach is performed using an accelerometer, a camera, and downward-looking and forward-looking laser range finders (LRFs). This produces four classification label sequences. In the second stage, a majority vote is implemented for each label sequences to match them into synchronized road patches. Then a Markov Random Field (MRF) model is designed to generate the final optimized identification results to improve the forward-looking LRF. This approach enables the vehicle to observe the upcoming road terrain before moving onto it by fusing all the classification results using an MRF algorithm. The experiments show this approach improved the terrain identification accuracy and robustness significantly for some familiar road terrains.
Yang, Z, Yu, C, Kim, J, Li, Z & Wang, L 2018, 'Evolution of cooperation driven by majority-pressure based interdependence', New Journal of Physics, vol. 20, no. 8, pp. 083047-083047.
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Zhao, L, Huang, S & Dissanayake, G 2018, 'Linear SFM: A hierarchical approach to solving structure-from-motion problems by decoupling the linear and nonlinear components', ISPRS Journal of Photogrammetry and Remote Sensing, vol. 141, pp. 275-289.
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© 2018 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) This paper presents a novel hierarchical approach to solving structure-from-motion (SFM) problems. The algorithm begins with small local reconstructions based on nonlinear bundle adjustment (BA). These are then joined in a hierarchical manner using a strategy that requires solving a linear least squares optimization problem followed by a nonlinear transform. The algorithm can handle ordered monocular and stereo image sequences. Two stereo images or three monocular images are adequate for building each initial reconstruction. The bulk of the computation involves solving a linear least squares problem and, therefore, the proposed algorithm avoids three major issues associated with most of the nonlinear optimization algorithms currently used for SFM: the need for a reasonably accurate initial estimate, the need for iterations, and the possibility of being trapped in a local minimum. Also, by summarizing all the original observations into the small local reconstructions with associated information matrices, the proposed Linear SFM manages to preserve all the information contained in the observations. The paper also demonstrates that the proposed problem formulation results in a sparse structure that leads to an efficient numerical implementation. The experimental results using publicly available datasets show that the proposed algorithm yields solutions that are very close to those obtained using a global BA starting with an accurate initial estimate. The C/C++ source code of the proposed algorithm is publicly available at https://github.com/LiangZhaoPKUImperial/LinearSFM.
Alvarez, JK & Kodagoda, S 1970, 'Application of deep learning image-to-image transformation networks to GPR radargrams for sub-surface imaging in infrastructure monitoring', 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), IEEE, Wuhan, China, pp. 611-616.
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The corrosion of reinforced concrete sewer pipes in aging infrastructure is a serious ongoing issue and as such, research into technologies that allow for autonomous site assessments are of major priority to wastewater managing utilities. The use of Ground Penetrating Radar (GPR) is being investigated for providing sub-surface images of sewer crowns. Due to the nature of GPRs, the analysis of a radargram for identifying sub-surface features is non-intuitive and usually require the use of an expert. Traditional methods to help ease analysis involve the use of Synthetic Aperture Radar (SAR) and migration techniques. These techniques refocus dipping and point reflectors to be closer to their true shape but require an accurate velocity model to be effective. This is not always readily available and difficult to estimate especially in regards to sewer conditions. We instead provide an alternative and present a deep learning framework for transforming ground penetrating radargrams into sub-surface permittivity maps. An evaluation of various state-of-the-art deep learning architectures is also conducted, comparing the performance of different objective functions and identifying current limitations. This work provides the base for further exploration of the application of deep learning for use in infrastructure monitoring.
Arora, A, Furlong, PM, Fitch, R, Fong, T, Sukkarieh, S & Elphic, R 1970, 'Online Multi-modal Learning and Adaptive Informative Trajectory Planning for Autonomous Exploration', Field and Service Robotics, Field and Service Robotics, Springer International Publishing, Zurich, Switzerland, pp. 239-254.
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In robotic information gathering missions, scientists are typically interested in understanding variables which require proxy measurements from specialized sensor suites to estimate. However, energy and time constraints limit how often these sensors can be used in a mission. Robots are also equipped with cheaper to use navigation sensors such as cameras. In this paper, we explore a challenging planning problem in which a robot is required to learn about a scientific variable of interest in an initially unknown environment by planning informative paths and deciding when and where to use its sensors. To tackle this we present two innovations: a Bayesian generative model framework to automatically learn correlations between expensive science sensors and cheaper to use navigation sensors online, and a sampling based approach to plan for multiple sensors while handling long horizons and budget constraints. Our approach does not grow in complexity with data and is anytime making it highly applicable to field robotics. We tested our approach extensively in simulation and validated it with real data collected during the 2014 Mojave Volatiles Prospector Mission. Our planning algorithm performs statistically significantly better than myopic approaches and at least as well as a coverage-based algorithm in an initially unknown environment while having added advantages of being able to exploit prior knowledge and handle other intricacies of the real world without further algorithmic modifications.
Bai, F, Vidal-Calleja, T, Huang, S & Xiong, R 1970, 'Predicting Objective Function Change in Pose-Graph Optimization', 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Madrid, Spain, pp. 145-152.
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© 2018 IEEE. Robust online incremental SLAM applications require metrics to evaluate the impact of current measurements. Despite its prevalence in graph pruning, information-theoretic metrics solely are insufficient to detect outliers. The optimal value of the objective function is a better choice to detect outliers but cannot be computed unless the problem is solved. In this paper, we show how the objective function change can be predicted in an incremental pose-graph optimization scheme, without actually solving the problem. The predicted objective function change can be used to guide online decisions or detect outliers. Experiments validate the accuracy of the predicted objective function, and an application to outlier detection is also provided, showing its advantages over M-estimators.
Best, G, Forrai, M, Mettu, RR & Fitch, R 1970, 'Planning-Aware Communication for Decentralised Multi-Robot Coordination', 2018 IEEE International Conference on Robotics and Automation (ICRA), 2018 IEEE International Conference on Robotics and Automation (ICRA), IEEE, Brisbane, QLD, Australia, pp. 1050-1057.
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© 2018 IEEE. We present an algorithm for selecting when to communicate during online planning phases of coordinated multi-robot missions. The key idea is that a robot decides to request communication from another robot by reasoning over the predicted information value of communication messages over a sliding time-horizon, where communication messages are probability distributions over action sequences. We formulate this problem in the context of the recently proposed decentralised Monte Carlo tree search (Dec-MCTS) algorithm for online, decentralised multi-robot coordination. We propose a particle filter for predicting the information value, and a polynomial-time belief-space planning algorithm for finding the optimal communication schedules in an online and decentralised manner. We evaluate the benefit of informative communication planning for a multi-robot information gathering scenario with 8 simulated robots. Our results show reductions in channel utilisation of up to four-fifths with surprisingly little impact on coordination performance.
Best, G, Huang, S & Fitch, R 1970, 'Decentralised Mission Monitoring with Spatiotemporal Optimal Stopping', 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Madrid, Spain, pp. 4810-4817.
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© 2018 IEEE. We consider a multi-robot variant of the mission monitoring problem. This problem arises in tasks where a robot observes the progress of another robot that is stochastically following a known trajectory, among other applications. We formulate and solve a variant where multiple tracker robots must monitor a single target robot, which is important because it enables the use of multi-robot systems to improve task performance in practice, such as in marine robotics missions. Our algorithm coordinates the behaviour of the trackers by computing optimal single-robot paths given a probabilistic representation of the other robots' paths. We employ a decentralised scheme that optimises over probability distributions of plans and has useful analytical properties. The planned trajectories collectively maximise the probability of observing the target throughout the mission with respect to probabilistic motion and observation models. We report simulation results for up to 8 robots that support our analysis and indicate that our algorithm is a feasible solution for improving the performance of mission monitoring systems.
Bykerk, L & Liu, D 1970, 'Experimental Verification of a Completely Soft Gripper for Grasping and Classifying Beam Members in Truss Structures', 2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), IEEE, Auckland, New Zealand, pp. 756-761.
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© 2018 IEEE. Robotic object exploration and identification methods to date have attempted to mimic human Exploratory Procedures (EPs) using complex, rigid robotic hands with multifaceted sensory suites. For applications where the target objects may have different or unknown cross-sectional shapes and sizes (e.g. beam members in truss structures), rigid grippers are not a good option as they are unable to adapt to the target objects. This may make it very difficult to recognise the shape and size of a beam member and the approaching angles which would result in a secure grasp. To best meet the requirements of adaptability and compliancy, a soft robotic gripper with simple exteroceptive force sensors has been designed. This paper experimentally verifies the gripper design by assessing its performance in grasping and adapting to a variety of target beam members in a truss structure. The sensor arrangement is also assessed by verifying that sufficient data is extracted during a grasp to recognise the approaching angle of the gripper. Firstly, the gripper is used to grasp each beam member from various angles of approach and readings from the force sensors are collected. Secondly, the collected sensor data is used to train and then test a range of commonly used classifiers for classification of the angle of approach. Thirdly, the classification results are analysed. Through this process, it is found that the gripper is proficient in grasping the variety of target beam members. Despite the uncertainty in the gripper pose, the sensor data collected from the soft gripper during a grasp is sufficient for classification of the angles of approach.
Caruana, A & Vidal-Calleja, T 1970, 'Very low complexity convolutional neural network for quadtree structures', Australasian Conference on Robotics and Automation, ACRA, Australian Robotics and Automation Association, ARAA, Lincoln, New Zealand, pp. 1-8.
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In this paper, we present a Very Low Complexity Convolutional Neural Network (VLC-CNN) for the purpose of generating quadtree data structures for image segmentation. The use of quadtrees to encode images has applications including video encoding and robotic perception, with examples including the Coding Tree Unit in the High Efficiency Video Coding (HEVC) standard and Occupancy Grid Maps (OGM) as environment representations with variable grid-size. While some methods for determining quadtree structures include brute-force algorithms or heuristics, this paper describes the use of a Convolutional Neural Network (CNN) to predict the quadtree structure. CNNs traditionally require substantial computational and memory resources to operate, however, VLC-CNN exploits downsampling and integer-only quantised arithmetic to achieve minimal complexity. Therefore, VLC-CNN's minimal design makes it feasible for implementation in realtime or memory-constrained processing applications.
Chen, Y, Huang, S, Fitch, R & Yu, J 1970, 'Efficient Active SLAM Based on Submap Joining, Graph Topology and Convex Optimization', 2018 IEEE International Conference on Robotics and Automation (ICRA), 2018 IEEE International Conference on Robotics and Automation (ICRA), IEEE, Brisbane, QLD, Australia, pp. 5159-5166.
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© 2018 IEEE. The active SLAM problem considered in this paper aims to plan a robot trajectory for simultaneous localization and mapping (SLAM) as well as for an area coverage task with robot pose uncertainty. Based on a model predictive control (MPC) framework, these two problems are solved respectively by different methods. For the uncertainty minimization MPC problem, based on the graphical structure of the 2D feature-based SLAM, a non-convex constrained least-squares problem is presented to approximate the original problem. Then, using variable substitutions, it is further transformed into a convex problem, and then solved by a convex optimization method. For the coverage task considering robot pose uncertainty, it is formulated and solved by the MPC framework and the sequential quadratic programming (SQP) method. In the whole process, considering the computation complexity, we use linear SLAM, which is a submap joining approach, to reduce the time for planning and estimation. Finally, various simulations are presented to validate the effectiveness of the proposed approach.
Dai, B, Le Gentil, C & Vidal-Calleja, T 1970, 'Connecting the dots for real-time LiDAR-based object detection with YOLO', Australasian Conference on Robotics and Automation, ACRA, Australasian Conference on Robotics and Automation, Lincoln, New Zealand.
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In this paper we introduce a generic method for people and vehicle detection using LiDAR data only, leveraging a pre-trained Convolutional Neural Network (CNN) from the RGB domain. Typically with machine learning algorithms, there is an inherent trade-off between the amount of training data available and the need for engineered features. The current state-of-the-art object detection and classification heavily rely on deep CNNs trained on enormous RGB image datasets. To take advantage of this inbuilt knowledge, we propose to fine-tune You only look once (YOLO) network transferring its understanding about object shapes to upsampled LiDAR images. Our method creates a dense depth/intensity map, which highlights object contours, from the 3D-point cloud of a LiDAR scan. The proposed method is hardware agnostic, hence can be used with any LiDAR data, independently on the number of channels or beams. Overall, the proposed pipeline exploits the notable similarity between upsampled LiDAR images and RGB images preventing the need to train a deep CNN from scratch. This transfer learning makes our method data efficient while avoiding the creation of heavily engineered features. Evaluation results show that our proposed LiDAR-only detection model has equivalent performance to its RGB-only counterpart.
Falque, R, Patel, M & Biehl, J 1970, 'Optimizing Placement and Number of RF Beacons to Achieve Better Indoor Localization', 2018 IEEE International Conference on Robotics and Automation (ICRA), 2018 IEEE International Conference on Robotics and Automation (ICRA), IEEE, Brisbane, QLD, Australia, pp. 2304-2311.
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© 2018 IEEE. In this paper, we propose a novel solution to optimize the deployment of Radio Frequency (RF) beacons for the purpose of indoor localization. We propose a system that optimizes both the number of beacons and their placement in a given environment. We propose a novel cost-function, called CovBsm, that allows to simultaneously optimize the 3-coverage while maximizing the beacon spreading. Using this cost function, we propose a framework that maximize both the number of beacons and their placement in a given environment. The proposed solution accounts for the indoor infrastructure and its influence on the RF signal propagation by embedding a realistic simulator into the optimization process.
Gracia, L, Solanes, JE, Munoz-Benavent, P, Miro, JV, Perez-Vidal, C & Tornero, J 1970, 'A Sliding Mode Control Architecture for Human-Manipulator Cooperative Surface Treatment Tasks', 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Madrid, Spain, pp. 1318-1325.
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© 2018 IEEE. This paper presents a control architecture readily suitable for surface treatment tasks such as polishing, grinding, finishing or deburring as carried out by a human operator, with the added benefit of accuracy, recurrence and physical strength as administered by a robotic manipulator partner. The shared strategy effectively couples the human operator propioceptive abilities and fine skills through his interactions with the autonomous physical agent. The novel proposed control scheme is based on task prioritization and a non-conventional sliding mode control, which is considered to benefit from its inherent robustness and low computational cost. The system relies on two force sensors, one located between the last link of the robot and the surface treatment tool, and the other located in some place of the robot end-effector: the former is used to suitably accomplish the conditioning task, while the latter is used by the operator to manually guide the robotic tool. When the operator chooses to cease guiding the tool, the robot motion safely switches back to an automatic reference tracking. The paper presents the theories for the novel collaborative controller, whilst its effectiveness for robotic surface treatment is substantiated by experimental results using a redundant 7R manipulator and a mock-up conditioning tool.
Hassan, M & Liu, D 1970, 'A Deformable Spiral Based Algorithm to Smooth Coverage Path Planning for Marine Growth Removal', 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Madrid, Spain, pp. 1913-1918.
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© 2018 IEEE. Ahstract- Marine growths that flourish on the surfaces of underwater structures, such as bridge pylons, make the inspection and maintenance of these structures challenging. A robotic solution, using an Intervention Autonomous Underwater Vehicle (I-AUV), is developed for removing marine growth. This paper presents a Deformable Spiral Coverage Path Planning (DSCPP) algorithm for marine growth removal. DSCPP generates smooth paths to prevent damage to the surfaces of the structures and to avoid frequent or aggressive decelerations and accelerations due to sharp turns. DSCPP generates a spiral path within a circle and analytically maps the path to a minimum bounding rectangle which encompasses an area of a surface with marine growth. It aims to achieve a spiral path with minimal length while preventing missed areas of coverage. Several case studies are presented to validate the algorithm. Comparison results show that DSCPP outperforms the popular boustrophedon-based coverage approach when considering the requirements for the application under consideration.
Hassan, M & Liu, D 1970, 'Performance Evaluation of an Evolutionary Multiobjective Optimization Based Area Partitioning and Allocation Approach', 2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), IEEE, Auckland, New Zealand, pp. 527-532.
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© 2018 IEEE. An Area Partitioning and Allocation (APA) approach was presented in[1]. The approach focused on optimizing the coverage performance of Autonomous Industrial Robots (AIRs) using multiple conflicting objectives and Voronoi partitioning. However, questions related to the optimality, convergence, and consistency of the Pareto solutions were not studied in details. In this paper, Inverted Generational Distance (IGD) metric is used to verify the convergence of the Pareto front towards Pareto optimal front (PF∗). The consistency in obtaining similar Pareto fronts for independent optimization runs is studied. The computational complexity of the approach with respect to the size of the coverage area and the number of AIRs is also discussed. Two application scenarios are used in this research.
Hunt, D, Hussein, M, Stewart, C, Dissanayake, G, Miro, JV, Olson, J & Rossi, R 1970, 'Rapid response non-destructive inspection robot for condition assessment of critical water mains', Australasian Conference on Robotics and Automation, ACRA, Australasian Conference on Robotics and Automation, ARAA, Lincoln University, New Zealand, pp. 1-7.
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This paper presents a robotic system that is able to rapidly assess the wall thickness of a cement lined cast iron (CI) water main pipe during the short time interval between a pipe failure and its repair. Wall thickness measurement through a cement lining of unknown depth is achieved using a sensor based on the pulsed eddy current (PEC) technique. Sensor geometry is selected such that remaining wall thickness' up to 20mm can be reliably measured. A six arm mechanism incorporating inbuilt compliance allows contact between the sensors and cement lining to be maintained even when the cement lining thickness is non-uniform; which is typically the case with in-situ lined pipes. A cart capable of navigating debris and steps transports the sensing mechanism through the pipe and also ensures it is positioned concentrically within a range of pipe sizes. Descriptions of the sensing strategy, sensor mechanism, driving cart and the robot control system are presented together with results from actual in-field pipe deployments to demonstrate effectiveness of the developed system.
Jadidi, MG, Patel, M, Miro, JV, Dissanayake, G, Biehl, J & Girgensohn, A 1970, 'A Radio-Inertial Localization and Tracking System with BLE Beacons Prior Maps', 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN), IEEE, Nantes, France, pp. 206-212.
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© 2018 IEEE. In this paper, we develop a system for the low-cost indoor localization and tracking problem using radio signal strength indicator, Inertial Measurement Unit (IMU), and magnetometer sensors. We develop a novel and simplified probabilistic IMU motion model as the proposal distribution of the sequential Monte-Carlo technique to track the robot trajectory. Our algorithm can globally localize and track a robot with a priori unknown location, given an informative prior map of the Bluetooth Low Energy (BLE) beacons. Also, we formulate the problem as an optimization problem that serves as the Backend of the algorithm mentioned above (Front-end). Thus, by simultaneously solving for the robot trajectory and the map of BLE beacons, we recover a continuous and smooth trajectory of the robot, corrected locations of the BLE beacons, and the time-varying IMU bias. The evaluations achieved using hardware show that through the proposed closed-loop system the localization performance can be improved; furthermore, the system becomes robust to the error in the map of beacons by feeding back the optimized map to the Front-end.
Katuwandeniya, K, Ranasinghe, R, Dantanarayana, L, Dissanayake, G & Liu, D 1970, 'Calibration of a Rotating Laser Range Finder using Intensity Features.', ICARCV, International Conference on Control, Automation, Robotics and Vision, IEEE, Singapore, Singapore, pp. 228-234.
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© 2018 IEEE. This paper presents an algorithm for calibrating a '3D range sensor' constructed using a two-dimensional laser range finder (LRF), that is rotated about an axis using a motor to obtain a three-dimensional point cloud. The sensor assembly is modelled as a two degree of freedom open kinematic chain, with one joint corresponding to the axis of the internal mirror in the LRF and the other joint set along the axis of the motor used to rotate the body of the LRF. In the application described in this paper, the sensor unit is mounted on a robot arm used for infrastructure inspection. The objective of the calibration process is to obtain the coordinate transform required to compute the locations of the 3D points with respect to the robot coordinate frame. Proposed strategy uses observations of a set of markers arbitrarily placed in the environment. Distances between these markers are measured and a metric multidimensional scaling is used to obtain the coordinates of the markers with respect to a local coordinate frame. Intensity associated with each beam point of a laser scan is used to locate the reflective markers in the 3D point cloud and a least squares problem is formulated to compute the relationship between the robot coordinate frame, LRF coordinate frame and the marker coordinate frame. Results from experiments using the robot, LRF combination to map a cavity inside a steel bridge structure are presented to demonstrate the effectiveness of the calibration process.
Kong, FH & Manchester, IR 1970, 'Iterative Learning of Energy-Efficient Dynamic Walking Gaits', 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp. 3815-3820.
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Dynamic walking robots have the potential for efficient and lifelike locomotion, but computing efficient gaits and tracking them is difficult in the presence of under-modeling. Iterative Learning Control (ILC) is a method to learn the control signal to track a periodic reference over several attempts, augmenting a model with online data. Terminal ILC (TILC), a variant of ILC, allows other performance objectives to be addressed at the cost of ignoring parts of the reference. However, dynamic walking robot gaits are not necessarily periodic in time. In this paper, we adapt TILC to jointly optimize final foot placement and energy efficiency on dynamic walking robots by indexing by a phase variable instead of time, yielding 'phase-indexed TILC' (θ - TILC). When implemented on a five-link walker in simulation, θ- TILC learns a more energy-efficient walking motion compared to traditional time-indexed TILC.
Kong, FH, Lee, KMB & Manchester, IR 1970, 'Motivating Diverse Student Cohorts with Problem-Based Learning in Undergraduate Control Engineering', Australasian Association for Engineering Education Annual Conference, Manly. Australia.
Lai, Y, Poon, J, Paul, G, Han, H & Matsubara, T 1970, 'Probabilistic Pose Estimation of Deformable Linear Objects', 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), IEEE, Munich, Germany, pp. 471-476.
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© 2018 IEEE. This paper presents a probabilistic framework for online tracking of nodes along deformable linear objects. The proposed framework does not require an a-priori model; instead, a Bayesian Committee Machine, starting as a tabula rasa, accumulates knowledge over time. The key benefits of this approach are a lack of reliance upon extensive pre-training data, which can be difficult to obtain in sufficiently large quantities, and the ability for robust estimation of nodes subject to occlusion. Another benefit is that the uncertainties obtained during inference from the underlying Gaussian Processes can be beneficial towards subsequent handling tasks. Comparisons of the non-time series framework were conducted against conventional regression models to measure the efficacy of the proposed framework.
Lai, Y, Sutjipto, S, Clout, MD, Carmichael, MG & Paul, G 1970, 'GAVRe2: Towards Data-Driven Upper-Limb Rehabilitation with Adaptive-Feedback Gamification', 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO), IEEE, Kuala Lumpur, Malaysia, pp. 164-169.
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This paper presents Game Adaptive Virtual Reality Rehabilitation (GAVRe2), a framework to augment upper limb rehabilitation using Virtual Reality (VR) gamification and haptic robotic manipulator feedback. GAVRe2 integrates independent systems in a modular fashion, connecting patients with therapists remotely to increase patient engagement during rehabilitation.
GAVRe2 exploits VR capabilities to not only increase the productivity of therapists administering rehabilitation, but also to improve rehabilitation mobility for patients. Conventional rehabilitation requires face-to-face physical interactions in a clinical setting which can be inconvenient for patients. The GAVRe2 approach provides an avenue for rehabilitation in a
domestic setting by remotely customizing a routine for the patient. Results are then reported back to therapists for data analysis and future training regime development.
GAVRe2 is evaluated experimentally through a system that integrates a popular VR system, a RGB-D camera, and a collaborative industrial robot, with results indicating potential benefits for long-term rehabilitation and the opportunity for upper limb rehabilitation in a domestic setting.
Le Gentil, C, Vidal-Calleja, T & Huang, S 1970, '3D Lidar-IMU Calibration Based on Upsampled Preintegrated Measurements for Motion Distortion Correction', 2018 IEEE International Conference on Robotics and Automation (ICRA), 2018 IEEE International Conference on Robotics and Automation (ICRA), IEEE, Brisbane, pp. 2149-2155.
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© 2018 IEEE. In this paper, we present a probabilistic framework to recover the extrinsic calibration parameters of a lidar-IMU sensing system. Unlike global-shutter cameras, lidars do not take single snapshots of the environment. Instead, lidars collect a succession of 3D-points generally grouped in scans. If these points are assumed to be expressed in a common frame, this becomes an issue when the sensor moves rapidly in the environment causing motion distortion. The fundamental idea of our proposed framework is to use preintegration over interpolated inertial measurements to characterise the motion distortion in each lidar scan. Moreover, by using a set of planes as a calibration target, the proposed method makes use of lidar point-to-plane distances to jointly calibrate and localise the system using on-manifold optimisation. The calibration does not rely on a predefined target as arbitrary planes are detected and modelled in the first lidar scan. Simulated and real data are used to show the effectiveness of the proposed method.
Lee, KMB, Lee, JJH, Yoo, C, Hollings, B & Fitch, R 1970, 'Active perception for plume source localisation with underwater gliders', Australasian Conference on Robotics and Automation, ACRA, Australasian Conference on Robotics and Automation, ARAA Website, Lincoln, New Zealand, pp. 1-10.
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We consider the problem of localising an unknown underwater plume source in an energy-optimal manner. We first develop a specialised Gaussian process (GP) regression technique for estimating the source location given concentration measurements and an ambient flow field. Then, we use the GP upper confidence bound (GP-UCB) for active perception to choose sampling locations that both improve the estimate of the source and lead the glider to the correct source location. A trim-based FMT∗planner is then used to find the sequence of controls that minimise the energy consumption. We provide a theoretical guarantee on the performance of the algorithm, and demonstrate the algorithm using both artificial and experimental datasets.
Liu, J, Sankaran, S, Ke, Y & Xu, X 1970, 'Developing a Value Capture Index for Social Infrastructure Public- Private Partnership Projects', IRNOP 2018, Melbourne, Australia.
Nguyen, L, Ulapane, N & Miro, JV 1970, 'Adaptive sampling for spatial prediction in environmental monitoring using wireless sensor networks: A review', 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), IEEE, Wuhan, China, pp. 346-351.
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© 2018 IEEE. The paper presents a review of the spatial prediction problem in the environmental monitoring applications by utilizing stationary and mobile robotic wireless sensor networks. First, the problem of selecting the best subset of stationary wireless sensors monitoring environmental phenomena in terms of sensing quality is surveyed. Then, predictive inference approaches and sampling algorithms for mobile sensing agents to optimally observe spatially physical processes in the existing works are analysed.
Poon, J, Cui, Y, Miro, JV & Matsubara, T 1970, 'Learning Mobility Aid Assistance via Decoupled Observation Models', 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), IEEE, Singapore, Singapore, pp. 1903-1910.
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© 2018 IEEE. This paper presents an active assistance framework for mobility systems, such as Power Mobility Devices (PMD), with the distinctive goal of being able to operate within a local moving window, as opposed to the common reliance upon persistent global environments and objectives. Demonstration data from able experts driving a simulated mobility aid in a representative indoor setting is used off-line to build behavioral models of navigation postulated separately upon user joystick inputs and on-board sensor data. These models are built respectively via Gaussian Processes for the joystick signals, and a Deep Convolutional Neural Network for the sensor data; in this case a planar LIDAR. Their combined outputs form a continuous distribution of estimated traversal likelihood within the user's immediate space, allowing for real-time stochastic optimal path planning to guide a user to its intended local destination. Moreover, the computational efficiency of the decoupled models permits rapid replanning on-the-fly for a smooth assistive action. On-line and off-line evaluations substantiate the advantages of the framework in generalising intelligent navigational assistance, of particular relevance for users who experience difficulty in safe mobility.
Ramon Soria, P, Sukkar, F, Martens, W, Arrue, BC & Fitch, R 1970, 'Multi-view Probabilistic Segmentation of Pome Fruit with a Low-Cost RGB-D Camera', ROBOT 2017: Third Iberian Robotics Conference, Iberian Robotics Conference, Springer International Publishing, Seville, Spain, pp. 320-331.
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Ranasinghe, R, Dissanayake, G & Liu, D 1970, 'Sensing for Autonomous Navigation Inside Steel Bridges', 2018 IEEE SENSORS, 2018 IEEE Sensors, IEEE, New Delhi, India, pp. 1-4.
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© 2018 IEEE. The main contribution of this paper is a strategy to build a map of a bridge structure and estimate the precise location of a robot within it. In particular, the focus is on the autonomous navigation of a robot inside the steel arches that support the Sydney Harbour Bridge. A two dimensional laser range finder sensor, rotated about an axis perpendicular to its spin axis is used to capture the geometry of the environment in the form of a set of three-dimensional points; a point cloud. First, the approximate robot location is estimated by exploiting the fact that the environment predominantly consists of planes. Using this location estimate as an initial guess, the iterative closest point (ICP) algorithm is used to align point clouds obtained from nearby locations. Results from the ICP, together with a simultaneous localisation and mapping algorithm is then used to obtain accurate estimates of the locations of all the poses from where information is gathered, as well as a complete map of the environment. Results from experiments are used to demonstrate the effectiveness of proposed techniques.
Ranasinghe, R, Dissanayake, G & Liu, D 1970, 'Sensing for autonomous navigation inside steel bridges', 2018 IEEE SENSORS, 17th IEEE SENSORS Conference, IEEE, New Delhi, INDIA, pp. 1511-1514.
Seong, H, Choi, H, Son, H & Kim, C 1970, 'Image-based 3D Building Reconstruction Using A-KAZE Feature Extraction Algorithm', Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC), 34th International Symposium on Automation and Robotics in Construction, International Association for Automation and Robotics in Construction (IAARC), Germany, pp. 1052-1052.
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© ISARC 2018 - 35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things. All rights reserved. Sensor technologies play a significant role in monitoring the health conditions of urban sewer assets. Currently, the concrete sewer systems are undergoing corrosion due to bacterial activities on the concrete surfaces. Therefore, water utilities use predictive models to estimate the corrosion by using observations such as relative humidity or surface moisture conditions. Surface moisture conditions can be estimated by electrical resistivity based moisture sensing. However, the measurements of such sensors are influenced by the proximal presence of reinforcing bars. To mitigate such e ects, the moisture sensor needs to be optimally oriented on the concrete surface. This paper focuses on developing a machine learning model for localizing the reinforcing bars inside the concrete through non-invasive measurements. This work utilizes a resistivity meter that works based on the Wenner technique to obtain electrical measurements on the concrete sample by taking measurements at di erent angles. Then, the measured data is fed to a Gaussian Markov Random Fields based spatial prediction model. The spatial prediction outcome of the proposed model demonstrated the feasibility of localizing the reinforcing bars with reasonable accuracy for the measurements taken at di erent angles. This information is vital for decision-making while deploying the moisture sensors in sewer systems.
Shakor, P, Nejadi, S & Paul, G 1970, 'An investigation into the behaviour of cementitious mortar in the construction of 3D printed members by the means of extrusion printing', 1st International Conference on 3D Construction Printing, Melbourne, Australia.
Shakor, P, Nejadi, S, Paul, G & Sanjayan, J 1970, 'A Novel Methodology of Powder-based Cementitious Materials in 3D Inkjet Printing for Construction Applications', 6th International Conference on Durability of Concrete Structures, ICDCS 2018, Sixth International Conference on Durability of Concrete Structures, Whittles Publishing, University of Leeds, Leeds, West Yorkshire, LS2 9JT, United Kingdom, pp. 685-695.
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Recently, additive manufacturing techniques such as 3D printing are becoming increasingly popular and widely used in a variety of applications. Inkjet 3D printing (i.e. powder-based printing) is one of the most reliable frequently-implemented techniques in 3D printers. This paper discusses a novel methodology to replace the currently used typical powders in 3D printing to make it possible to use the printed specimens in construction applications. The printed cubic (20?20?20mm) and prism (60?5?5mm) specimens with different saturation levels are printed to investigate the relative strength of the 3D printed specimens. Curing in different saturation environments can increase their strength and durability. In general, the experimental results show that the highest compressive strength was recorded (14.68MPa) for the samples that are first cured in water then dried in an oven for one hour at 40°C, comparing to the samples that are cured without drying at 40°C (4.81MPa). Accordingly, it has been discovered that the post-processing technique has an effective and significant impact on the strength of the printed specimens. Furthermore, samples which are cast using manual mixing have been also been compared in detail.
Shalbafan, S, Leigh, E, Pollack, J & Sankaran, S 1970, 'Decision-making in project portfolio management: using the Cynefin framework to understand the impact of complexity', Project Management Research and Practice, International Research Network on Organizing by Projects, University of Technology, Sydney, Boston University, United States, pp. 1-20.
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Synopsis The majority of project portfolio management tools are not flexible and responsive to complex and dynamic environments. This can result in business losses when management does not effectively adjust project portfolios to meet organizational and contextual needs. This paper concentrates on the impact of individual decision-making, perceptions of decision processes and the influence of uncertainty on effective decision-making in project portfolio management. Relevance for practice and education This research explores the impact of real-time events on managers during decision-making processes for project portfolio management, using a purpose-built simulation. The simulation development was informed by the Cynefin framework. The Cynefin framework emphasizes the importance of applying different leadership styles and decision-making approaches depending upon the complexity of the situation. Research design A multi-method, abductive research process was used to collect and analyse the data. Data collection involved four complete iterations of a purpose-built simulation, resulting in 66 datasets of individuals’ perspectives of the project portfolio management decision-making process, under varying levels of complexity. The research data were focused on participants’ perceptions of their efforts to manage key decision turning points through two “real-time” events, simulating project cancellations and organizational change. Main findings Participants were found to use different approaches to decision-making, depending on the complexity of the situation. The findings show that participants’ roles in the simulation, participants’ experience, decision makers’ feeling, the maturity of team cognition, and diversity of participants are key considerations that influence the success of decision-making under uncertainty in PPM contexts. Research implications The findings in this study build on previous research in a number of ways. They demonstrate the effectiven...
Shi, L & Miro, JV 1970, 'Towards optimised and reconstructable sampling inspection of pipe integrity for improved efficiency of non-destructive testing', WATER SCIENCE AND TECHNOLOGY-WATER SUPPLY, 2016 IWA World Water Congress, IWA PUBLISHING, Brisbane, Queensland, Australia, pp. 515-523.
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Thiyagarajan, K, Kodagoda, S, Nguyen, LV & Wickramanayake, S 1970, 'Gaussian Markov Random Fields for Localizing Reinforcing Bars in Concrete Infrastructure', Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC), 34th International Symposium on Automation and Robotics in Construction, International Association for Automation and Robotics in Construction (IAARC).
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Tran, A, Liu, D, Ranasinghe, R & Carmichael, M 1970, 'A Method for Quantifying a Robot’s Confidence in its Human Co-worker in Human-Robot Cooperative Grit-Blasting', 50th International Symposium on Robotics, ISR 2018, International Symposium on Robotics, Munich, pp. 474-481.
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In cooperative Human-Robot operations with physical contact, the human is generally in control while the robot assists the human. However, if the performance of the human were to decrease due to factors such as fatigue or distractions, there should be a mechanism that allows the robot to measure the performance of human operator and intervene in the interaction if needed. This becomes more important in physical Human-Robot Interactions such as cooperative grit-blasting, as the safety of the human may be affected if their performance decreases. In this work, a method for measuring the confidence of a robot in its human operator is presented. This method is then verified in a Human-Robot cooperative grit-blasting operation.
Tran, A, Liu, D, Ranasinghe, R & Carmichael, M 1970, 'Identifying Human Hand Orientation around a Cylindrical Handlebar for physical Human-Robot Interaction', 50th International Symposium on Robotics, ISR 2018, International Symposium on Robotics, Munich, pp. 427-434.
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This paper is concerned with identifying the orientation of the human hand relative to a cylindrical handlebar. In physical Human-Robot Interaction, a handlebar is commonly used as the point of contact between the human operator and the robot. Identifying the orientation of the operator’s hand provides the robot with additional information on how the operator interacts with the robot. A flexible sensor array composed of 160 pressure sensing cells was wrapped around a cylindrical handlebar. Grasping patterns of ten subjects was recorded. Support Vector Machine (SVM) and Bayesian Inference classifiers were implemented to identify the hand orientation of a subject relative to the handlebar. Principal Component Analysis (PCA) was used to reduce the number of features in the classification. Comparisons between the classifiers of SVM and Bayesian Inference, with/without PCA, were conducted for evaluating their accuracy. Two scenarios were used in the comparisons: in the first scenario, the training data and the test data were different but from the same subject; in the second scenario, the training data and the test data were from different subjects.
Ulapane, N, Nguyen, L, Miro, JV & Dissanayake, G 1970, 'A solution to the inverse pulsed eddy current problem enabling 3D profiling', 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), IEEE, Wuhan, China, pp. 1267-1272.
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© 2018 IEEE. When a Pulsed Eddy Current (PEC) sensor assesses a metallic surface (i.e., a wall of finite thickness), the inverse problem involves quantification of the geometry and material properties of the wall. Once a PEC sensor is calibrated for a particular material, and the material under test happens to be considerably homogeneous, the inverse problem reduces to quantification of geometry alone. The state-of-the-art in the industry produces a quantification of this geometry only in the form of average wall thickness remaining underneath the sensor footprint, and produces a 2.5D map containing wall thickness information. Therefore, this paper contributes by proposing a solution that can jointly estimate the remaining wall thickness as well as lift-off (i.e., offset from the sensor to the surface of healthy material), in order to advance PEC sensing outputs by enabling estimation of wall condition in 3D. Since PEC maps are used as inputs for stress calculation and remaining life prediction of certain infrastructure like critical pipes, 3D profiles may become a richer form of input for such applications than 2.5D maps. Since PEC sensing is commonly used to assess ferromagnetic materials, this paper focuses on similar materials as well. The solution is demonstrated in simulation alone and future work should focus on experimental implementations.
Unicomb, J, Ranasinghe, R, Dantanarayana, L & Dissanayake, G 1970, 'A Monocular Indoor Localiser Based on an Extended Kalman Filter and Edge Images from a Convolutional Neural Network', 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Madrid, Spain, pp. 1-9.
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The main contribution of this paper is an extended Kalman filter (EKF)based algorithm for estimating the 6 DOF pose of a camera using monocular images of an indoor environment. In contrast to popular visual simultaneous localisation and mapping algorithms, the technique proposed relies on a pre-built map represented as an unsigned distance function of the ground plane edges. Images from the camera are processed using a Convolutional Neural Network (CNN)to extract a ground plane edge image. Pixels that belong to these edges are used in the observation equation of the EKF to estimate the camera location. Use of the CNN makes it possible to extract ground plane edges under significant changes to scene illumination. The EKF framework lends itself to use of a suitable motion model, fusing information from any other sensors such as wheel encoders or inertial measurement units, if available, and rejecting spurious observations. A series of experiments are presented to demonstrate the effectiveness of the proposed technique.
Valls, MJ, Hunt, D, Ulapane, N & Behrens, M 1970, 'Field and Service Robotics', Field and Service Robotics: Results of the 11th International Conference, Conference on Field and Service Robotics, Springer International Publishing, Zurich, pp. 319-334.
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Virgona, A, Alempijevic, A & Vidal-Calleja, TA 1970, 'Socially Constrained Tracking in Crowded Environments Using Shoulder Pose Estimates.', ICRA, IEEE International Conference on Robotics and Automation, IEEE, Brisbane, QLD, Australia, pp. 1-9.
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© 2018 IEEE. Detecting and tracking people is a key requirement in the development of robotic technologies intended to operate in human environments. In crowded environments such as train stations this task is particularly challenging due the high numbers of targets and frequent occlusions. In this paper we present a framework for detecting and tracking humans in such crowded environments in terms of 2D pose (x, y, θ). The main contributions are a method for extracting pose from the most visible parts of the body in a crowd, the head and shoulders, and a tracker which leverages social constraints regarding peoples orientation, movement and proximity to one another, to improve robustness in this challenging environment. The framework is evaluated on two datasets: one captured in a lab environment with ground truth obtained using a motion capture system, and the other captured in a busy inner city train station. Pose errors are reported against the ground truth and the tracking results are then compared with a state-of-the-art person tracking framework.
Wang, J, Song, J, Zhao, L & Huang, S 1970, 'A Submap Joining Based RGB-D SLAM Algorithm Using Planes as Features', 11th Conference on Field and Service Robotics (FSR 2017), 11th Conference on Field and Service Robotics (FSR 2017), Springer International Publishing, Zurich, Switzerland, pp. 367-382.
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Wang, T, Lu, W & Liu, D 1970, 'A Case Study: Modeling of A Passive Flexible Link on A Floating Platform for Intervention Tasks', 2018 13th World Congress on Intelligent Control and Automation (WCICA), 2018 13th World Congress on Intelligent Control and Automation (WCICA), IEEE, Changsha, China, pp. 187-193.
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© 2018 IEEE. This paper focuses on modeling of a robotic system consisting of a floating platform and a passive flexible-link, which is subjected to three-dimensional large bending deformation during intervention tasks. It investigates the feasibility and efficacy of the quasi-Lagrangian approach and the Euler-Bernoulli beam assumption in modeling this system. Simulations and experiments were conducted to evaluate the model. Then the contact force was calculated with given external input force along with the pose and velocities of the robot, which is validated by the measurements obtained from force-torque sensors. It also found that the accelerations calculated from the model have some deviation from the results obtained from a tracking system.
Wang, T, Lu, W & Liu, D 1970, 'Excessive disturbance rejection control of autonomous underwater vehicle using reinforcement learning', Australasian Conference on Robotics and Automation, ACRA, 2018 Australasian Conference on Robotics and Automation, Lincoln, New Zealand.
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Small Autonomous Underwater Vehicles (AUV) in shallow water might not be stabilized well by feedback or model predictive control. This is because wave and current disturbances may frequently exceed AUV thrust capabilities and disturbance estimation and prediction models available are not sufficiently accurate. In contrast to classical model-free Reinforcement Learning (RL), this paper presents an improved RL for Excessive disturbance rejection Control (REC) that is able to learn and utilize disturbance behaviour, through formulating the disturbed AUV dynamics as a multi-order Markov chain. The unobserved disturbance behaviour is then encoded in the AUV state-action history of fixed length, its embeddings are learned within the policy optimization. The proposed REC is further enhanced by a base controller that is pre-trained on iterative Linear Quadratic Regulator (iLQR) solutions for a reduced AUV dynamic model, resulting in hybrid-REC. Numerical simulations on pose regulation tasks have demonstrated that REC significantly outperforms a canonical controller and classical RL, and that the hybrid-REC leads to more efficient and safer sampling and motion than REC.
Xu, X, Sankaran, S & Ke, Y 1970, 'An Investigation of the Relationship between Opportunism and Innovation during the Build Phase in PPP Projects', EURAM 2018, EURAM 2018, Reykjavik, Iceland.
Xu, X, Sankaran, S, Ke, Y & Liu, J 1970, 'Opportunism Forms of Public-Private Partnership Projects in China: Principal-Agent Relationship Perspective', International Research Network on Organizing by Projects, Melbourne, Australia.
Zhang, Y, Zhang, T & Huang, S 1970, 'Comparison of EKF based SLAM and optimization based SLAM algorithms', 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), IEEE, Wuhan, China, pp. 1308-1313.
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© 2018 IEEE. This paper compares the recent developed state-of-the-art extended Kalman filter (EKF) based simultaneous localization and mapping (SLAM) algorithm, namely, invariant EKF SLAM, with the nonlinear least squares optimization based SLAM algorithms. Simulations in 1D, 2D, and 3D are used to evaluate the invariant EKF SLAM algorithm. It is demonstrated that in most 2D/3D scenarios with practical noise levels, the accuracy of invariant EKF is very close to that of nonlinear least squares optimization based SLAM. In the simple 1D case, the Kalman filter results and the linear least squares results are exactly the same (for any noise levels) due to the linear motion model and linear observation model involved.
Zhao, J, Huang, S & Zhao, L 1970, 'Constrained Gaussian mixture models based scan matching method', Australasian Conference on Robotics and Automation, ACRA, Australasian Conference on Robotics and Automation, ARAA, New Zealand, pp. 1-8.
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This paper presents a Gaussian mixture model (GMM) based robust scan matching method which implements GMM to represent 2D scan points and improves the accuracy of scan matching. The proposed method transfers each new scan to GMM first, exploiting the covariance of every GMM component to represent scan points. Compared with the conventional GMM based method of scan matching, our technique implements GMM similarity comparison to evaluate the overlaps between scans. In order to get rid of the poor convergence due to the inaccurate initial value given to the iteration process, we proposed a geometry-constraint-based GMM similarity calculation method, which is one contribution of this paper. Another contribution is we propose a dynamic scale factor making the cost function more adapted to different initial value. Experiments on simulated data are employed and the results indicate that our method is able to enlarge the valid range of initial value and accumulate small errors after sequential matchings.