Publications

Fitch, R., Alempijevic, A. & Peynot, T. 2016, 'Global reconfiguration of a team of networked mobile robots among obstacles', Springer, Berlin, Germany, pp. 639-656.
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This paper presents a full system demonstration of dynamic sensor-based reconfiguration of a networked robot team. Robots sense obstacles in their environment locally and dynamically adapt their global geometric configuration to conform to an abstract goal shape.We present a novel two-layer planning and control algorithm for team reconfiguration that is decentralized and assumes local (neighbour-to-neighbour) communication only. The approach is designed to be resource-efficient and we show experiments using a team of nine mobile robots with modest computation, communication, and sensing. The robots use acoustic beacons for localisation and can sense obstacles in their local neighbourhood using IR sensors. Our results demonstrate globally-specified reconfiguration from local information in a real robot network, and highlight limitations of standard mesh networks in implementing decentralised algorithms.

Huang, S. & Dissanayake, G. 2016, 'Robot Localization: An Introduction' in Wiley Encyclopedia of Electrical and Electronics Engineering, Wiley, USA.
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Robot localization is the process of determining where a mobile robot is located with respect to its environment. Localization is one of the most fundamental competencies required by an autonomous robot as the knowledge of the robot's own location is an essential precursor to making decisions about future actions. In a typical robot localization scenario, a map of the environment is available and the robot is equipped with sensors that observe the environment as well as monitor its own motion. The localization problem then becomes one of estimating the robot position and orientation within the map using information gathered from these sensors. Robot localization techniques need to be able to deal with noisy observations and generate not only an estimate of the robot location but also a measure of the uncertainty of the location estimate. This article provides an introduction to estimation of theoretic solutions to the robot localization problem. It begins by discussing the mathematical models used to describe the robot motion and observations from the sensors. Two of the most common probabilistic techniques, the extended Kalman filter and the particle filter, that can be used to combine information from sensors to compute an estimate of the robot location are then discussed in detail and illustrated by simple examples. A brief summary of the large body of literature on robot localization is presented next. Appendices that present the essential mathematical background and alternative techniques are provided. The MATLAB code of the localization algorithms is also available.

Au, W., Chung, H. & Chen, C. 2016, 'Path planning and assembly mode-changes of 6-DOF Stewart-Gough-type parallel manipulators', Mechanism and Machine Theory, vol. 106, pp. 30-49.
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© 2016 International Federation for the Promotion of Mechanism and Machine Science The Stewart-Gough platform (SGP) is a six degree-of-freedom (DOF) parallel manipulator whose reachable workspace is complex due to its closed-loop configuration and six DOF outputs. As such, methods of path planning that involve storing the entire reachable workspace in memory at high resolutions are not feasible due to this six-dimensional workspace. In addition, complete path planning algorithms struggle in higher dimensional applications without significant customisations. As a result, many workspace analysis algorithms and path planning schemes use iterative techniques, particularly when tracking the manipulator's many direct kinematic solutions. The aim of this paper is to present the viability of singularity-free path planning in the Stewart-Gough platform's 6-dimensional workspace on modern-day computing systems by demonstrating its assembly mode-changing capability. The entire workspace volume is found using flood-fill algorithms with smooth and singularity-free trajectories generated within this known workspace. Workspace volume analysis was also performed with results comparable to other works.

Ball, D., Upcroft, B., Wyeth, G., Corke, P., English, A., Ross, P., Patten, T., Fitch, R., Sukkarieh, S. & Bate, A. 2016, 'Vision-based Obstacle Detection and Navigation for an Agricultural Robot', Journal of Field Robotics, vol. 33, no. 8, pp. 1107-1130.
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This paper describes a vision-based obstacle detection and navigation system for use as part of a robotic solution for the sustainable intensification of broad-acre agriculture. To be cost-effective, the robotics solution must be competitive with current human-driven farm machinery. Significant costs are in high-end localization and obstacle detection sensors. Our system demonstrates a combination of an inexpensive global positioning system and inertial navigation system with vision for localization and a single stereo vision system for obstacle detection. The paper describes the design of the robot, including detailed descriptions of three key parts of the system: novelty-based obstacle detection, visually-aided guidance, and a navigation system that generates collision-free kinematically feasible paths. The robot has seen extensive testing over numerous weeks of field trials during the day and night. The results in this paper pertain to one particular 3 h nighttime experiment in which the robot performed a coverage task and avoided obstacles. Additional results during the day demonstrate that the robot is able to continue operating during 5 min GPS outages by visually following crop rows.

Cliff, O.M., Prokopenko, M. & Fitch, R.C. 2016, 'An Information Criterion for Inferring Coupling of Distributed Dynamical Systems', Frontiers in Robotics and AI, vol. 3, pp. 1-9.
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The behavior of many real-world phenomena can be modeled by non-linear dynamical systems whereby a latent system state is observed through a filter. We are interested in interacting subsystems of this form, which we model by a set of coupled maps as a synchronous update graph dynamical system. Specifically, we study the structure learning problem for spatially distributed dynamical systems coupled via a directed acyclic graph. Unlike established structure learning procedures that find locally maximum posterior probabilities of a network structure containing latent variables, our work exploits the properties of dynamical systems to compute globally optimal approximations of these distributions. We arrive at this result by the use of time delay embedding theorems. Taking an information-theoretic perspective, we show that the log-likelihood has an intuitive interpretation in terms of information transfer.

Dantanarayana, L., Dissanayake, G. & Ranasinge, R. 2016, 'C-LOG: A Chamfer distance based algorithm for localisation in occupancy grid-maps', CAAI Transactions on Intelligence Technology, vol. 1, no. 3, pp. 272-284.
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Huang, S. & Dissanayake, G. 2016, 'A critique of current developments in simultaneous localization and mapping', International Journal of Advanced Robotic Systems, vol. 13, no. 5, pp. 1-13.
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© SAGE Publications Ltd, unless otherwise noted. Manuscript content on this site is licensed under Creative Commons Licenses. The number of research publications dealing with the simultaneous localization and mapping problem has grown significantly over the past 15 years. Many fundamental and practical aspects of simultaneous localization and mapping have been addressed, and some efficient algorithms and practical solutions have been demonstrated. The aim of this paper is to provide a critical review of current theoretical understanding of the fundamental properties of the SLAM problem, such as observability, convergence, achievable accuracy and consistency. Recent research outcomes associated with these topics are briefly discussed together with potential future research directions.

Khosoussi, K., Huang, S. & Dissanayake, G. 2016, 'A sparse separable SLAM back-end', IEEE Transactions on Robotics, vol. 32, no. 6, pp. 1536-1549.
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© 2004-2012 IEEE. We propose a scalable algorithm to take advantage of the separable structure of simultaneous localization and mapping (SLAM). Separability is an overlooked structure of SLAM that distinguishes it from a generic nonlinear least-squares problem. The standard relative-pose and relative-position measurement models in SLAM are affine with respect to robot and features' positions. Therefore, given an estimate for robot orientation, the conditionally optimal estimate for the rest of the state variables can be easily computed by solving a sparse linear least-squares problem. We propose an algorithm to exploit this intrinsic property of SLAM by stripping the problem down to its nonlinear core, while maintaining its natural sparsity. Our algorithm can be used in conjunction with any Newton-based solver and is applicable to 2-D/3-D pose-graph and feature-based SLAM. Our results suggest that iteratively solving the nonlinear core of SLAM leads to a fast and reliable convergence as compared to the state-of-the-art sparse back-ends.

Khushaba, R.N., Al-Timemy, A., Kodagoda, S. & Nazarpour, K. 2016, 'Combined influence of forearm orientation and muscular contraction on EMG pattern recognition', EXPERT SYSTEMS WITH APPLICATIONS, vol. 61, pp. 154-161.
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Kodagoda, S., Sehestedt, S.A. & Dissanayake, G. 2016, 'Socially aware path planning for mobile robots', Robotica, vol. 34, no. 3, pp. 513-526.
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Humanrobot interaction is an emerging area of research where a robot may need to be working in human-populated environments. Human trajectories are generally not random and can belong to gross patterns. Knowledge about these patterns can be learned through observation. In this paper, we address the problem of a robot's social awareness by learning human motion patterns and integrating them in path planning. The gross motion patterns are learned using a novel Sampled Hidden Markov Model, which allows the integration of partial observations in dynamic model building. This model is used in the modified A* path planning algorithm to achieve socially aware trajectories. Novelty of the proposed method is that it can be used on a mobile robot for simultaneous online learning and path planning. The experiments carried out in an office environment show that the paths can be planned seamlessly, avoiding personal spaces of occupants.

Kodikara, J., Valls Miro, J. & Melchers, R. 2016, 'Failure Prediction of Critical Cast Iron Pipes', Advances in Water Research, vol. 26, no. 3, pp. 6-11.
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In 2011, a consortium of Australian water utilities led by Sydney Water (SW) joined forces with WRF and UK Water Industry Research (UKWIR) to initiate a five-year research program, Advanced Condition Assessment and Pipe Failure Prediction Project (ACAPFP).

Lu, W., Zhu, P. & Ferrari, S. 2016, 'A Hybrid-Adaptive Dynamic Programming Approach for the Model-Free Control of Nonlinear Switched Systems', IEEE TRANSACTIONS ON AUTOMATIC CONTROL, vol. 61, no. 10, pp. 3203-3208.
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Nguyen, J.L., Lawrance, N.R.J., Fitch, R. & Sukkarieh, S. 2016, 'Real-time path planning for long-term information gathering with an aerial glider', AUTONOMOUS ROBOTS, vol. 40, no. 6, pp. 1017-1039.
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Nguyen, L.V., Kodagoda, S. & Ranasinghe, R. 2016, 'Spatial Sensor Selection via Gaussian Markov Random Fields', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 46, no. 9, pp. 1226-1239.
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© 2013 IEEE. This paper addresses the problem of selecting the most informative sensor locations out of all possible sensing positions in predicting spatial phenomena by using a wireless sensor network. The spatial field is modeled by Gaussian Markov random fields (GMRFs), where sparsity of the precision matrix enables the network to benefit from computation. A new spatial sensor selection criterion is proposed based on mutual information (MI) between random variables at selected locations and those at unselected locations and interested but unlikely sensor placed positions, which enhances resulting prediction. The GMRF-based optimality criterion is then proven to be computationally and efficiently resolved, especially in a large-scale sensor network, by a polynomial time approximation algorithm. More importantly, with demonstrations of monotonicity and submodularity properties of the MI set function in the proposed selection criterion, our near-optimal solution is also guaranteed by at least within ${(1-1/e)}$ of the optimal performance. The effectiveness of the proposed approach is compared and illustrated using two real-life large data sets with promising results.

Nguyen, L.V., Kodagoda, S., Ranasinghe, R. & Dissanayake, G. 2016, 'Information-Driven Adaptive Sampling Strategy for Mobile Robotic Wireless Sensor Network', IEEE Transactions on Control Systems Technology, vol. 24, no. 1, pp. 372-379.
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This brief addresses the issue of monitoring physical spatial phenomena of interest using information collected by a resource-constrained network of mobile, wireless, and noisy sensors that can take discrete measurements as they navigate through the environment. We first propose an efficient novel optimality criterion for designing a sampling strategy to find the most informative locations in taking future observations to minimize the uncertainty at all unobserved locations of interest. This solution is proven to be within bounds. The computational complexity of this proposition is shown to be practically feasible. We then prove that under a certain condition of monotonicity property, the approximate entropy at resulting locations obtained by our proposed algorithm is within 1-(1/e) of the optimum, which is then utilized as a stopping criterion for the sampling algorithm. The criterion enables the prediction results to be within user-defined accuracies by controlling the number of mobile sensors. The effectiveness of the proposed method is illustrated using a prepublished data set.

Norouzi, M., Valls Miro, J. & Dissanayake, G. 2016, 'Probabilistic stable motion planning with stability uncertainty for articulated vehicles on challenging terrains', Autonomous Robots, vol. 40, no. 2, pp. 361-381.
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© 2015, Springer Science+Business Media New York. A probabilistic stable motion planning strategy applicable to reconfigurable robots is presented in this paper. The methodology derives a novel statistical stability criterion from the cumulative distribution of a tip-over metric. The measure is dynamically updated with imprecise terrain information, localization and robot kinematics to plan safety-constrained paths which simultaneously allow the widest possible visibility of the surroundings by simultaneously assuming highest feasible vantage robot configurations. The proposed probabilistic stability metric allows more conservative poses through areas with higher levels of uncertainty, while avoiding unnecessary caution in poses assumed at well-known terrain sections. The implementation with the well known grid based A* algorithm and also a sampling based RRT planner are presented. The validity of the proposed approach is evaluated with a multi-tracked robot fitted with a manipulator arm and a range camera using two challenging elevation terrains data sets: one obtained whilst operating the robot in a mock-up urban search and rescue arena, and the other from a publicly available dataset of a quasi-outdoor rover testing facility.

Patten, T., Zillich, M., Fitch, R., Vincze, M. & Sukkarieh, S. 2016, 'Viewpoint Evaluation for Online 3-D Active Object Classification', IEEE Robotics and Automation Letters, vol. 1, no. 1, pp. 73-81.
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Ryu, K., Dantanarayana, L., Furukawa, T. & Dissanayake, G. 2016, 'Grid-based Scan-to-Map Matching for Accurate 2D Map Building', Advanced Robotics, vol. 30, no. 7, pp. 431-448.
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This paper presents a grid-based scan-to-map matching technique for accurate 2D map building. At every acquisition of a new scan, the proposed technique matches the new scan to the previous scan similarly to the conventional techniques, but further corrects the error by matching the new scan to the globally defined map. In order to achieve best scan-to-map matching at each acquisition, the map is represented as a grid map with multiple normal distributions (NDs) in each cell, which is one contribution of this paper. Additionally, the new scan is also represented by NDs, developing a novel ND-to-ND matching technique. This ND-to-ND matching technique has significant potential in the enhancement of the global matching as well as the computational efficiency. Experimental results first show that the proposed technique accumulates very small errors after consecutive matchings and identifies that the scans are matched better to the map with the multi-ND representation than one ND representation. The proposed t...

Sun, Y., Zhao, L., Zhou, G. & Yan, L. 2016, 'Absolute Orientation Based on Distance Kernel Functions', Remote Sensing, vol. 8, no. 3, pp. 1-18.
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The classical absolute orientation method is capable of transforming tie points (TPs) from a local coordinate system to a global (geodetic) coordinate system. The method is based only on a unique set of similarity transformation parameters estimated by minimizing the total difference between all ground control points (GCPs) and the fitted points. Nevertheless, it often yields a transformation with poor accuracy, especially in large-scale study cases. To address this problem, this study proposes a novel absolute orientation method based on distance kernel functions, in which various sets of similarity transformation parameters instead of only one set are calculated. When estimating the similarity transformation parameters for TPs using the iterative solution of a non-linear least squares problem, we assigned larger weighting matrices for the GCPs for which the distances from the point are short. The weighting matrices can be evaluated using the distance kernel function as a function of the distances between the GCPs and the TPs. Furthermore, we used the exponential function and the Gaussian function to describe distance kernel functions in this study. To validate and verify the proposed method, six synthetic and two real datasets were tested. The accuracy was significantly improved by the proposed method when compared to the classical method, although a higher computational complexity is experienced

Takami, K., Furukawa, T., Kumon, M., Kimoto, D. & Dissanayake, G. 2016, 'Estimation of a nonvisible field-of-view mobile target incorporating optical and acoustic sensors', Autonomous Robots, vol. 40, no. 2, pp. 343-359.
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© 2015, Springer Science+Business Media New York. This paper presents a nonvisible field-of-view (NFOV) target estimation approach that incorporates optical and acoustic sensors. An optical sensor can accurately localize a target in its field-of-view whereas the acoustic sensor could estimate the target location over a much larger space, but only with limited accuracy. A recursive Bayesian estimation framework where observations of the optical and acoustic sensors are probabilistically treated and fused is proposed in this paper. A technique to construct the observation likelihood when two microphones are used as the acoustic sensor is also described. The proposed technique derives and stores the interaural level difference of observations from the two microphones for different target positions in advance and constructs the likelihood through correlation. A parametric study of the proposed acoustic sensing technique in a controlled test environment, and experiments with an NFOV target in an actual indoor environment are presented to demonstrate the capability of the proposed technique.

To, W., Paul, G. & Liu, D. 2016, 'An approach for identifying classifiable regions of an image captured by autonomous robots in structural environments', Robotics and Computer Integrated Manufacturing, vol. 37, pp. 90-102.
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When an autonomous robot is deployed in a structural environment to visually inspect surfaces, the capture conditions of images (e.g. camera's viewing distance and angle to surfaces) may vary due to un-ideal robot poses selected to position the camera in a collision-free manner. Given that surface inspection is conducted by using a classifier trained with surface samples captured with limited changes to the viewing distance and angle, the inspection performance can be affected if the capture conditions are changed. This paper presents an approach to calculate a value that represents the likelihood of a pixel being classifiable by a classifier trained with a limited dataset. The likelihood value is calculated for each pixel in an image to form a likelihood map that can be used to identify classifiable regions of the image. The information necessary for calculating the likelihood values is obtained by collecting additional depth data that maps to each pixel in an image (collectively referred to as a RGB-D image). Experiments to test the approach are conducted in a laboratory environment using a RGB-D sensor package mounted onto the end-effector of a robot manipulator. A naive Bayes classifier trained with texture features extracted from Gray Level Co-occurrence Matrices is used to demonstrate the effect of image capture conditions on surface classification accuracy. Experimental results show that the classifiable regions identified using a likelihood map are up to 99.0% accurate, and the identified region has up to 19.9% higher classification accuracy when compared against the overall accuracy of the same image.

Valls Miro, J. & Shi, L. 2016, 'Aiming for the Holy Grail: Pipe Condition Assessment Along Critical Mains from Limited Inspections', Utility Magazine, vol. 10, pp. 90-92.
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The Advanced Condition Assessment and Pipe Failure Prediction Project is coming up with a novel condition assessment research concept: exploiting data-driven research to improve large critical water mains condition prediction, over extended sections of pipeline, from limited condition assessment inspection data.

Vander Poorten, E., Zhao, L., Tran, P., Devreker, A., Gruijthuijsen, C., Portoles-Diez, S., Smoljkic, G., Strbac, V., Famaey, N., Reynaerts, D., Vander Sloten, J., Tibebu, A., Yu, B., Rauch, C., Bernard, F., Kassahun, Y., Metzen, J.H., Giannarou, S., Lee, S., Yang, G., Mazomenos, E., Chang, P., Stoyanov, D., Kvasnytsia, M., Van Deun, J., Verhoelst, E., Sette, M., Di Iasio, A., Leo, G., Hertner, F., Scherly, D., Chelini, L., Häni, N., Seatovic, D., Rosa, B., De Praetere, H. & Herijgers, P. 2016, 'Cognitive AutonomouS CAtheters Operating in Dynamic Environments', Journal of Medical Robotics Research, vol. 1, no. 3.
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Advances in miniaturized surgical instrumentation are key to less demanding and safer medical interventions. In cardiovascular procedures interventionalists turn towards catheter-based interventions, treating patients considered unfit for more invasive approaches. A positive outcome is not guaranteed. The risk for calcium dislodgement, tissue damage or even vessel rupture cannot be eliminated when instruments are maneuvered through fragile and diseased vessels. This paper reports on the progress made in terms of catheter design, vessel reconstruction, catheter shape modeling, surgical skill analysis, decision-making and control. These efforts are geared towards the development of the necessary technology to autonomously steer catheters through the vasculature, a target of the EU-funded project CASCADE (Cognitive AutonomouS CAtheters operating in Dynamic Environments). Whereas autonomous placement of an aortic valve implant forms the ultimate and concrete goal, the technology of individual building blocks to reach such ambitious goal is expected to be much sooner impacting and assisting interventionalists in their daily clinical practice.

Wei, H., Lu, W., Zhu, P., Ferrari, S., Liu, M., Klein, R.H., Omidshafiei, S. & How, J.P. 2016, 'Information value in nonparametric Dirichlet-process Gaussian-process (DPGP) mixture models', AUTOMATICA, vol. 74, pp. 360-368.
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Yoo, C., Fitch, R. & Sukkarieh, S. 2016, 'Online task planning and control for fuel-constrained aerial robots in wind fields', INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH, vol. 35, no. 5, pp. 438-453.
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Zhao, L., Giannarou, S., Lee, S.-.L. & Yang, G.Z. 2016, 'SCEM+: Real-time Robust Simultaneous Catheter and Environment Modelling for Endo-vascular Navigation', IEEE Robotics and Automation Letters, vol. 1, no. 2, pp. 961-968.
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Endovascular procedures are characterised by significant challenges mainly due to the complexity in catheter control and navigation. Real-time recovery of the 3-D structure of the vasculature is necessary to visualise the interaction between the catheter and its surrounding environment to facilitate catheter manipulations. State-of-the-art intraoperative vessel reconstruction approaches are increasingly relying on nonionising imaging techniques such as optical coherence tomography (OCT) and intravascular ultrasound (IVUS). To enable accurate recovery of vessel structures and to deal with sensing errors and abrupt catheter motions, this letter presents a robust and real-time vessel reconstruction scheme for endovascular navigation based on IVUS and electromagnetic (EM) tracking. It is formulated as a nonlinear optimisation problem, which considers the uncertainty in both the IVUS contour and the EM pose, as well as vessel morphology provided by preoperative data. Detailed phantom validation is performed and the results demonstrate the potential clinical value of the technique.

Al-Muhsen, N., Wang, J. & Hong, G. 2016, 'Investigation to Combustion and Emission Characteristics of the Dual Ethanol Injection Spark Ignition Engine', 20th Australasian Fluid Mechanics Conference, Australasian Fluid Mechanics Conference, Australasian Fluid Mechanics Society, Perth, AU, pp. 1-4.
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Abstract Ethanol fuel, as a bioproduct with greater octane number, combustion speed and latent heat of vaporization, has become a common choice as an additive and/or an alternative option to gasoline fuel in the spark ignition engines. In order to fully utilize ethanol fuel properties to improve engine performance, a new injection strategy, ethanol port injection plus ethanol direct injection (EDI), has been in development. Work reported in this paper aimed to investigate, experimentally, the effect of ethanol fuel and dual ethanol injection strategy on engine performance, combustion and emissions characteristics at two engine loads and optimized spark timing. The results of both engine loads, light and medium, demonstrated that the indicated mean effective pressure (IMEP) was significantly improved over all dual ethanol injection strategy compared to GPI. The maximum improvement was 3.3485% and 4.357% at light and medium engine loads respectively. The improvement was mainly due to the reduced combustion duration (θ10-90%) which was reduced by 8.15CAD at light load and 4.28CAD at medium load compared to GPI. However, at higher EDI percentages, the over cooling effect and poor mixture quality adversely affected the combustion quality. The indicated specific nitric oxide emission was considerably reduced, at 100% of EDI, by up to 55.1% and 58.46% at light and medium loads respectively. Nevertheless, because of poor mixture quality and high wall wetting, the indicated specific hydrocarbon and the indicated specific carbon monoxide were raised with the increase of EDI percentage. Regarding the effect of spark timing, the dual ethanol injection strategy improved the IMEP significantly at the maximum IMEP spark timing.

Bai, F., Huang, S., Vidal Calleja, T.A. & Zhang, Q. 2016, 'Incremental SQP Method for Constrained Optimization Formulation in SLAM', Proceedings of the 14th International Conference on Control, Automation, Robotics and Vision (ICARCV), International Conference on Control, Automation, Robotics and Vision, IEEE, Phuket, Thailand.
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The simultaneous localization and mapping (SLAM) problem has been a research focus for many years and have reached a mature state. However, more robust solutions to the SLAM problem are still required, especially in large noise level scenarios. Because of the strong non-linearity of the SLAM problem, it is vital to start from a good initial value to avoid being trapped in local minima. In this paper, we propose a new SLAM formulation transforming the unconstrained Least Squares formulation into a constrained optimization problem. Algorithms based on this new formulation can naturally start from good initial value. Different from other constrained optimization problem, this new formulation can be efficiently solved with Sequential Quadratic Programming (SQP) methods. Based on SQP, we propose an incremental SQP algorithm to solve SLAM, which shows great advantage over Gauss Newton (g2o implementation) when working in large noise level scenarios. Experimental results show the validity of the proposed approach.

Banihashemi Namini, S.S., Ding, G.K.C. & Wang, J. 2016, 'Identification of BIM-Compatible Variables for Energy Optimization of Residential Buildings: A Delphi Study', AUBEA 2016 The 40th Australasian Universities Building Education Association Conference, Australian Universities Building Education Association Annual Conference, Central Queensland University, Cairns, Australia, pp. 281-291.
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It is believed that drawing an applicable, relevant and coherent batch of variables is a fundamental tenet in the success of having an integrated BIM-based energy optimisation but in order to achieve a high level of usefulness, these variables need to be refined and prioritised. Thus, this paper is to investigate BIM compatible variables which are of top priorities for energy optimisation of residential buildings in the design stage. A sequential exploratory research was conducted to find out the most relevant and significant variables that have a high impact on the energy consumption of residential buildings. A pool including more than 30 variables was established and refined through running Delphi approach with energy and BIM experts to reach the final list of prioritized variables. Conducting a three-round Delphi enabled authors to obtain more meticulous results via a consensus agreement among the respondents on the top 13 variables through lenses of BIM compatibility, applicability to optimization and design stage.

Best, G. & Fitch, R. 2016, 'Probabilistic Maximum Set Cover with Path Constraints for Informative Path Planning', Website Proceedings of Australasian Conference on Robotics and Automation 2016, Australasian Conference on Robotics and Automation, ARAA, Brisbane, Australia, pp. 1-10.
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We pose a new formulation for informative path planning problems as a generalisation of the well-known maximum set cover problem. This new formulation adds path constraints and travel costs, as well as a probabilistic observation model, to the maximum set cover problem. Our motivation is informative path planning applications where the observation model can be naturally encoded as overlapping subsets of a set of discrete elements. These elements may include features, landmarks, regions, targets or more abstract quantities, that the robot aims to observe while moving through the environment with a given travel budget. This formulation allows directly modelling the dependencies of observations from different viewpoints. We show this problem is NP-hard and propose a branch and bound tree search algorithm. Simulated experiments empirically evaluate the bounding heuristics, several tree expansion policies and convergence rate towards optimal. The tree pruning allows finding optimal or bounded-approximate solutions in a reasonable amount of time, and therefore indicates our work is suitable for practical applications.

Best, G., Cliff, O., Patten, T., Mettu, R. & Fitch, R. 2016, 'Decentralised Monte Carlo Tree Search for Active Perception', Workshop on the Algorithmic Foundations of Robotics (WAFR), Workshop on the Algorithmic Foundations of Robotics (WAFR), International Workshop on the Algorithmic Foundations of Robotics, San Francisco, USA.
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We propose a decentralised variant of Monte Carlo tree search (MCTS) that is suitable for a variety of tasks in multi-robot active perception. Our algorithm allows each robot to optimise its own individual action space by maintaining a probability distribution over plans in the joint-action space. Robots periodically communicate a compressed form of these search trees, which are used to update the locally-stored joint distributions using an optimisation approach inspired by variational methods. Our method admits any objective function defined over robot actions, assumes intermittent communication, and is anytime. We extend the analysis of the standard MCTS for our algorithm and characterise asymptotic convergence under reasonable assumptions. We evaluate the practical performance of our method for generalised team orienteering and active object recognition using real data, and show that it compares favourably to centralised MCTS even with severely degraded communication. These examples support the relevance of our algorithm for real-world active perception with multi-robot systems.

Best, G., Faigl, J. & Fitch, R. 2016, 'Multi-Robot Path Planning for Budgeted Active Perception with Self-Organising Maps', Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Daejeon, Korea, pp. 3164-3171.
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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 polynomial-bounded runtime independent of 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 selfocclusions, and the rewards are measured as discriminability in the ensemble of shape functions feature space. 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 budgeted active perception tasks with continuous sets of candidate viewpoints and long planning horizons.

Bykerk, L., Liu, D. & Waldron, K. 2016, 'A Topology Optimisation Based Design of a Compliant Gripper for Grasping Objects with Irregular Shapes', 2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), IEEE/ASME International Conference on Advanced Intelligent Mechatronics, IEEE, Banff, Canada.
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Complex steel structures such as power transmis- sion towers require regular inspection and maintenance during their lifetime. This work is currently completed by teams of human workers who climb the live structures. The exposure of these workers to the risks of climbing and completing work on towers provides motivation for developing a robotic substitute. There are many complex elements of climbing power transmission towers, such as the variation in beam shapes, sizes and orientations. To the best of our knowledge, there is no existing robotic grasping solution that can be directly used in this complex environment. This paper presents a topology optimisation based design of a compliant gripper for grasping objects with irregular shapes such as the beam members found in power transmission towers. The structure of the gripper is obtained through the use of a modified topology optimisation model where stiffness constraints are implemented in the optimisation to increase the strength of the gripper in desired areas. The stiffness constrained topology optimisation produces a novel gripper design which is validated through both simulations and physical testing of the manufactured gripper on a variety of physical objects.

Cao, D., Hong, G. & Wang, J. 2016, 'Preliminary investigation to the feasibility of chemical heat storage for saving the exhaust gas energy in a spark ignition engine', Website proceedings of the 20th Australasian Fluid Mechanics Conference, Australasian Fluid Mechanics Conference, The 20th Australasian Fluid Mechanics Conference, Perth, Australia, pp. 1-4.
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Heat storage has become more important because it utilizes the wasted energy to improve the overall efficiency of energy systems. This study was aimed to develop a chemical heat storage system using magnesium hydroxide (Mg(OH)2) and its endothermic and exothermic reactions to recover the thermal energy of the exhaust gas in internal combustion engines. It was proposed that the reactor receives the thermal energy of exhaust gas in the dehydration of Mg(OH)2 to become MgO and H2O, and releases the stored energy in the hydration of MgO. To increase the thermal conductivity of pure Mg(OH)2 for enhancing the reactor’s performance, the working material used, EM8 block, is the mixture of Mg(OH)2 and expanded graphite at a ratio of 8:1. Experiments were conducted on a 6-cylinder spark ignition engine (Toyota Aurion 2GR-FE 3.5L) at stoichiometric air/fuel ratios to estimate the amount of energy loss in the exhaust gas. Experimental data of exhaust gas temperature and volume ratios of exhaust gas constitutions were used to calculate the energy rates of each of the exhaust gas constituents and to estimate the reactor efficiency in the dehydration process. Results of the preliminary investigation show that the proposed chemical heat storage system may be feasible to recover approximately 5.8 % of the heat loss in the exhaust gas

Cui, Y., Poon, J.T., Valls Miro, J., Matsubara, T. & Sugimoto, K. 2016, 'Optimal Control Approach for Active Local Driving Assistance in Mobility Aids', 34th annual conference of the Robotics Society of Japan (RSJ), Japan.
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Cui, Y.D., Poon, J.T., Matsubara, T., Valls Miro, J., Sugimoto, K. & Yamazaki, K. 2016, 'Environment-adaptive Interaction Primitives for Human-Robot Motor Skill Learning', 16th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2016, IEEE-RAS International Conference on Humanoid Robots, IEEE, Cancun, Mexico, pp. 711-717.
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In complex environments where robots are expected to co-operate with human partners, it is vital for the robot to consider properties of their collaborative activity in addition to the behavior of its partner. In this paper, we propose to learn such complex interactive skills by observing the demonstrations of a human-robot team with additional external attributes. We propose Environment-adaptive Interaction Primitives (EalPs) as an extension of Interaction Primitives. In cooperation tasks between human and robot with different environmental conditions, EalPs not only improve the predicted motor skills of robot within a brief observed human motion, but also obtain the generalization ability to adapt to new environmental conditions by learning the relationships between each condition and the corresponding motor skills from training samples. Our method is validated in the collaborative task of covering objects by plastic bag with a humanoid Baxter robot. To achieve the task successfully, the robot needs to coordinate itself to its partner while also considering information about the object to be covered.

Dissanayake, G. 2015, 'INFRASTRUCTURE ROBOTICS: OPPORTUNITIES AND CHALLENGES', ASSISTIVE ROBOTICS, 18th Climbing and Walking Robots Conference (CLAWAR), WORLD SCIENTIFIC PUBL CO PTE LTD, Hangzhou, PEOPLES R CHINA, pp. 3-3.

Emery, B.M., Ghaffari Jadidi, M., Nakamura, K. & Valls Miro, J. 2016, 'An Audio-visual Solution to Sound Source Localization and Tracking with Applications to HRI', Proceedings of the Australasian Conference on Robotics & Automation (ACRA), Australasian Conference on Robotics and Automation, ARAA, Brisbane, pp. 1-10.
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Robot audition is an emerging and growing branch in the robotic community and is necessary for a natural Human-Robot Interaction (HRI). In this paper, we propose a framework that integrates advances from Simultaneous Localization And Mapping (SLAM), bearing-only target tracking, and robot audition techniques into a unifed system for sound source identification, localization, and tracking. In indoors, acoustic observations are often highly noisy and corrupted due to reverberations, the robot ego-motion and background noise, and possible discontinuous nature of them. Therefore, in everyday interaction scenarios, the system requires accommodating for outliers, robust data association, and appropriate management of the landmarks, i.e. sound sources. We solve the robot self-localization and environment representation problems using an RGB-D SLAM algorithm, and sound source localization and tracking using recursive Bayesian estimation in the form of the extended Kalman Filter with unknown data associations and an unknown number of landmarks. The experimental results show that the proposed system performs well in the medium-sized cluttered indoor environment.

Falque, R., Vidal Calleja, T.A., Dissanayake, G. & Valls Miro, J. 2016, 'From the skin-depth equation to the inverse RFEC sensor model', 2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV), International Conference on Control, Automation, Robotics and Vision, IEEE, Phuket, Thailand.
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Hassan, M., Liu, D.L. & Paul, G.P. 2016, 'Modeling and Stochastic Optimization of Complete Coverage under Uncertainties in Multi-Robot Base Placements', Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on, IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE (Institute of Electrical and Electronics Engineers), Daejeon, Korea, pp. 2978-2984.
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Uncertainties in base placements of mobile, autonomous industrial robots can cause incomplete coverage in tasks such as grit-blasting and spray painting. Sensing and localization errors can cause such uncertainties in robot base placements. This paper addresses the problem of collaborative complete coverage under uncertainties through appropriate base placements of multiple mobile and autonomous industrial robots while aiming to optimize the performance of the robot team. A mathematical model for complete coverage under uncertainties is proposed and then solved using a stochastic multi-objective optimization algorithm. The approach aims to concurrently find an optimal number and sequence of base placements for each robot such that the robot team's objectives are optimized whilst uncertainties are accounted for. Several case studies based on a real-world application using a real-world object and a complex simulated object are provided to demonstrate the effectiveness of the approach for different conditions and scenarios, e.g. various levels of uncertainties, different numbers of robots, and robots with different capabilities.

Hassan, M., Liu, Paul & Huang 2015, 'An Approach to Base Placement for Effective Collaboration of Multiple Autonomous Industrial Robots', International Conference on Robotics and Automation (ICRA), Institute of Electrical and Electronics Engineers (IEEE), Washington State Convention Center in Seattle, Washington, USA, pp. 3286-3291.
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There are many benefits for the deployment of multiple autonomous industrial robots to carry out a task, particularly if the robots act in a highly collaborative manner. Collaboration can be possible when each robot is able to autonomously explore the environment, localize itself, create a map of the environment and communicate with other robots. This paper presents an approach to the modeling of the collaboration problem of multiple robots determining optimal base positions and orientations in an environment by considering the team objectives and the information shared amongst the robots. It is assumed that the robots can communicate so as to share information on the environment, their operation status and their capabilities. The approach has been applied to a team of robots that are required to perform complete surface coverage tasks such as grit-blasting and spray painting in unstructured environments. Case studies of such applications are presented to demonstrate the effectiveness of the approach.

Hefferan, B., Cliff, O. & Fitch, R. 2016, 'Adversarial Patrolling with Reactive Point Processes', Proceedings of the Australasian Conference on Robotics & Automation (ACRA), Australasian Conference on Robotics and Automation, ARAA, Brisbane, Australia.
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Huang, S., Khosoussi, K., Dissanayake, G. & Sukhatme, G. 2016, 'Designing Sparse Reliable Pose-Graph SLAM: A Graph-Theoretic Approach', International Workshop on the Algorithmic Foundations of Robotics.
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Kassir, A., Fitch, R. & Sukkarieh, S. 2016, 'Communication-Efficient Motion Coordination and Data Fusion in Information Gathering Teams', Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Daejeon, Korea, pp. 5258-5265.
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Multi-robot information gathering teams typically require communication for data fusion and cooperative decision making. However, when communication takes place over wireless networks, stringent bandwidth limits apply. These limits raise the need for efficient utilisation of available communication resources in a manner that balances information gathering utility with communication costs or limits. In our previous work, we introduced the dynamic information flow (DIF) problem as a general formulation of this trade-off. We introduced two variants of the problem addressing the issue of communication efficiency for data fusion only. In this paper, we extend one of the variants to address communication efficiency for both data fusion and cooperative decision making in a synergistic manner. We present a solution to this new variant that integrates a multi-cast routing algorithm with information structure optimisation. This solution allows teams that involve high-data-rate sensors and tight coordination to respect bandwidth limits. Through several simulations we verify that our solution significantly reduces bandwidth usage of such teams while maintaining information gathering performance.

Khosoussi, K., Huang, S. & Dissanayake, G. 2016, 'Tree-Connectivity: Evaluating the Graphical Structure of SLAM', Proceedings - IEEE International Conference on Robotics and Automation, IEEE International Conference on Robotics and Automation, IEEE, Stockholm, Sweden, pp. 1316-1322.
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Kodagoda, S., Abeywardena, D., Barnes, B., Dissanayake, G. & Huang, S. 2016, 'Fast, on-board, model-aided visual-inertial odometry system for quadrotor micro aerial vehicles', Proceedings - IEEE International Conference on Robotics and Automation, IEEE International Conference on Robotics and Automation, IEEE, Stockholm, Sweden.
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The main contribution of this paper is a high frequency, low-complexity, on-board visual-inertial odometry system for quadrotor micro air vehicles. The system consists of an extended Kalman filter (EKF) based state estimation algorithm that fuses information from a low cost MEMS inertial measurement unit acquired at 200Hz and VGA resolution images from a monocular camera at 50Hz. The dynamic model describing the quadrotor motion is employed in the estimation algorithm as a third source of information. Visual information is incorporated into the EKF by enforcing the epipolar constraint on features tracked between image pairs, avoiding the need to explicitly estimate the location of the tracked environmental features. Combined use of the dynamic model and epipolar constraints makes it possible to obtain drift free velocity and attitude estimates in the presence of both accelerometer and gyroscope biases. A strategy to deal with the unobservability that arises when the quadrotor is in hover is also provided. Experimental data from a real-time implementation of the system on a 50 gram embedded computer are presented in addition to the simulations to demonstrate the efficacy of the proposed system.

Liao, Y., Kodagoda, S., Wang, Y., Shi, L. & Liu, Y. 2016, 'Understand scene categories by objects: A semantic regularized scene classifier using Convolutional Neural Networks', 2016 IEEE International Conference on Robotics and Automation (ICRA), IEEE International Conference on Robotics and Automation, IEEE, Stockholm, Sweden, pp. 2318-2325.
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Scene classification is a fundamental perception task for environmental understanding in today's robotics. In this paper, we have attempted to exploit the use of popular machine learning technique of deep learning to enhance scene understanding, particularly in robotics applications. As scene images have larger diversity than the iconic object images, it is more challenging for deep learning methods to automatically learn features from scene images with less samples. Inspired by human scene understanding based on object knowledge, we address the problem of scene classification by encouraging deep neural networks to incorporate object-level information. This is implemented with a regularization of semantic segmentation. With only 5 thousand training images, as opposed to 2.5 million images, we show the proposed deep architecture achieves superior scene classification results to the state-of-the-art on a publicly available SUN RGB-D dataset. In addition, performance of semantic segmentation, the regularizer, also reaches a new record with refinement derived from predicted scene labels. Finally, we apply our model trained on SUN RGB-D dataset to a set of images captured in our university using a mobile robot, demonstrating the generalization ability of the proposed algorithm.

Ma, H., Shi, L., Kodagoda, S. & Xiong, R. 2016, 'A Semantic Labeling Strategy to Reject Unknown Objects in Large Scale 3D Point Clouds', Proceedings of the 35th Chinese Control Conference, Chinese Control Conference, IEEE, Chengdu, Sichuan, China, pp. 7070-7075.
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In recent years, there has been a growing interest in the research of semantic labeling of indoor scenes represented by 3D point clouds. A fundamental problem that has largely been oversighted in the current research is the way of dealing with the unknown class which collectively includes all the objects that are of no interest to the application developer. In the training stage, these objects are either completely removed or labeled as unknown, resulting in a trained model which is not fully and fairly exposed to the actual sample space. In the test stage, the unknown objects are naturally present and provided to the classifier, causing a significant drop of the classification accuracy-usually 20%~30%. Simply improving the features or the classifier will not address the root cause problem. In this paper, we propose a labeling framework combining both Conditional Random Field (CRF) and PI-SVM to specifically solve the problem caused by the unknown class. First, we use a CRF to model the contextual relations in the 3D space, for which the parameters for both node potential and edge potential are learned from training data. Then, we make use of the rejection strategy of the PI-SVM, which estimates an unnormalized probability for each class. Finally, we reinforce the result of CRF with the belief provided by the PI-SVM, and the labeling result is based on the agreement of the two classifiers. The proposed method takes advantage of the global optimization of CRF and the advantage of unknown rejection of PI-SVM. Experimental results on publicly available data set show that this method has improved the classification accuracy by 10.7% given the accuracy drop of 19.23% caused by the unknown.

Nguyen, J.L., Lawrance, N.R.J., Fitch, R. & Sukkarieh, S. 2016, 'Informative soaring with drifting thermals', Proceedings - IEEE International Conference on Robotics and Automation, IEEE International Conference on Robotics and Automation, IEEE, Stockholm, Sweden, pp. 1522-1529.
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© 2016 IEEE.The informative soaring (IFS) problem involves a gliding unmanned aerial vehicle (UAV) exploiting energy from thermals to extend its information gathering capability. In this paper, we address the realistic situation of detecting new thermals drifting with the wind in the search environment. We consider complex target-search scenarios characterised by information clusters and propose a new set of algorithms designed to both explore for and exploit high-value thermals to maximise information gain. Our algorithms: 1) compute a thermal exploration map to detect useful thermals that eventually intercept clusters, 2) solve a boundary value problem for interthermal path segment (ITP) generation with moving thermals, 3) compute thermal time windows to gather information from clusters and form a cluster service schedule, and 4) use branch and bound (BnB) tree search for global planning, considering high-utility-rate ITPs to maximise information gain. Our solution is compared against a greedy method that neither considers the thermal exploration map nor cluster schedule and a full knowledge method that has access to all thermals. Numerical simulations show that on average, our solution outperforms the greedy method in one-third of 2400 Monte Carlo trials, and achieves similar performance to the full knowledge method when environmental conditions are favourable.

Nguyen, L. & Kodagoda, S. 2016, 'Soil Organic Matter Estimation in Precision Agriculture using Wireless Sensor Networks', IEEE International Conference on Control, Automation, Robotics and Vision, International Conference on Control, Automation, Robotics and Vision, IEEE, Thailand.
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Patten, T., Fitch, R. & Sukkarieh, S. 2014, 'Multi-Robot Coverage Planning with Resource Constraints for Horticulture Applications', Acta Horticulturae, International Horticultural Congress on Horticulture, International Society for Horticultural Science, Brisbane, Australia, pp. 655-662.
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A multi-robot system is a team of autonomous robots that work together to perform a given task. Multi-robot systems have great potential for use in horticulture applications. Robots have the potential to perform crop surveillance, efficiently apply fertiliser and chemical inputs, and perform weeding and harvesting. In all of these tasks, robots must visit many trees or plants over a large area in a time-sensitive manner. Multi-robot systems are appropriate because many robots can work efficiently in parallel. However, a fundamental challenge to be addressed is how to coordinate the motion of many robots while also respecting resource constraints such as limited energy storage, liquid payload, and harvested product storage. The algorithmic problem of multi-robot coverage planning with resource constraints is similar to the NP-hard vehicle routing problem, but the computational complexity of general resource-constrained coverage remains unknown. We show that one variant of this problem, coverage with fixed replenishment stations and zero queuing time, can be solved in polynomial time using area partitioning and graph search. We present algorithms and analysis for this variant, and demonstrate the behaviour of our algorithms in simulation experiments with up to 100 robots. The robots cover a large area organised as a collection of sub-areas with defined boundaries and row orientations. Robots plan to visit one of several possible replenishment stations in order to satisfy resource constraints. Each robot may replenish itself multiple times throughout its mission. This work is practically applicable to systems where refill time is short relative to working time

Paul, G., Liu, L. & Liu, D. 2016, 'A Novel Approach to Steel Rivet Detection in Poorly Illuminated Steel Structural Environments', Control, Automation, Robotics and Vision (ICARCV), 2016 14th International Conference on, International Conference on Control, Automation, Robotics and Vision, IEEE, Phuket, Thailand.
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It is becoming increasingly achievable for steel bridge structures, which are normally both inaccessible and hazardous for humans, to be inspected and maintained by autonomous robots. Steel bridges have been traditionally constructed by securing plate members together with rivets. However, rivets present a challenge for robots both in terms of cleaning and surface traversal. This paper presents a novel approach to RGBD image and point cloud analysis that enables rivets to be rapidly and robustly located using low cost, non-contact sensing devices that can be easily affixed to a robot. The approach performs classification based on: (a) high-intensity blobs in color images, (b) the non-linear perturbations in depth images, and (c) surface normal clusters in 3D point clouds. The predicted rivet locations from the three classifiers are combined using a probabilistic occupancy mapping technique. Experiments are conducted in several different lab and real-world steel bridge environments, where there is no external lighting infrastructure, and the sensors are attached to a mobile platform, i.e. a climbing inspection robot. The location of rivets within 2m of the robot can be robustly located within 10mm of their correct location. The state of voxels can be predicted with above 95% accuracy, in approximately 1 second per frame.

Paul, G., Mao, S., Liu, L. & Xiong, R. 2015, 'Mapping Repetitive Structural Tunnel Environments for a Biologically Inspired Climbing Robot', Assistive Robots: Proceedings of the 18th International Conference on CLAWAR 2015, International Conference on Climbing and Walking Robots, World Scientific, Hangzhou, China, pp. 325-333.
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This paper presents an approach to using noisy and incomplete depth-camera datasets to detect reliable surface features for use in map construction for a caterpillar-inspired climbing robot. The approach uses a combination of plane extraction, clustering and template matching techniques to infer from the restricted dataset a usable map. This approach has been tested in both laboratory and real-world steel bridge tunnel datasets generated by a climbing robot, with the results showing that the generated maps are accurate enough for use in localisation and step trajectory planning.

Quin, P.D., Paul, G., Alempijevic, A. & Liu, D. 2016, 'Exploring in 3D with a Climbing Robot: Selecting the Next Best Base Position on Arbitrarily-Oriented Surfaces', Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on, IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Daejeon, Korea, pp. 5770-5775.
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This paper presents an approach for selecting the next best base position for a climbing robot so as to observe the highest information gain about the environment. The robot is capable of adhering to and moving along and transitioning to surfaces with arbitrary orientations. This approach samples known surfaces, and takes into account the robot kinematics, to generate a graph of valid attachment points from which the robot can either move to other positions or make observations of the environment. The information value of nodes in this graph are estimated and a variant of A* is used to traverse the graph and discover the most worthwhile node that is reachable by the robot. This approach is demonstrated in simulation and shown to allow a 7 degree-of-freedom inchworm-inspired climbing robot to move to positions in the environment from which new information can be gathered about the environment.

Ranasinghe, R. & Kodagoda, S. 2016, 'Spatial Prediction in Mobile Robotic Wireless Sensor Networks withNetwork Constraints', IEEE, International Conference on Control, Automation, Robotics and Vision, IEEE, Phuket, Thailand.
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Reeks, C., Carmichael, M., Liu, D. & Waldron, K. 2016, 'Angled sensor configuration capable of measuring tri-axial forces for pHRI', Proceedings - IEEE International Conference on Robotics and Automation, IEEE International Conference on Robotics and Automation, IEEE, Stockholm, Sweden, pp. 3089-3094.
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This paper presents a new configuration for single axis tactile sensor arrays molded in rubber to enable tri-axial force measurement. The configuration requires the sensing axis of each sensor in the array to be rotated out of alignment with respect to external forces. This angled sensor array measures shear forces along axes in a way that is different to a planar sensor array. Three sensors using the angled configuration (22.5°, 45° and 67.5°) and a fourth sensor using the planar configuration (0°) have been fabricated for experimental comparison. Artificial neural networks were trained to interpret the external force applied along each axis (X, Y and Z) from raw pressure sensor values. The results show that the angled sensor configuration is capable of measuring tri-axial external forces with a root mean squared error of 1.79N, less error in comparison to the equivalent sensor utilizing the planar configuration (4.52N). The sensors are then implemented to control a robotic arm. Preliminary findings show angled sensor arrays to be a viable alternative to planar sensor arrays for shear force measurement; this has wide applications in physical Human Robot Interaction (pHRI).

Reid, W., Fitch, R., Goktogan, A.H. & Sukkarieh, S. 2016, 'Motion Planning for ReconfigurableMobile Robots Using Hierarchical FastMarching Trees', Website proceedings of the 12th Workshop on the Algorithmic Foundations of Robotics, Workshop on the Algorithmic Foundations of Robotics (WAFR), WAFR, San Francisco, USA, pp. 1-16.
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Reconfigurable mobile robots are versatile platforms that may safely traverse cluttered environments by morphing their physical geometry. However, planning paths for these robots is challenging due to their many degrees of freedom. We propose a novel hierarchical variant of the Fast Marching Tree (FMT*) algorithm. Our algorithm assumes a decomposition of the full state space into multiple sub-spaces, and begins by rapidly finding a set of paths through one such sub-space. This set of solutions is used to generate a biased sampling distribution, which is then explored to find a solution in the full state space. This technique provides a novel way to incorporate prior knowledge of sub-spaces to ef- ficiently bias search within the existing FMT* framework. Importantly, probabilistic completeness and asymptotic optimality are preserved. Experimental results are provided for a reconfigurable wheel-on-leg platform that benchmark the algorithm against state-of-the-art samplingbased planners. In minimizing an energy objective that combines the mechanical work required for platform locomotion with that required for reconfiguration, the planner produces intuitive behaviors where the robot dynamically adjusts its footprint, varies its height, and clambers over obstacles using legged locomotion. These results illustrate the generality of the planner in exploiting the platform’s mechanical ability to fluidly transition between various physical geometric configurations, and wheeled/legged locomotion modes.

Shi, L. & Valls Miro, J. 2016, 'Towards Optimized and Reconstructable Sampling Inspection of Pipe Integrity for Improved Efficiency of NDT', 2016 IWA World Water Congress, Brisbane, Queensland, Australia.
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Shi, L., Valls Miro, J., Rajalingam, J., Wood, R. & Vitanage, D. 2016, 'High Precision GPS Aided In-pipe Distance Calibration For Satellite Image-based Pipeline Mapping', OZWATER’16 Australia’s International Water Conference & Exhibition, OZWATER’16 Australia’s International Water Conference & Exhibition, Australian Water Association, Melbourne, pp. 1-8.
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Asset management and pipe condition assessment (CA) activities in the water industry usually require locating buried pipes accurately to minimise inspection and maintenance costs. A typical challenge in practice is locating an anomaly detected by an in-pipe inspection tool from aboveground in order to dig up a pipe for replacement. Accumulated in-pipe errors over longer distances in particular can easily lead to selecting the wrong pipe section for further investigation or exhumation. In fact, some in-pipe CA providers suggest utility personnel dig up a number of sections of pipe around the suggested location so as to ensure finding the target section. In this paper we propose a mechanism to accurately correlate a 3D pipeline profile built from GPS surveying results of aboveground pipeline features with in-pipe chainage distances, so as to establish an accurate link between above-ground GPS coordinates and inpipe distance measurements. This approach naturally characterises and corrects for some of the most prominent in-pipe chainage measurement errors that can lead to uncertainties about the reported location of a buried pipeline from above-ground. The detailed pipeline information can then be projected onto satellite imagery as an accurate easy-to-understand reference for efficient decision making.

Shi, L., Valls Miro, J., Zhang, T., Vidal Calleja, T., Sun, L. & Dissanayake, G. 2016, 'Constrained Sampling of 2.5D Probabilistic Maps for Augmented Inference', IEEE International Conference on Intelligent Robots and Systems, IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Daejeon, Korea.
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Song, J.W., Wang, J., Zhao, L., Huang, S. & Dissanayake, G. 2016, '3D Shape Recovery of Deformable Soft-tissue with Computed Tomography and Depth Scan', Website proceedings of the Australasian Conference on Robotics and Automation 2016, Australasian Conference on Robotics and Automation, ARAA, Queensland, Australia, pp. 1-9.
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Knowing the tissue environment accurately is very important in minimal invasive surgery (MIS). While, as the soft-tissues is deformable, reconstruction of the soft-tissues environment is challenging. This paper proposes a new framework for recovering the deformation of the soft-tissues by using a single depth sensor. This framework makes use of the morphology information of the soft-tissues from Xray computed tomography, and deforms it by the embedded deformation method. Here, the key is to build a distance field function of the scan from the depth sensor, which can be used to perform accurate model-to-scan deformation together with robust non-rigid shape registration in the same go. Simulations show that soft-tissue shape in the previous step can be ef- ficiently deformed to fit the partially observed scan in the current step by using the proposed method. And the results from the simulated sequential deformation of three different softtissues demonstrate the potential clinical value for MIS.

Su, D., Nakamura, K., Nakadai, K. & Valls Miro, J. 2016, 'Robust Sound Source Mapping using Three-layered Selective Audio Rays for Mobile Robots', 2016 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2016), IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Daejeon, Korea, pp. 2771-2777.
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This paper investigates sound source mapping in a real environment using a mobile robot. Our approach is based on audio ray tracing which integrates occupancy grids and sound source localization using a laser range finder and a microphone array. Previous audio ray tracing approaches rely on all observed rays and grids. As such observation errors caused by sound reflection, sound occlusion, wall occlusion, sounds at misdetected grids, etc. can significantly degrade the ability to locate sound sources in a map. A three-layered selective audio ray tracing mechanism is proposed in this work. The first layer conducts frame-based unreliable ray rejection (sensory rejection) considering sound reflection and wall occlusion. The second layer introduces triangulation and audio tracing to detect falsely detected sound sources, rejecting audio rays associated to these misdetected sounds sources (short-term rejection). A third layer is tasked with rejecting rays using the whole history (long-term rejection) to disambiguate sound occlusion. Experimental results under various situations are presented, which proves the effectiveness of our method.

Su, D., Vidal Calleja, T.A. & Valls Miro, J. 2016, 'Split Conditional Independent Mapping for Sound Source Localisation with Inverse-Depth Parametrisation', IEEE International Conference on Intelligent Robots and Systems, IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Daejeon, Korea, pp. 2000-2006.
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In this paper, we propose a framework to map stationary sound sources while simultaneously localise a moving robot. Conventional methods for localisation and sound source mapping rely on a microphone array and either, a proprioceptive sensor only (such as wheel odometry) or an additional exteroceptive sensor (such as cameras or lasers) to get accurately the robot locations. Since odometry drifts over time and sound observations are bearing-only, sparse and extremely noisy, the former can only deal with relatively short trajectories before the whole map drifts. In comparison, the latter can get more accurate trajectory estimation over long distances and a better estimation of the sound source map as a result. However, in most of the work in the literature, trajectory estimation and sound source mapping are treated as uncorrelated, which means an update on the robot trajectory does not propagate properly to the sound source map. In this paper, we proposed an efficient method to correlate robot trajectory with sound source mapping by exploiting the conditional independence property between two maps estimated by two different Simultaneous Localisation and Mapping (SLAM) algorithms running in parallel. In our approach, the first map has the flexibility that can be built with any SLAM algorithm (filtering or optimisation) to estimate robot poses with an exteroceptive sensor. The second map is built by using a filtering-based SLAM algorithm locating all stationary sound sources parametrised with Inverse Depth Parametrisation (IDP). Robot locations used during IDP initialisation are the common features shared between the two SLAM maps, which allow to propagate information accordingly. Comprehensive simulations and experimental results show the effectiveness of the proposed method.

Sun, L., Vidal Calleja, T.A. & Valls Miro, J. 2016, 'Gaussian Markov Random Fields for Fusion in Information Form', Proceedings - IEEE International Conference on Robotics and Automation, IEEE International Conference on Robotics and Automation, Institute of Electrical and Electronics Engineers (IEEE), Stockholm, Sweden, pp. 1840-1845.
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2.5D maps are preferable for representing the environment owing to compactness. When noisy observations from diverse sensors at different resolutions are available, the problem of 2.5D mapping turns to how to compound the information in an effective and efficient manner. This paper proposes a generic probabilistic framework for fusing efficiently multiple sources of sensor data to generate amendable, high-resolution 2.5D maps. The key idea is to exploit the sparsity of the information matrix. Gaussian Markov Random Fields are employed to learn a prior map, using the conditional independence property between location to obtain a representation of the state. This prior map encoded in information form can then be updated with other sources of data in constant time. Later, mean state vector and variances can be efficiently recovered using numerical methods. The proposed approach allows accurate estimation of 2.5D maps at arbitrary resolution, while incorporating sensor noise and spatial dependency in a statistically reasonable way. We apply the proposed framework to pipe wall thickness mapping and fuse data from two diverse sensors that have different resolutions. Experimental results are compared with three other approaches, showing that, while greatly reducing computation time, the proposed framework is able to capture in large extend the spatial correlation to generate equivalent results to the expensive optimal fusion method in covariance form with a Gaussian Process prior.

Takami, K., Furukawa, T., Kumon, M. & Dissanayake, G. 2015, 'Non-field-of-view acoustic target estimation in complex indoor environment', Proceedings of the 10th Field and Service Robotics (FSR), International Conference on Field and Service Robotics, Springer, Toronto, Canada, pp. 577-592.
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© Springer International Publishing Switzerland 2016.This paper presents a new approach which acoustically localizes a mobile target outside the Field-of-View (FOV), or the Non-Field-of-View (NFOV), of an optical sensor, and its implementation to complex indoor environments. In this approach, microphones are fixed sparsely in the indoor environment of concern. In a prior process, the Interaural Level Difference IID of observations acquired by each set of two microphones is derived for different sound target positions and stored as an acoustic cue. When a newsound is observed in the environment, a joint acoustic observation likelihood is derived by fusing likelihoods computed from the correlation of the IID of the new observation to the stored acoustic cues. The location of the NFOVtarget is finally estimated within the recursive Bayesian estimation framework. After the experimental parametric studies, the potential of the proposed approach for practical implementation has been demonstrated by the successful tracking of an elderly person needing health care service in a home environment.

Takami, K., Liu, H., Furukawa, T., Kumon, M. & Dissanayake, G. 2016, 'Non-Field-of-View Sound Source Localization Using Diffraction and Reflection Signals', Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on, IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Daejeon, Korea, pp. 157-162.
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This paper describes a non-field-of-view (NFOV) localization approach for a mobile robot in an unknown environment based on an acoustic signal combined with the geometrical information from an optical sensor. The approach estimates the location of a target through the mobile robot's sensor observation frame, which consists of a combination of diffraction and reflection acoustic signals and a 3-D environment geometrical description. This fusion of audio-visual sensor observation likelihoods allows the robot to estimate the NFOV target. The diffraction and reflection observations from the microphone array generate the acoustic joint observation likelihood. The observed geometry also determines far-field or near-field acoustic conditions to improve the estimation of the sound direction of arrival. A mobile robot equipped with a microphone array and an RGB-D sensor was tested in a controlled environment, an anechoic chamber, to demonstrate the NFOV localization capabilities. This resulted in +/- 18 degrees, and less than 0.75 m error in angle and distance estimation, respectively.

Takami, K., Liu, H., Makoto, K., Furukawa, T. & Dissanayake, G. 2016, 'Recursive Bayesian estimation of NFOV target using diffraction and reflection signals', FUSION 2016 - 19th International Conference on Information Fusion, Proceedings, International Conference on Information Fusion, IEEE, Heidelberg, Germany, pp. 1923-1930.
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© 2016 ISIF.This paper presents an approach to the recursive Bayesian estimation of non-field-of-view (NFOV) sound source tracking based on reflection and diffraction signals with an incorporation of optical sensors. The approach takes multi-modal sensoy fusion of a mobile robot, which combines an optical 3D environment geometrical description with a microphone array acoustic signal to estimate the target location. The robot estimates target location either in the field-of-view (FOV) or in the NFOV by fusion of sensor observation likelihoods. For the NFOV case, the microphone array provides reflection and diffraction observations to generate a joint acoustic observation likelihood. With the data fusion between the 3D description and the acoustic observation, the target estimation is performed in an unknown environment. Finally, the sensor observation combined with the motion model of the target iteratively performs tracking within a recursive Bayesian estimation framework. The proposed approach was tested with a microphone array with an RGB-D sensor in a controlled anechoic chamber to demonstrate the NFOV tracking capabilities for a moving target.

Thiyagarajan, K. & Kodagoda, S. 2016, 'Analytical Model and Data-driven Approach for Concrete Moisture Prediction', ISARC 2016 - 33rd International Symposium on Automation and Robotics in Construction, International Symposium on Automation and Robotics in Construction, IAARC, Auburn, Alabama, USA..
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Thiyagarajan, K., Kodagoda, S. & Alvarez, J.K. 2016, 'An Instrumentation System for Smart Monitoring of Surface Temperature', 2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016, International Conference on Control, Automation, Robotics and Vision, IEEE, Phuket, Thailand.
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Thiyagarajan, K., Kodagoda, S. & Ulapane, N. 2016, 'Data-driven Machine Learning Approach for Predicting Volumetric Moisture Content of Concrete Using Resistance Sensor Measurements', Proceedings of the 11th IEEE Conference on Industrial Electronics and Applications (ICIEA 2016), IEEE Conference on Industrial Electronics and Applications, IEEE, Hefei, China, pp. 1288-1293.
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In sewerage industry, hydrogen sulphide induced corrosion of reinforced concretes is a global problem. To achieve a comprehensive knowledge of the propagation of concrete corrosion, it is vital to monitor the critical factors such as moisture. In this context, this paper investigates the use of resistance measuring and processing for estimating the concrete moisture content. The behavior of concrete moisture with resistance and surface temperature are studied and the effects of pH concentration on concrete are analyzed. Gaussian Process regression modeling is carried out to predict volumetric moisture content of concrete, where the results from experimental data are used to train the prediction model.

Wang, Y., Xiong, R., Huang, S. & Wu, J. 2015, 'Multi-Session SLAM Over Low Dynamic Workspace Using RGBD Sensor', ASSISTIVE ROBOTICS: Proceedings of the 18th International Conference on CLAWAR 2015, International Conference on Climbing and Walking Robots (CLAWAR), World Scientific, Hangzhou, China, pp. 633-640.
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Wijerathna, B.S., Falque, R., Kodagoda, S. & Dissanayake, G. 2016, 'Linear Approximation for Mapping Remaining Wall Thickness Using aMagnetic Flux Leakage Sensor', Website Proceedings of the 2016 Australasian Conference on Robotics and Automation, Australasian Conference on Robotics and Automation, ACRA, The University of Queensland, pp. 1-8.
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Use of an unconventional sensor for mapping the remaining wall thickness of a pipe is presented in this paper. This is achieved through the development of a sensor model relating the measurements from a Magnetic Flux Leakage (MFL) sensor to the environment geometry. Conventional sensors, such as laser-range finders commonly used in the robotic community are not able to infer thickness profiles of ferromagnetic structures such as water pipes when the surface is covered with corrosion products. Sensors based on electromagnetic principles or ultrasound are the methods of choice in such situations to estimate the extent of corrosion and predict eventual failure. The general relationship between readings from electromagnetic sensors and the environment geometry is governed by a set of partial differential equations (Maxwells equations). However, in the case of an MFL sensor, it is demonstrated that a linear combination of the thickness profiles can be used to adequately model the sensor signal. Parameters associated with the sensor model are obtained using a two-dimensional fi- nite element simulations. Extensive simulation results are presented to validate the proposed method by estimating a remaining wall thickness map of a realistic pipe.

Woolfrey, J.K., Liu, D.K. & Carmichael, M. 2016, 'Kinematic Control of an Autonomous Underwater Vehicle-Manipulator System Using Autoregressive Prediction of Vehicle Motion and Model Predictive Control', Proceedings - IEEE International Conference on Robotics and Automation, IEEE International Conference on Robotics and Automation, IEEE, Stockholm, Sweden.
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Yang, C., Paul, G., Ward, P. & Liu, D. 2016, 'A Path Planning Approach Via Task-Objective Pose Selection with Application to an Inchworm-Inspired Climbing Robot', IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM, IEEE/ASME International Conference on Advanced Intelligent Mechatronics, IEEE, Banff, Canada, pp. 401-406.
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This paper presents a stepping path planning approach for a climbing robot inspired kinematically from an inchworm caterpillar’s looping locomotion. This approach generates an optimised multi-step path to traverse through space and to land a specific footpad onto a selected point on a surface with a specific footpad orientation. The candidate landing joint configuration for each step is generated by a pose selection process, using an optimisation technique with task- objective functions based on the constraints of the robot. Then another technique is used to obtain a new set of poses satisfying strict constraints of the landing motion. The set of candidate landing poses is used to compute the subsequent steps. A valid motion trajectory, which avoids all obstacles, can be generated by a point-to-point planner for each of the landing poses from the current pose. This single step planning technique is then expanded to multi-step path planning by building a search tree, where a combination of steps is evaluated and optimised by a cost function, which includes objectives related to robot movement. This approach is implemented and validated on the climbing robot in real-world steel bridge environments. The planner successfully finds multi-step paths in these field trials enabling the robot to traverse through several complex structures inside the bridge steel box girders.

Zhang, T., Huang, S., Liu, D., Shi, L., Zhou, C. & Xiong, R. 2016, 'A Method of State Estimation for Underwater Vehicle Navigation Around A Cylindrical Structure', 2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA), IEEE Conference on Industrial Electronics and Applications, IEEE, Hefei, China, pp. 101-106.
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Recently, increasing efforts have been focused on the development and adoption of autonomous underwater vehicles (AUVs) for various applications. However, the GPS signals are usually unavailable, the vehicle dynamics is very uncertain, and the complicated vision based localization algorithms may not work well in the underwater environments. Hence, accurate and timely state estimation using low-cost sensors remains a challenge for the control and navigation of AUVs. This paper considers the state estimation problem for underwater vehicle navigation around a cylindrical structure. The vehicle is assumed to be equipped with only low-cost sensors: an inertia measurement unit (IMU), a pressure sensor and a monocular camera. By exploiting the prior knowledge on the size and shape of the structure, an efficient algorithm for estimating the state of the AUV is developed without using any dynamic model. Firstly, a state observer is proposed under the condition that the localization result (rotational and translational position) is available. Next, we present a method for localization based on the IMU readings, pressure sensor readings and the image of the cylindrical structure, which uses the geometry of the structure and only requires simple image processing (line extraction). Then we prove that the proposed observer is globally stable. Preliminary experimental results and simulation results are reasonable and promising, which implies the proposed method has potential to be used in the real AUV navigation applications.

Zhao, L., Giannarou, S., Lee, S.-.L. & Yang, G.Z. 2016, 'Registration-free Simultaneous Catheter and Environment Modelling', Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016 (LNCS), Medical Image Computing and Computer-Assisted Intervention, Springer, Athens, Greece, pp. 525-533.
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Endovascular procedures are challenging to perform due to the complexity and difficulty in catheter manipulation. The simultaneous recovery of the 3D structure of the vasculature and the catheter position and orientation intra-operatively is necessary in catheter control and navigation. State-of-art Simultaneous Catheter and Environment Modelling provides robust and real-time 3D vessel reconstruction based on real-time intravascular ultrasound (IVUS) imaging and electromagnetic (EM) sensing, but still relies on accurate registration between EM and pre-operative data. In this paper, a registration-free vessel reconstruction method is proposed for endovascular navigation. In the optimisation framework, the EM-CT registration is estimated and updated intra-operatively together with the 3D vessel reconstruction from IVUS, EM and pre-operative data, and thus does not require explicit registration. The proposed algorithm can also deal with global (patient) motion and periodic deformation caused by cardiac motion. Phantom and in-vivo experiments validate the accuracy of the algorithm and the results demonstrate the potential clinical value of the technique.

Zhao, L., Giannarou, S., Lee, S.-.L., Merrifield, R. & Yang, G.Z. 2016, 'Intra-operative Simultaneous Catheter and Environment Modelling for Endovascular Navigation Based on Intravascular Ultrasound, Electromagnetic Tracking and Pre-operative Data', Proceedings of The Hamlyn Symposium on Medical Robotics, The Hamlyn Symposium on Medical Robotics, Imperial College London and the Royal Geographical Society, London, UK, pp. 76-77.
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Cardiovascular diseases (CVD) form the single most common cause of death. Catheter procedures are among the most common surgical interventions used to treat CVD. Due to their minimal access trauma, these procedures extend the range of patients able to receive interventional CVD treatment to age groups dominated by co-morbidity and unacceptable risks for open surgery [1]. The downside associated with minimising access incisions lies at the increased complexity and difficult manipulation of the instruments and anatomical targets, which is mainly caused by the loss of direct access to the anatomy and the poor visualisation of the surgical site. The current clinical approaches to endovascular procedures mainly rely on 2D guidance based on X-ray fluoroscopy, which uses ionising radiation and dangerous contrast agents [2]. In this paper, a Simultaneous Catheter and Environment Modelling (SCEM) method is presented for endovascular navigation based on intravascular ultrasound (IVUS) imaging, electromagnetic (EM) sensing as well as the vessel structure information provided from the pre-operative CT/MR imaging (see Fig. 1). Thus, radiation dose and contrast agents are avoided. The proposed SCEM intra-operatively recovers the 3D structure of the vasculature together with the pose of the catheter tip, which the knowledge of the interaction between the catheter and its surroundings can be provided. The corresponding uncertainties of both vessel reconstruction and catheter pose can also be computed which is necessary for autonomous robotic catheter navigation. Experimental results using three different phantoms, with different catheter motions and cardiac motions simulated by using a periodic pump demonstrated the accuracy of the vessel reconstruction and the potential clinical value of the proposed SCEM method.

Cliff, O.M., Prokopenko, M. & Fitch, R. 2016, An Information Criterion for Inferring Coupling in Complex Networks.

Ulapane 2016, 'Nondestructive evaluation of ferromagnetic critical water pipes using pulsed eddy current testing'.
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Modern day maintenance of infrastructure demands significant attention to structural health monitoring. Assessment of surface condition alone is insufficient for health and strength assessment, creating the necessity to evaluate the integrity of subsurface regions through Nondestructive Evaluation (NDE). This thesis focuses on approaches to solving the problem of condition assessment of critical pipes, i.e., large diameter high-pressure pipes owned and managed by water utilities to distribute consumable fresh water to customers, by developing techniques for representing the geometry of electrically conductive ferromagnetic materials via Pulsed Eddy Current (PEC) sensors. The main contribution of this thesis is a novel detector coil voltage decay rate based PEC signal feature, the fundamental behavior of the feature is analytically described and experimentally validated. The feature has a convenient advantage in practical application since it is directly extractable from raw PEC signals and demonstrates significant invariance to sensor shape, size, and lift-off. The feature behavior is exploited in two estimation approaches, in situ measurements on pipes are performed and pipe wall thickness is inferred with uncertainty. Firstly, an analytical approach to learning a function mapping the decay rate feature to test piece thickness with the aid of signals captured on calibration blocks is presented. The requirement of fabricating calibration blocks to have material properties matching those of pipes is extremely challenging. Thus, combining ultrasound measurements together with PEC is proposed to address material variations. Secondly, a numerical NDE semi-parametric estimation approach is presented, PEC sensor signals are simulated taking into account measured electrical and magnetic properties of materials being tested. The thickness-feature function is learned probabilistically using Gaussian Process. Unlike in the analytical approach, the function is learned non...