Arora, A, Furlong, PM, Fitch, R, Sukkarieh, S & Fong, T 2019, 'Multi-modal active perception for information gathering in science missions', Autonomous Robots.
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Robotic science missions in remote environments, such as deep ocean and outer space, can involve studying phenomena that cannot directly be observed using on-board sensors but must be deduced by combining measurements of correlated variables with domain knowledge. Traditionally, in such missions, robots passively gather data along prescribed paths, while inference, path planning, and other high level decision making is largely performed by a supervisory science team located at a different location, often at a great distance. However, communication constraints hinder these processes, and hence the rate of scientific progress. This paper presents an active perception approach that aims to reduce robots’ reliance on human supervision and improve science productivity by encoding scientists’ domain knowledge and decision making process on-board. We present a Bayesian network architecture to compactly model critical aspects of scientific knowledge while remaining robust to observation and modeling uncertainty. We then formulate path planning and sensor scheduling as an information gain maximization problem, and propose a sampling-based solution based on Monte Carlo tree search to plan informative sensing actions which exploit the knowledge encoded in the network. The computational complexity of our framework does not grow with the number of observations taken and allows long horizon planning in an anytime manner, making it highly applicable to field robotics with constrained computing. Simulation results show statistically significant performance improvements over baseline methods, and we validate the practicality of our approach through both hardware experiments and simulated experiments with field data gathered during the NASA Mojave Volatiles Prospector science expedition.
Best, G, Cliff, OM, Patten, T, Mettu, RR & Fitch, R 2019, 'Dec-MCTS: Decentralized planning for multi-robot active perception', International Journal of Robotics Research, vol. 38, no. 2-3, pp. 316-337.
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© The Author(s) 2018. We propose a decentralized 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 optimize its own actions by maintaining a probability distribution over plans in the joint-action space. Robots periodically communicate a compressed form of their search trees, which are used to update the joint distribution using a distributed optimization approach inspired by variational methods. Our method admits any objective function defined over robot action sequences, assumes intermittent communication, is anytime, and is suitable for online replanning. Our algorithm features a new MCTS tree expansion policy that is designed for our planning scenario. We extend the theoretical analysis of standard MCTS to provide guarantees for convergence rates to the optimal payoff sequence. We evaluate the performance of our method for generalized team orienteering and online active object recognition using real data, and show that it compares favorably to centralized MCTS even with severely degraded communication. These examples demonstrate the suitability of our algorithm for real-world active perception with multiple robots.
Bykerk, L, Quin, P & Liu, D 2019, 'A Method for Selecting the Next Best Angle-of-Approach for Touch-Based Identification of Beam Members in Truss Structures', IEEE Sensors Journal, vol. 19, no. 10, pp. 3939-3949.
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© 2001-2012 IEEE. A robot designed to climb truss structures such as power transmission towers is expected to have an adequate tactile sensing in the grippers to identify a structural beam member and its properties. Depending on how a gripper grasps a structural member, defined as the Angle-of-Approach (AoA), the extracted tactile data can result in erroneous identifications due to the similarities in beam cross-sectional shapes and sizes. In these cases, further grasps at favorable Angles-of-Approach (AoAs) are required to correctly identify the beam member and its properties. This paper presents an information-based method which uses tactile data to determine the next best AoA for the identification of beam members in truss structures. The method is used in conjunction with a state estimate of beam shape, dimension, and AoA calculated by a Random Forest classifier. The method is verified through simulation by using the data collected using a soft gripper retrofitted with simple tactile sensors. The results show that this method can correctly identify a structural beam member and its properties with a small number of grasps (typically fewer than 4). This method can be applied to other adaptive robotic gripper designs fitted with suitable tactile sensors, regardless of the number of sensors used and their layout.
Cui, Y, Poon, J, Miro, JV, Yamazaki, K, Sugimoto, K & Matsubara, T 2019, 'Environment-adaptive interaction primitives through visual context for human–robot motor skill learning', Autonomous Robots.
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© 2018, The Author(s). In situations where robots need to closely co-operate with human partners, consideration of the task combined with partner observation maintains robustness when partner behavior is erratic or ambiguous. This paper documents our approach to capture human–robot interactive skills by combining their demonstrative data with additional environmental parameters automatically derived from observation of task context without the need for heuristic assignment, as an extension to overcome shortcomings of the interaction primitives framework. These parameters reduce the partner observation period required before suitable robot motion can commence, while also enabling success in cases where partner observation alone was inadequate for planning actions suited to the task. Validation in a collaborative object covering exercise with a humanoid robot demonstrate the robustness of our environment-adaptive interaction primitives, when augmented with parameters directly drawn from visual data of the task scene.
Ernesto Solanes, J, Gracia, L, Muñoz-Benavent, P, Valls Miro, J, Perez-Vidal, C & Tornero, J 2019, 'Robust Hybrid Position-Force Control for Robotic Surface Polishing', Journal of Manufacturing Science and Engineering, Transactions of the ASME, vol. 141, no. 1.
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© 2018 HMP Communications. All rights reserved. This work presents a hybrid position-force control of robots for surface polishing using task priority. The robot force control is designed using sliding mode ideas in order to benefit from its inherent robustness and low computational cost. In order to avoid the chattering drawback typically present in sliding mode control, several chattering-free controllers are evaluated and tested. A distinctive feature of the method is that the sliding mode force task is defined using not only equality constraints but also inequality constraints, which are satisfied using conventional and nonconventional sliding mode control, respectively. Moreover, a lower priority tracking controller is defined to follow the desired reference trajectory on the surface being polished. The applicability and the effectiveness of the proposed approach considering the mentioned chattering-free controllers are substantiated by experimental results using a redundant 7R manipulator.
Ghaffari Jadidi, M, Valls Miro, J & Dissanayake, G 2019, 'Sampling-based incremental information gathering with applications to robotic exploration and environmental monitoring', International Journal of Robotics Research, vol. 38, no. 6, pp. 658-685.
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© The Author(s) 2019. We propose a sampling-based motion-planning algorithm equipped with an information-theoretic convergence criterion for incremental informative motion planning. The proposed approach allows dense map representations and incorporates the full state uncertainty into the planning process. The problem is formulated as a constrained maximization problem. Our approach is built on rapidly exploring information-gathering algorithms and benefits from the advantages of sampling-based optimal motion-planning algorithms. We propose two information functions and their variants for fast and online computations. We prove an information-theoretic convergence for an entire exploration and information-gathering mission based on the least upper bound of the average map entropy. A natural automatic stopping criterion for information-driven motion control results from the convergence analysis. We demonstrate the performance of the proposed algorithms using three scenarios: comparison of the proposed information functions and sensor configuration selection, robotic exploration in unknown environments, and a wireless signal strength monitoring task in a lake from a publicly available dataset collected using an autonomous surface vehicle.
Hassan, M, Liu, D & Xu, D 2019, 'A Two-Stage Approach to Collaborative Fiber Placement through Coordination of Multiple Autonomous Industrial Robots', Journal of Intelligent and Robotic Systems.
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Hodges, J, Attia, T, Arukgoda, J, Kang, C, Cowden, M, Doan, L, Ranasinghe, R, Abdelatty, K, Dissanayake, G & Furukawa, T 2019, 'Multistage bayesian autonomy for high-precision operation in a large field', Journal of Field Robotics, vol. 36, no. 1, pp. 183-203.
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© 2018 Wiley Periodicals, Inc. This paper presents a generalized multistage bayesian framework to enable an autonomous robot to complete high-precision operations on a static target in a large field. The proposed framework consists of two multistage approaches, capable of dealing with the complexity of high-precision operation in a large field to detect and localize the target. In the multistage localization, locations of the robot and the target are estimated sequentially when the target is far away from the robot, whereas these locations are estimated simultaneously when the target is close. A level of confidence (LOC) for each detection criterion of a sensor and the associated probability of detection (POD) of the sensor are defined to make the target detectable with different LOCs at varying distances. Differential entropies of the robot and target are used as a precision metric for evaluating the performance of the proposed approach. The proposed multistage observation and localization approaches were applied to scenarios using an unmanned ground vehicle (UGV) and an unmanned aerial vehicle (UAV). Results with the UGV in simulated environments and then real environments show the effectiveness of the proposed approaches to real-world problems. A successful demonstration using the UAV is also presented.
Huang, S 2019, 'A review of optimisation strategies used in simultaneous localisation and mapping', Journal of Control and Decision, vol. 6, no. 1, pp. 61-74.
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© 2018, © 2018 Northeastern University, China. This paper provides a brief review of the different optimisation strategies used in mobile robot simultaneous localisation and mapping (SLAM) problem. The focus is on the optimisation-based SLAM back end. The strategies are classified based on their purposes such as reducing the computational complexity, improving the convergence and improving the robustness. It is clearly pointed out that some approximations are made in some of the methods and there is always a trade-off between the computational complexity and the accuracy of the solution. The local submap joining is a strategy that has been used to address both the computational complexity and the convergence and is a flexible tool to be used in the SLAM back end. Although more research is needed to further improve the SLAM back end, nowadays there are quite a few relatively mature SLAM back end algorithms that can be used by SLAM researchers and users.
Khosoussi, K, Giamou, M, Sukhatme, GS, Huang, S, Dissanayake, G & How, JP 2019, 'Reliable Graphs for SLAM', International Journal of Robotics Research, vol. 38, no. 2-3, pp. 260-298.
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© The Author(s) 2019. Estimation-over-graphs (EoG) is a class of estimation problems that admit a natural graphical representation. Several key problems in robotics and sensor networks, including sensor network localization, synchronization over a group, and simultaneous localization and mapping (SLAM) fall into this category. We pursue two main goals in this work. First, we aim to characterize the impact of the graphical structure of SLAM and related problems on estimation reliability. We draw connections between several notions of graph connectivity and various properties of the underlying estimation problem. In particular, we establish results on the impact of the weighted number of spanning trees on the D-optimality criterion in 2D SLAM. These results enable agents to evaluate estimation reliability based only on the graphical representation of the EoG problem. We then use our findings and study the problem of designing sparse SLAM problems that lead to reliable maximum likelihood estimates through the synthesis of sparse graphs with the maximum weighted tree connectivity. Characterizing graphs with the maximum number of spanning trees is an open problem in general. To tackle this problem, we establish several new theoretical results, including the monotone log-submodularity of the weighted number of spanning trees. We exploit these structures and design a complementary greedy–convex pair of efficient approximation algorithms with provable guarantees. The proposed synthesis framework is applied to various forms of the measurement selection problem in resource-constrained SLAM. Our algorithms and theoretical findings are validated using random graphs, existing and new synthetic SLAM benchmarks, and publicly available real pose-graph SLAM datasets.
Le, A, Le, H, Nguyen, L & Phan, M 2019, 'An efficient adaptive hierarchical sliding mode control strategy using neural networks for 3D overhead cranes', International Journal of Automation and Computing.
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In this paper, a new adaptive hierarchical sliding mode control scheme for a 3D overhead crane system is proposed. A controller is first designed by the use of a hierarchical structure of two first-order sliding surfaces represented by two actuated and un-actuated subsystems in the bridge crane. Parameters of the controller are then intelligently estimated, where uncertain parameters due to disturbances in the 3D overhead crane dynamic model are proposed to be represented by radial basis function networks whose weights are derived from a Lyapunov function. The proposed approach allows the crane system to be robust under uncertainty conditions in which some uncertain and unknown parameters are highly difficult to determine. Moreover, stability of the sliding surfaces is proved to be guaranteed. Effectiveness of the proposed approach is then demonstrated by implementing the algorithm in both synthetic and real-life systems, where the results obtained by our method are highly promising.
Le, HX, Le, AV & Nguyen, L 2019, 'Adaptive Fuzzy Observer based Hierarchical Sliding Mode Control for Uncertain 2D Overhead Cranes', Cyber-Physical Systems.
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This paper proposes a new approach to robustly control a 2D under-actuated overhead crane system, where a payload is effectively transported to a destination in real time with small sway angles, given its inherent uncertainties such as actuator nonlinearities and external disturbances. The control law is proposed to be developed by the use of the robust hierarchical sliding mode control (HSMC) structure in which a second-level sliding surface is formulated by two first-level sliding surfaces drawn on both actuated and under-actuated outputs of the crane. The unknown and uncertain parameters of the proposed control scheme are then adaptively estimated by the fuzzy observer, where the adaptation mechanism is derived from the Lyapunov theory. More importantly, stability of the proposed strategy is theoretically proved. Effectiveness of the proposed adaptive fuzzy observer based HSMC (AFHSMC) approach was extensively validated by implementing the algorithm in both synthetic simulations and real-life experiments, where the results obtained by our method are highly promising.
Liu, L, Zhang, T, Leighton, B, Zhao, L, Huang, S & Dissanayake, G 2019, 'Robust Global Structure From Motion Pipeline With Parallax on Manifold Bundle Adjustment and Initialization', IEEE Robotics and Automation Letters, vol. 4, no. 2, pp. 2164-2171.
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© 2016 IEEE. In this letter, we present a novel global structure from motion (SfM) pipeline that is particularly effective in dealing with low-parallax scenes and camera motion collinear with the features that represent the environment structure. It is therefore particularly suitable in Urban SLAM, in which frequent road-facing motion poses many challenges to conventional SLAM algorithms. Our pipeline includes a recently explored bundle adjustment (BA) method that exploits a feature parameterization using Parallax angle between on-Manifold observation rays (PMBA). It is demonstrated that this BA stage has a consistently stable optimization configuration for features with any parallax and therefore low-parallax features can stay in reconstruction without pre-filtering. To allow practical usage of PMBA, we provide a compatible initialization stage in the SfM to initialize all camera poses simultaneously, exhibiting friendliness to collinear motion. This is achieved by simplifying PMBA into a hybrid graph problem of high connectivity yet small node set size, solved using a robust linear programming technique. Using simulations and a series of publicly available real datasets including "KITTI" and "Bundle Adjustment in the Large," we demonstrate the robustness of the position initialization stage in handling collinear motion and outlier matches, superior convergence performance of the BA stage in the presence of low-parallax features, and effectiveness of our pipeline to handle many sequential or out-of-order urban scenes.
Nguyen, L, Valls Miro, J & Qiu, X 2019, 'Multilevel B-Splines based Learning Approach for Sound Source Localization', IEEE Sensors Journal, vol. 10, no. 10, pp. 3871-3881.
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In this paper, a new learning approach for sound source localization is presented using ad hoc either synchronous or asynchronous distributed microphone networks based on the time differences of arrival (TDOA) estimation. It is first to propose a new concept in which the coordinates of a sound source location are defined as the functions of TDOAs, computing for each pair of microphone signals in the network. Then, given a set of pre-recorded sound measurements and their corresponding source locations, the multilevel B-splines-based learning model is proposed to be trained by the input of the known TDOAs and the output of the known coordinates of the sound source locations. For a new acoustic source, if its sound signals are recorded, the correspondingly computed TDOAs can be fed into the learned model to predict the location of the new source. Superiorities of the proposed method are to incorporate the acoustic characteristics of a targeted environment and even remaining uncertainty of TDOA estimations into the learning model before conducting its prediction and to be applicable for both synchronous or asynchronous distributed microphone sensor networks. The effectiveness of the proposed algorithm in terms of localization accuracy and computational cost in comparisons with the state-of-the-art methods was extensively validated on both synthetic simulation experiments as well as in three real-life environments.
Nguyen, LV, Hu, G & Spanos, CJ 2019, 'Efficient Sensor Deployments for Spatio-Temporal Environmental Monitoring', IEEE Transactions on Systems Man and Cybernetics: Systems.
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This paper addresses the problem of efficiently deploying sensors in spatial environments, e.g., buildings, for the purposes of monitoring spatio-temporal environmental phenomena. By modeling the environmental fields using spatio-temporal Gaussian processes, a new and efficient optimality-cost function of minimizing prediction uncertainties is proposed to find the best sensor locations. Though the environmental processes spatially and temporally vary, the proposed approach of choosing sensor positions is proven not to be affected by time variations, which significantly reduces computational complexity of the optimization problem. The sensor deployment optimization problem is then solved by a practical and feasible polynomial algorithm, where its solutions are theoretically proven to be guaranteed. The proposed method is also theoretically and experimentally compared with the existing works. The effectiveness of the proposed algorithm is demonstrated by implementation in a real tested space in a university building, where the obtained results are highly promising.
Nguyen, T, Thai, N, Pham, H, Phan, T, Le, H, Nguyen, H & Nguyen, L 2019, 'Adaptive neural network based backstepping sliding mode control approach for dual arm robots', Journal of Control, Automation and Electrical Systems, vol. 30, pp. 512-521.
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The paper introduces an adaptive strategy to effectively control a nonlinear dual arm robot under external disturbances and uncertainties. By the use of the backstepping sliding mode control (BSSMC) method,
the proposed algorithm rst allows the manipulators to be able to robustly track the desired trajectories. Furthermore, due to the nonlinear, uncertain and unmodelled dynamics of the dual arm robot, it is proposed to employ the radial basis function network (RBFN) to adaptively estimate the robot's dynamic model. Though the estimation of the dynamics is approximate, the adaptation law is derived from the Lyapunov theory, which provides the controller with ability to guarantee stability of the whole system in spite of its nonlinearities, parameter uncertainties and external load variations. The effectiveness of the proposed RBFN-BSSMC approach is demonstrated by implementation in a simulation environment with realistic parameters, where the obtained results are highly promising.
Shakor, P, Nejadi, S & Paul, G 2019, 'A Study into the Effect of Different Nozzles Shapes and Fibre-Reinforcement in 3D Printed Mortar', Materials, vol. 12, no. 10.
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Recently, 3D printing has become one of themost popular additivemanufacturing technologies.
This technology has been utilised to prototype trial and produced components for various applications,
such as fashion, food, automotive,medical, and construction. In recent years, automation also has become
increasingly prevalent in the construction field. Extrusion printing is the most successful method to print
cementitiousmaterials, but it still faces significant challenges, such as pumpability ofmaterials, buildability,
consistency in the materials, flowability, and workability. This paper investigates the properties of 3D
printed fibre-reinforced cementitious mortar prisms and members in conjunction with automation to
achieve the optimum mechanical strength of printed mortar and to obtain suitable flowability and
consistent workability for the mixed cementitious mortar during the printing process. This study also
considered the necessary trial tests, which are required to check the mechanical properties and behaviour
of the proportions of the cementitious mix. Mechanical strength was measured and shown to increase
when the samples were printed using fibre-reinforced mortar by means of a caulking gun, compared
with the samples that were printed using the same mix delivered by a progressive cavity pump to a
6 degree-of-freedom robot. The flexural strength of the four-printed layer fibre-reinforced mortar was
found to be 3.44 0.11MPa and 5.78 0.02MPa for the one-layer. Moreover, the mortar with different
types of nozzles by means of caulking is printed and compared. Several experimental tests for the fresh
state of the mortar were conducted and are discussed.
Shakor, P, Nejadi, S, Paul, G & Malek, S 2019, 'Review of emerging additive manufacturing technologies in 3D printing of cementitious materials in the construction industry', Frontiers in Built Environment, vol. 4.
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Additive manufacturing is a fabrication technology that is rapidly revolutionizing the manufacturing and construction sectors. In this paper, a review of various prototyping technologies for printing cementitious materials and selected 3D printing techniques are presented in detail. Benchmark examples are provided to compare three well-known printing techniques; inkjet printing (binder jetting), selected laser sintering (SLS), and extrusion printing (extrusion based process). A comprehensive search in the literature was conducted to identify various mix designs that could be employed when printing cementitious materials. Aspects of concrete mix design are described, and some new experiments are conducted to analyse the printability of new mixes by the authors. Future research in the area of the rheology of cementitious materials and its relationship with the structural performance of finished concretes are highlighted.
Shakor, P, Nejadi, S, Paul, G & Malekmohammadi, S 2019, 'Review of emerging additive manufacturing technologies in 3D printing of cementitious materials in the construction industry', Frontiers in Built Environment, vol. 4, pp. 1-17.
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Shakor, P, Nejadi, S, Paul, G, Sanjayan, J & Nazari, A 2019, 'Mechanical Properties of Cement-Based Materials and Effect of Elevated Temperature on Three-Dimensional (3-D) Printed Mortar Specimens in Inkjet 3-D Printing', ACI Materials Journal, vol. 116, no. 2, pp. 55-67.
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Tang, L, Wang, Y, Ding, X, Yin, H, Xiong, R & Huang, S 2019, 'Topological local-metric framework for mobile robots navigation: a long term perspective', Autonomous Robots, vol. 43, no. 1, pp. 197-211.
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© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Long term mapping and localization are the primary components for mobile robots in real world application deployment, of which the crucial challenge is the robustness and stability. In this paper, we introduce a topological local-metric framework (TLF), aiming at dealing with environmental changes, erroneous measurements and achieving constant complexity. TLF organizes the sensor data collected by the robot in a topological graph, of which the geometry is only encoded in the edge, i.e. the relative poses between adjacent nodes, relaxing the global consistency to local consistency. Therefore the TLF is more robust to unavoidable erroneous measurements from sensor information matching since the error is constrained in the local. Based on TLF, as there is no global coordinate, we further propose the localization and navigation algorithms by switching across multiple local metric coordinates. Besides, a lifelong memorizing mechanism is presented to memorize the environmental changes in the TLF with constant complexity, as no global optimization is required. In experiments, the framework and algorithms are evaluated on 21-session data collected by stereo cameras, which are sensitive to illumination, and compared with the state-of-art global consistent framework. The results demonstrate that TLF can achieve similar localization accuracy with that from global consistent framework, but brings higher robustness with lower cost. The localization performance can also be improved from sessions because of the memorizing mechanism. Finally, equipped with TLF, the robot navigates itself in a 1 km session autonomously.
Ulapane, N & Nguyen, L 2019, 'Review of Pulsed Eddy Current Signal Feature Extraction Methods for Conductive Ferromagnetic Material Thickness Quantification', Electronics (Basel).
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Thickness quantification of conductive ferromagnetic materials has become a common necessity in present-day structural health monitoring and infrastructure maintenance. Recent research has found Pulsed Eddy Current (PEC) sensing, especially the detector-coil-based PEC sensor architecture, to effectively serve as a nondestructive sensing technique for this purpose. As a result, several methods of varying complexity have been proposed in recent years to extract PEC signal features, against which conductive ferromagnetic material thickness behaves as a function, in return enabling thickness quantification owing to functional behaviours. It can be seen that almost all features specifically proposed in the literature for the purpose of conductive ferromagnetic material-thickness quantification are in some way related to the diffusion time constant of eddy currents. This paper examines the relevant feature-extraction methods through a controlled experiment in which the methods are applied to a single set of experimentally captured PEC signals, and provides a review by discussing the quality of the extractable features, and their functional behaviours for thickness quantification, along with computational time taken for feature extraction. Along with this paper, the set of PEC signals and some MATLAB codes for feature extraction are provided as supplementary materials for interested readers.
Wang, J, Song, J, Zhao, L, Huang, S & Xiong, R 2019, 'A submap joining algorithm for 3D reconstruction using an RGB-D camera based on point and plane features', Robotics and Autonomous Systems, vol. 118, pp. 93-111.
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© 2019 Elsevier B.V. In standard point-based methods, the depth measurements of the point features suffer from noise, which will lead to incorrect global structure of the environment. This paper presents a submap joining based SLAM with an RGB-D camera by introducing planes as well as points as features.This work is consisted of two steps: submap building and submap joining. Several adjacent keyframes, with the corresponding small patches, visual feature points, and planes observed from these keyframes, are used to build a submap. We fuse the submaps into a global map in a sequential fashion, such that, the global structure is recovered gradually through plane feature associations and optimization. We also show that the proposed algorithm can handle plane association problem incrementally in submap level, as the plane covariance can be obtained in each submap. The use of submap significantly reduces the computational cost during the optimization process, while keeping all information about planes. The proposed method is validated using both publicly available RGB-D benchmarks and datasets collected by authors. The algorithm can produce accurate trajectories and high quality 3D models on these challenging datasets, which are difficult for existing RGB-D SLAM or SFM algorithms.
Woolfrey, J, Lu, W & Liu, D 2019, 'A Control Method for Joint Torque Minimization of Redundant Manipulators Handling Large External Forces', Journal of Intelligent and Robotic Systems: Theory and Applications.
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© 2019, The Author(s). In this paper, a control method is developed for minimizing joint torque on a redundant manipulator where an external force acts on the end-effector. Using null space control, the redundant task is designed to minimize the torque required to oppose the external force, and reduce the dynamic torque. Furthermore, the joint motion can be weighted to factor in physical constraints such as joint limits, collision avoidance, etc. Conventional methods for joint torque minimization only consider the internal dynamics of the manipulator. If external forces acting on the end-effector are inadvertently implemented in to these control methods this could lead to joint configurations that amplify the resulting joint torque. The proposed control method is verified through two different case studies. The first case study involves simulation of high-pressure blasting. The second is a simulation of a manipulator lifting and moving a heavy object. The results show that the proposed control method reduces overall joint torque compared to conventional methods. Furthermore, the joint torque is minimized such that there is potential for a manipulator to execute certain tasks beyond its nominal payload capacity.
Zhan, J, Ge, XJ, Huang, S, Zhao, L, Wong, JKW & He, SXJ 2019, 'Improvement of the inspection-repair process with building information modelling and image classification', Facilities, vol. 37, no. 7-8, pp. 395-414.
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© 2019, Emerald Publishing Limited. Purpose: Automated technologies have been applied to facility management (FM) practices to address labour demands of, and time consumed by, inputting and processing manual data. Less attention has been focussed on automation of visual information, such as images, when improving timely maintenance decisions. This study aims to develop image classification algorithms to improve information flow in the inspection-repair process through building information modelling (BIM). Design/methodology/approach: To improve and automate the inspection-repair process, image classification algorithms were used to connect images with a corresponding image database in a BIM knowledge repository. Quick response (QR) code decoding and Bag of Words were chosen to classify images in the system. Graphical user interfaces (GUIs) were developed to facilitate activity collaboration and communication. A pilot case study in an inspection-repair process was applied to demonstrate the applications of this system. Findings: The system developed in this study associates the inspection-repair process with a digital three-dimensional (3D) model, GUIs, a BIM knowledge repository and image classification algorithms. By implementing the proposed application in a case study, the authors found that improvement of the inspection-repair process and automated image classification with a BIM knowledge repository (such as the one developed in this study) can enhance FM practices by increasing productivity and reducing time and costs associated with ecision-making. Originality/value: This study introduces an innovative approach that applies image classification and leverages a BIM knowledge repository to enhance the inspection-repair process in FM practice. The system designed provides automated image-classifying data from a smart phone, eliminates time required to input image data manually and improves communication and collaboration between FM personnel for maintenance i...
Zhao, L, Huang, S & Dissanayake, G 2019, 'Linear SLAM: Linearising the SLAM problems using submap joining', AUTOMATICA, vol. 100, pp. 231-246.
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Aldini, S, Akella, A, Singh, A, Wang, Y-K, Carmichael, M, Liu, D & Lin, C-T 2019, 'Effect of Mechanical Resistance on Cognitive Conflict in Physical Human-Robot Collaboration', International Conference on Robotics and Automation, Canada.
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Bai, F, Vidal Calleja, T, Huang, S & Xiong, R 2018, 'Predicting Objective Function Change in Pose-Graph Optimization', International Conference on Intelligent Robots and Systems, IEEE, Madrid, pp. 1-8.
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Robust online incremental SLAM applications require metrics to evaluate the impact of current measurements. Despite its prevalence in graph pruning, information-theoretic metrics solely are insufficient to detect outliers. The optimal value of the objective function is a better choice to detect outliers but cannot be computed unless the problem is solved. In this paper, we show how the objective function change can be predicted in an incremental pose-graph optimization scheme, without actually solving the problem. The predicted objective function change can be used to guide online decisions or detect outliers. Experiments validate the accuracy of the predicted objective function, and an application to outlier detection is also provided, showing its advantages over M-estimators.
Bai, F, Vidal-Calleja, T, Huang, S & Xiong, R 2018, 'Predicting Objective Function Change in Pose-Graph Optimization', IEEE International Conference on Intelligent Robots and Systems, IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Madrid, Spain, pp. 145-152.
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© 2018 IEEE. Robust online incremental SLAM applications require metrics to evaluate the impact of current measurements. Despite its prevalence in graph pruning, information-theoretic metrics solely are insufficient to detect outliers. The optimal value of the objective function is a better choice to detect outliers but cannot be computed unless the problem is solved. In this paper, we show how the objective function change can be predicted in an incremental pose-graph optimization scheme, without actually solving the problem. The predicted objective function change can be used to guide online decisions or detect outliers. Experiments validate the accuracy of the predicted objective function, and an application to outlier detection is also provided, showing its advantages over M-estimators.
Giovanangeli, N, Piyathilaka, L, Kodagoda, S, Thiyagarajan, K, Barclay, S & Vitanage, D 2019, 'Design and Development of Drill-Resistance Sensor Technology for Accurately Measuring Microbiologically Corroded Concrete Depths', 36 International Symposium on Automation and Robotics in Construction, International Association for Automation and Robotics in Construction, Canada, pp. 735-735.
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Gracia, L, Solanes, JE, Munoz-Benavent, P, Miro, JV, Perez-Vidal, C & Tornero, J 2018, 'A Sliding Mode Control Architecture for Human-Manipulator Cooperative Surface Treatment Tasks', IEEE International Conference on Intelligent Robots and Systems, IEEE/RSJ International Conference on Intelligent Robots and Systems, Madrid, Spain, pp. 1318-1325.
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© 2018 IEEE. This paper presents a control architecture readily suitable for surface treatment tasks such as polishing, grinding, finishing or deburring as carried out by a human operator, with the added benefit of accuracy, recurrence and physical strength as administered by a robotic manipulator partner. The shared strategy effectively couples the human operator propioceptive abilities and fine skills through his interactions with the autonomous physical agent. The novel proposed control scheme is based on task prioritization and a non-conventional sliding mode control, which is considered to benefit from its inherent robustness and low computational cost. The system relies on two force sensors, one located between the last link of the robot and the surface treatment tool, and the other located in some place of the robot end-effector: the former is used to suitably accomplish the conditioning task, while the latter is used by the operator to manually guide the robotic tool. When the operator chooses to cease guiding the tool, the robot motion safely switches back to an automatic reference tracking. The paper presents the theories for the novel collaborative controller, whilst its effectiveness for robotic surface treatment is substantiated by experimental results using a redundant 7R manipulator and a mock-up conditioning tool.
Gunatilake, A, Piyathilaka, L, Kodagoda, S, Barclay, S & Vitanage, D 2019, 'Real-Time 3D Profiling with RGB-D Mapping in Pipelines UsingStereo Camera Vision and Structured IR Laser Ring', The 14th IEEE Conference on Industrial Electronics and Applications 2019, Xi'an, China.
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This paper is focused on delivering a solution that can scan and reconstruct the 3D profile of a pipeline in real-time using a crawler robot. A structured infrared (IR) laser ring projector and a stereo camera system are used to generate the 3D profile of the pipe as the robot moves inside the pipe. The proposed stereo system does not require field calibrations and it is not affected by the lateral movement of the robot, hence capable of producing an accurate 3D map. The wavelength of the IR light source is chosen to be non overlapping with the visible spectrum of the color camera. Hence RGB color values of the depth can be obtained by projecting the 3D map into the color image frame. The proposed system is implemented in Robotic Operating System (ROS) producing real-time RGB-D maps with defects. The defect map exploit differences in ovality enabling real-time identification of structural defects such as surface corrosion in pipe infrastructure. The lab experiments showed the proposed laser profiling system can detect ovality changes of the pipe with millimeter level of accuracy and resolution.
Hassan, M & Liu, D 2018, 'A Deformable Spiral Based Algorithm to Smooth Coverage Path Planning for Marine Growth Removal', 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), International Conference on Intelligent Robots and Systems, IEEE, Madrid, Spain, pp. 1913-1918.
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Marine growths that flourish on the surfaces of underwater structures, such as bridge pylons, make the inspection and maintenance of these structures challenging. A robotic solution, using an Intervention Autonomous Underwater Vehicle (I-AUV), is developed for removing marine growth. This paper presents a Deformable Spiral Coverage Path Planning (DSCPP) algorithm for marine growth removal. DSCPP generates smooth paths to prevent damage to the surfaces of the structures and to avoid frequent or aggressive decelerations and accelerations due to sharp turns. DSCPP generates a spiral path within a circle and analytically maps the path to a minimum bounding rectangle which encompasses an area of a surface with marine growth. It aims to achieve a spiral path with minimal length while preventing missed areas of coverage. Several case studies are presented to validate the algorithm. Comparison results show that DSCPP outperforms the popular boustrophedon-based coverage approach when considering the requirements for the application under consideration.
Lai, Y, Sutjipto, S, Clout, M, Carmichael, M & Paul, G 2018, 'GAVRe2: Towards Data-driven Upper-Limb Rehabilitation with Adaptive-Feedback Gamification', 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO), IEEE International Conference on Robotics and Biomimetics, IEEE, Kuala Lumpur, Malaysia, pp. 164-169.
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This paper presents Game Adaptive Virtual Reality Rehabilitation (GAVRe2), a framework to augment upper limb rehabilitation using Virtual Reality (VR) gamification and haptic robotic manipulator feedback. GAVRe2 integrates independent systems in a modular fashion, connecting patients with therapists remotely to increase patient engagement during rehabilitation.
GAVRe2 exploits VR capabilities to not only increase the productivity of therapists administering rehabilitation, but also to improve rehabilitation mobility for patients. Conventional rehabilitation requires face-to-face physical interactions in a clinical setting which can be inconvenient for patients. The GAVRe2 approach provides an avenue for rehabilitation in a
domestic setting by remotely customizing a routine for the patient. Results are then reported back to therapists for data analysis and future training regime development.
GAVRe2 is evaluated experimentally through a system that integrates a popular VR system, a RGB-D camera, and a collaborative industrial robot, with results indicating potential benefits for long-term rehabilitation and the opportunity for upper limb rehabilitation in a domestic setting.
Le Gentil, C, Vidal Calleja, T & Huang, S 2019, 'IN2LAMA: INertial Lidar Localisation And MApping', International Conference on Robotics and Automation, Montreal.
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Lu, W & Liu, D 2018, 'A Frequency-Limited Adaptive Controller for Underwater Vehicle-Manipulator Systems Under Large Wave Disturbances', 13th World Congress on Intelligent Control and Automation (WCICA), World Congress on Intelligent Control and Automation, IEEE, Changsha, China.
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Maleki, B, Alempijevic, A & Vidal Calleja, T 2018, 'Continuous Optimization Framework for Depth Sensor Viewpoint Selection', Workshop on the Algorithmic Foundations of Robotics, Merida, Mexico.
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Distinguishing differences between areas represented with point cloud data is generally approached by choosing a optimal viewpoint. The most informative view of a scene ultimately enables to have the optimal coverage over distinct points both locally and globally while accounting for the distance to the foci of attention. Measures of surface saliency, related to curvature inconsistency, extenuate differences in shape and are coupled with viewpoint selection approaches. As there is no analytical solution for optimal viewpoint selection, candidate viewpoints are generally discretely sampled and evaluated for information and require (near) exhaustive combinatorial searches. We present a consolidated optimization framework for optimal viewpoint selection with a continuous cost function and analytically derived Jacobian that incorporates view angle, vertex normals and measures of task related surface information relative to viewpoint. We provide a mechanism in the cost function to incorporate sensor attributes such as operating range, field of view and angular resolution. The framework is evaluated as competing favorably with the state-of-the-art approaches to viewpoint selection while significantly reducing the number of viewpoints to be evaluated in the process.
Munasinghe, MINP, Miles, L & Paul, G 2019, 'Direct-Write Fabrication of Wear Profiling IoT Sensor for 3D Printed Industrial Equipment', 36th International Symposium on Automation and Robotics in Construction (ISARC 2019), Banff, Canada.
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Additive Manufacturing (AM), also known as 3D
printing, is an emerging technology, not only as a
prototyping technology, but also to manufacture
complete products. Gravity Separation Spirals (GSS)
are used in the mining industry to separate slurry
into different density components. Currently, spirals
are manufactured using moulded polyurethane on
fibreglass substructure, or injection moulding. These
methods incur significant tooling cost and lead times
making them difficult to customise, and they are
labour-intensive and can expose workers to
hazardous materials. Thus, a 3D printer is under
development that can print spirals directly, enabling
mass customisation. Furthermore, sensors can be
embedded into spirals to measure the operational
conditions for predictive maintenance, and to collect
data that can improve future manufacturing
processes. The localisation of abrasive wear in the
GSS is an essential factor in optimising parameters
such as suitable material, print thickness, and infill
density and thus extend the lifetime and
performance of future manufactured spirals. This
paper presents the details of a wear sensor, which
can be 3D printed directly into the spiral using
conductive material. Experimental results show that
the sensor can both measure the amount of wear and
identify the location of the wear in both the
horizontal and vertical axes. Additionally, it is shown
that the accuracy can be adjusted according to the
requirements by changing the number and spacing
of wear lines.
Nguyen, L & Valls Miro, J 2019, 'Acoustic Sensor Networks and Mobile Robotics for Sound Source Localization', IEEE International Conference on Control & Automation, IEEE International Conference on Control & Automation, Edinburgh, UK.
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Localizing a sound source is a fundamental but still challenging issue in many applications, where sound information is gathered by static and local microphone sensors. Therefore, this work proposes a new system by exploiting advances in sensor networks and robotics to more accurately address the problem of sound source localization. By the use of the network infrastructure, acoustic sensors are more efficient to spatially monitor acoustical phenomena. Furthermore, a mobile robot is proposed to carry an extra microphone array in order to collect more acoustic signals when it travels around the environment. Driving the robot is guided by the need to increase the quality of the data gathered by the static acoustic sensors, which leads to better probabilistic fusion of all the information gained, so that an increasingly accurate map of the sound source can be built. The proposed system has been validated in a real-life environment, where the obtained results are highly promising.
Nguyen, L, Valls Miro, J & Qiu, X 2019, 'Can a Robot Hear the Shape and Dimensions of a Room?', IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019), IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019), IEEE, Macau, China.
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Knowing the geometry of a space is desirable for many applications, e.g. sound source localization, sound field reproduction or auralization. In circumstances where only acoustic signals can be obtained, estimating the geometry of a room is a challenging proposition. Existing methods have been proposed to reconstruct a room from the room impulse responses (RIRs). However, the sound source and microphones must be deployed in a feasible region of the room for it to work, which is impractical when the room is unknown. This work propose to employ a robot equipped with a sound source and four acoustic sensors, to follow a proposed path planning strategy to moves around the room to collect first image sources for room geometry estimation. The strategy can effectively drives the robot from a random initial location through the room so that the room geometry is guaranteed to be revealed. Effectiveness of the proposed approach is extensively validated in a synthetic environment, where the results obtained are highly promising.
Sutjipto, S, Tish, D, Paul, G, Vidal Calleja, T & Schork, T 2018, 'Towards Visual Feedback Loops for Robot-Controlled Additive Manufacturing', Robotic Fabrication in Architecture, Art and Design 2018, ROBARCH 2018: Robotic Fabrication in Architecture, Art and Design 2018, Springer, Cham, Zurich, pp. 85-97.
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Robotic additive manufacturing methods have enabled the design and fabrication of novel forms and material systems that represent an important step forward for architectural fabrication. However, a common problem in additive manufacturing is to predict and incorporate the dynamic behavior of the material that is the result of the complex confluence of forces and material properties that occur during fabrication. While there have been some approaches towards verification systems, to date most robotic additive manufacturing processes lack verification to ensure deposition accuracy. Inaccuracies, or in some instances critical errors, can occur due to robot dynamics, material self-deflection, material coiling, or timing shifts in the case of multi-material prints. This paper addresses that gap by presenting an approach that uses vision-based sensing systems to assist robotic additive manufacturing processes. Using online image analysis techniques, occupancy maps can be created and updated during the fabrication process to document the actual position of the previously deposited material. This development is an intermediary step towards closed-loop robotic control systems that combine workspace sensing capabilities with decision-making algorithms to adjust toolpaths to correct for errors or inaccuracies if necessary. The occupancy grid map provides a complete representation of the print that can be analyzed to determine various key aspects, such as, print quality, extrusion diameter, adhesion between printed parts, and intersections within the meshes. This valuable quantitative information regarding system robustness can be used to influence the system’s future actions. This approach will help ensure consistent print quality and sound tectonics in robotic additive manufacturing processes, improving on current techniques and extending the possibilities of robotic fabrication in architecture.
Wickramanayake, S, Thiyagarajan, K, Kodagoda, S & Piyathilaka, L 2019, 'Frequency Sweep Based Sensing Technology for Non-destructive Electrical Resistivity Measurement of Concrete', 36 International Symposium on Automation and Robotics in Construction, International Association for Automation and Robotics in Construction, Canada, pp. 1290-1290.
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