Arora, A, Furlong, PM, Fitch, R, Sukkarieh, S & Fong, T 2019, 'Multi-modal active perception for information gathering in science missions', Autonomous Robots, vol. 43, no. 7, pp. 1827-1853.
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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.
Chen, Y, Huang, S & Fitch, R 2019, 'Active SLAM for Mobile Robots with Area Coverage and Obstacle Avoidance', IEEE/ASME Transactions on Mechatronics.
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IEEE We present an active simultaneous localization and mapping~(SLAM) framework for a mobile robot to obtain a collision-free trajectory with good performance in SLAM uncertainty reduction and in an area coverage task. Based on a model predictive control~(MPC) framework, these two tasks are solved by the introduction of a control switching mechanism. For SLAM uncertainty reduction, graph topology is used to approximate the original problem as a constrained non-linear least-squares problem. A convex half-space representation is applied to relax non-convex spatial constraints that represent obstacle avoidance. Using convex relaxation, the problem is solved by a convex optimization method and a rounding procedure based on singular value decomposition (SVD). The area coverage task is addressed with a sequential quadratic programming (SQP) method. A submap joining approach, called Linear SLAM, is used to address the associated challenges of avoiding local minima, minimizing control switching, and potentially high computational cost. Finally, various simulations and experiments using an aerial robot are presented that verify the effectiveness of the proposed method, showing that our method produces a more accurate SLAM result and is more computationally efficient compared with multiple existing methods.
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, vol. 43, no. 5, pp. 1225-1240.
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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.
Ding, X, Wang, Y, Xiong, R, Li, D, Tang, L, Yin, H & Zhao, L 2019, 'Persistent Stereo Visual Localization on Cross-Modal Invariant Map', IEEE Transactions on Intelligent Transportation Systems, pp. 1-13.
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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.
Gracia, L, Solanes, JE, Muñoz-Benavent, P, Valls Miro, J, Perez-Vidal, C & Tornero, J 2019, 'Human-robot collaboration for surface treatment tasks', Interaction Studies, vol. 20, no. 1, pp. 148-184.
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© John Benjamins Publishing Company. This paper presents a human-robot closely collaborative solution to cooperatively perform surface treatment tasks such as polishing, grinding, finishing, deburring, etc. The proposed scheme is based on task priority and nonconventional sliding mode control. Furthermore, the proposal includes two force sensors attached to the manipulator end-effector and tool: one sensor is used to properly accomplish the surface treatment task, while the second one is used by the operator to guide the robot tool. The applicability and feasibility of the proposed collaborative solution for robotic surface treatment are substantiated by experimental results using a redundant 7R manipulator: the Sawyer collaborative robot.
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: theory and applications, vol. 95, pp. 915-933.
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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.
Jadidi, MG, Miro, JV & 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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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, vol. 16, pp. 614-627.
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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, vol. 5, no. 3, pp. 191-208.
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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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Lu, W & Liu, D 2019, 'A Scalable Sampling-Based Optimal Path Planning Approach via Search Space Reduction', IEEE Access, vol. 7, pp. 153921-153935.
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Nguyen, K-D & Liu, D 2019, 'Gibbon-inspired Robust Asymmetric Brachiation along an Upward Slope', INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, vol. 17, no. 10, pp. 2647-2654.
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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, TV, Thai, NH, Pham, HT, Phan, TA, Nguyen, L, Le, HX & Nguyen, HD 2019, 'Adaptive Neural Network-Based Backstepping Sliding Mode Control Approach for Dual-Arm Robots', Journal of Control, Automation and Electrical Systems, vol. 30, no. 4, pp. 512-521.
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Piyathilaka, L & Kodagoda, S 2019, 'Understanding Human Context in 3D Scenes by Learning Spatial Affordances with Virtual Skeleton Models.', CoRR, vol. abs/1906.05498.
Reid, W, Fitch, R, Goktogan, AH & Sukkarieh, S 2019, 'Sampling-based hierarchical motion planning for a reconfigurable wheel-on-leg planetary analogue exploration rover', JOURNAL OF FIELD ROBOTICS.
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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 & 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 & Aslani, F 2019, 'Heat Curing as a Means of Post-processing Influence on 3D Printed Mortar Specimens in Powder-based 3D Printing', Indian Concrete Journal, vol. 93, no. 09, pp. 65-74.
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Inkjet (Powder-based) three-dimensional printing (3DP) shows significant promise in concrete construction applications. The accuracy, speed, and capacity to build complicated geometries are the most beneficial features of inkjet 3DP. Therefore, inkjet 3DP needs to be carefully studied and evaluated with construction goals in mind and employed in real-world applications, where it is most appropriate. This paper focuses on the important aspect of curing 3DP specimens. It discusses the enhanced mechanical properties of the mortar that are unlocked through a heat-curing process. Experiments were conducted on cubic mortar specimens that were printed and cured in an oven at a range of different temperatures (40, 60, 80, 90, 100°C). The results of the experimental tests showed that 80°C is the optimum heat-curing temperature to achieve the highest compressive strength and flexural strength of the printed mortar specimens. These tests were performed on two different dimensions of the cubic specimens, namely, 20x20x20 mm, 50x50x50 mm and on prism specimens with dimensions of 160x40x40 mm. The inkjet 3DP process and the post-processing curing are discussed. In addition, 3D scanning of the printed specimens was employed and the surface roughness profiles of the 3DP gypsum specimens and cement mortar are recorded 13.76 µm and 22.31µm, respectively.
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, vol. 96, pp. 3-16.
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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.
Woolfrey, J, Lu, W, Vidal-Calleja, T & Liu, D 2019, 'Clarifying clairvoyance: Analysis of forecasting models for near-sinusoidal periodic motion as applied to AUVs in shallow bathymetry', Ocean Engineering, vol. 190.
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© 2019 Elsevier Ltd This paper shows that Gaussian Process Regression (GPR) with a periodic kernel has better mean prediction accuracy and uncertainty bounds than time series or Fourier series when forecasting motion data of underwater vehicles subject to wave excitation. Many robotic systems, such as autonomous underwater vehicles (AUVs), are required to operate in environments with disturbances and relative motion that make task performance difficult. This motion often exhibits periodic, near-sinusoidal behaviour. By predicting this motion, control strategies can be developed to improve accuracy. Moreover, factoring in uncertainty can aid the robustness of these predictive control methods. Time series and Fourier series have been applied to several predictive control problems in a variety of fields. However, there are contradictory results in performance based on parameters, assessment criteria, and application. This paper seeks to clarify these discrepancies using AUV motion as a case study. GPR is also introduced as a third candidate for prediction based on previous applications to time series forecasting in other fields of science. In addition to assessing mean prediction accuracy, the ability of each model to adequately bound prediction error is also considered as a key performance indicator.
Yang, Z, Yu, C, Kim, J, Li, Z & Wang, L 2019, 'Evolution of cooperation in synergistically evolving dynamic interdependent networks: Fundamental advantages of coordinated network evolution', New Journal of Physics, vol. 21, no. 7.
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© 2019 The Author(s). Published by IOP Publishing Ltd on behalf of the Institute of Physics and Deutsche Physikalische Gesellschaft. Real networks are not only multi-layered yet also dynamic. The role of coordinated network evolution regarding dynamic multi-layer networks where both network and strategy evolution simultaneously show diverse interdependence by layers remains poorly addressed. Here, we propose a general and simple coevolution framework to analyze how coordination of different dynamical processes affects strategy propagation in synergistically evolving interdependent networks. The strategic feedback constitutes the main driving force of network evolution yet the inherent cross-layer self-optimization functions as its compensation. We show that these two ingredients often catalyze a better performance of network evolution in propagating cooperation. Coordinated network evolution may be a double-edged sword to cooperation and the network-Adapting rate plays a crucial role in flipping its double-sided effect. It often economizes the cost and time consumption for driving the system to the full cooperation phase. Importantly, strongly coupled slow-Tuned networks can outperform weakly coupled fast-regulated networks in solving social dilemmas, highlighting the fundamental advantages of coordinated network evolution and the importance of synergistic effect of dynamical processes in upholding human cooperation in multiplex networks.
Yu, H, Lu, W, Liu, D, Han, Y & Wu, Q 2019, 'Speeding up Gaussian Belief Space Planning for Underwater Robots Through a Covariance Upper Bound', IEEE ACCESS, vol. 7, pp. 121961-121974.
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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, J, Huang, S, Zhao, L, Chen, Y & Luo, X 2019, 'Conic Feature Based Simultaneous Localization and Mapping in Open Environment via 2D Lidar', IEEE Access, vol. 7, pp. 173703-173718.
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© 2013 IEEE. The conventional planar scan matching approach cannot cope well with the open environment as lacking of sufficient edges and corners. This paper presents a conic feature based simultaneous localization and mapping (SLAM) algorithm via 2D lidar which can adapt to an open environment nicely. The novelty of this work includes threefold: (1) defining a conic feature based parametrization approach; (2) developing a method to utilize feature's conic geometric information and odometry information since open environments are short of regular linear geometric features; (3) developing a factor graph based framework which can be adapted with the proposed parametrization. Simulation experiments and real environment experiments demonstrated that the proposed SLAM algorithm can get accurate and convincing results for the open environment and the map in our representation can express accurately the environment situation.
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', https://ieeexplore.ieee.org/xpl/conhome/8780387/proceeding, International Conference on Robotics and Automation, IEEE, Canada, pp. 6137-6143.
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Physical Human-Robot Collaboration (pHRC) is about the interaction between one or more human operator(s) and one or more robot(s) in direct contact and voluntarily exchanging forces to accomplish a common task. In any pHRC, the intuitiveness of the interaction has always been a priority, so that the operator can comfortably and safely interact with the robot. So far, the intuitiveness has always been described in a qualitative way. In this paper, we suggest an objective way to evaluate intuitiveness, known as prediction error negativity (PEN) using electroencephalogram (EEG). PEN is defined as a negative deflection in event related potential (ERP) due to cognitive conflict, as a consequence of a mismatch between perception and reality. Experimental results showed that the forces exchanged between robot and human during pHRC modulate the amplitude of PEN, representing different levels of cognitive conflict. We also found that PEN amplitude significantly decreases (p <; 0.05) when a mechanical resistance is being applied smoothly and more time in advance before an invisible obstacle, when compared to a scenario in which the resistance is applied abruptly before the obstacle. These results indicate that an earlier and smoother resistance reduces the conflict level. Consequently, this suggests that smoother changes in resistance make the interaction more intuitive.
Aldini, S, Carmichael, MG & Liu, D 2019, 'A Risk Reduction Framework for Design of Physical Human-Robot Collaboration', https://ssl.linklings.net/conferences/acra/acra2019_proceedings/views/by_auth.html, Australasian Conference on Robotics and Automation, Adelaide.
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As robots designed to physically interact with humans become common in various application areas, shared workspaces and force exchange between human and robot lead to new challenges in terms of safety.
Often, a variety of safety techniques is necessary, and deciding what methods to include in a comprehensive safety framework is not an easy task. This paper is concerned with the design of robotic co-wokers that involve physical Human-Robot Collaboration (pHRC), with humans and robots in continuous direct physical contact and exchanging forces.
A hierarchical risk reduction framework is presented for guiding the design of robotic co-workers to reduce the risk associated with hazards commonly found in pHRC tasks. A case study is presented to demonstrate the use of the framework in designing an Assistance-as-Needed roBOT (ANBOT) which has been extensively tested in practical industry applications.
Arukgoda, J, Ranasinghe, R & Dissanayake, G 2019, 'Representation of uncertain occupancy maps with high level feature vectors', IEEE International Conference on Automation Science and Engineering, pp. 1035-1041.
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© 2019 IEEE. This paper presents a novel method for representing an uncertain occupancy map using a 'feature vector' and an associated covariance matrix. Input required is a point cloud generated using observations from a sensor captured at different locations in the environment. Both the sensor locations and the measurements themselves may have an associated uncertainty. The output is a set of coefficients and their uncertainties of a cubic spline approximation to the distance function of the environment, thereby resulting in a compact parametric representation of the environment geometry. Cubic spline coefficients are computed by solving a non-linear least squares problem that enforces the Eikonal equation over the space in which the environment geometry is defined, and zero boundary condition at each observation in the point cloud. It is argued that a feature based representation of point cloud maps acquired from uncertain locations using noisy sensors has the potential to open up a new direction in robot mapping, localisation and SLAM. Numerical examples are presented to illustrate the proposed technique.
Arukgoda, J, Ranasinghe, R & Dissanayake, G 2019, 'Robot localisation in 3D environments using sparse range measurements', IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM, pp. 551-558.
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© 2019 IEEE. This paper presents an algorithm for mobile robot localisation given a map of a 3D environment and a sparse set of range-bearing measurements. The environment is represented using a spline approximation of its vector distance function (VDF). For a given location in the environment, VDF encodes the distance to the nearest occupied region along three orthogonal axes. VDF is first obtained from an occupancy voxel map and its three components are then approximated in the least-square sense using a set of three dimensional cubic b-splines, providing a rich and continuous representation of the environment. First and second order derivatives of the VDF are also computed and stored. The difference between an observed range measurement in a given direction and its expected value is formulated as a function of the robot location and the spline coefficients representing the VDF. This leads to a non-linear least-squares optimization problem that can be solved to localise the robot given a set of such measurements. It is demonstrated that a sparse set of range-bearing measurements, an order of magnitude smaller than what is typically available from 3D range sensor is adequate to achieve accurate localisation. The algorithm presented is illustrated using a number of examples including a single point range sensor mounted on a pan-tilt head to localise a robot moving in an indoor environment.
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.
Chen, Y, Huang, S, Fitch, R, Zhao, L, Yu, H & Yang, D 2019, 'On-line 3D active pose-graph SLAM based on key poses using graph topology and sub-maps', Proceedings - IEEE International Conference on Robotics and Automation, pp. 169-175.
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© 2019 IEEE. In this paper, we present an on-line active pose-graph simultaneous localization and mapping (SLAM) frame-work for robots in three-dimensional (3D) environments using graph topology and sub-maps. This framework aims to find the best trajectory for loop-closure by re-visiting old poses based on the T-optimality and D-optimality metrics of the Fisher information matrix (FIM) in pose-graph SLAM. In order to reduce computational complexity, graph topologies are introduced, including weighted node degree (T-optimality metric) and weighted tree-connectivity (D-optimality metric), to choose a candidate trajectory and several key poses. With the help of the key poses, a sampling-based path planning method and a continuous-time trajectory optimization method are combined hierarchically and applied in the whole framework. So as to further improve the real-time capability of the method, the sub-map joining method is used in the estimation and planning process for large-scale active SLAM problems. In simulations and experiments, we validate our approach by comparing against existing methods, and we demonstrate the on-line planning part using a quad-rotor unmanned aerial vehicle (UAV).
Fryc, S, Liu, L & Vidal Calleja, T 2019, 'Efficient Pipeline for Mobile Brick Picking', https://ssl.linklings.net/conferences/acra/acra2019_proceedings/views/by_auth.html, Australasian Conference on Robotics and Automation, ARAA, Adelaide, pp. 1-8.
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Autonomous mobile manipulation is gaining more and more attention for a range of application including disaster response, logistics, manufacturing and construction because removes work space limitation and allows object handling. A key challenge in mobile manipulation is the interaction between motion planning and perception that will deliver stable and efficient solutions. In this work, we are interested in the problem of picking a single brick shaped object from an unstructured pile using a mo- bile manipulator and a 3D camera system. We propose a robust multi-stage pipeline for a efficient, collision-free brick picking given the object pose. The key contribution of this work is a scoring function used to find the most suitable configuration considering the integrated kinematic chain of mobile base and manipulator arm. Realistic simulation results show the proposed pipeline has 100% success rate as opposed to a standard off-the-shelf solution, which has high-failure rates.
Galea, M & Vidal Calleja, T 2019, 'Point Cloud Edge Detection and Template Matching with 1D Gradient Descent for Wall Pose Estimation', https://ssl.linklings.net/conferences/acra/acra2019_proceedings/views/by_auth.html, Australasian Conference on Robotics and Automation, Adelaide, Australia.
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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', 36th 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', 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA), IEEE Conference on Industrial Electronics and Applications, IEEE, Xi'an, China, pp. 916-921.
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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.
Hadgraft, R, Francis, B, Brown, T, Fitch, R & Halkon, B 2019, 'Renewing Mechanical and Mechatronics Programs', AAEE2019, Brisbane, Australia.
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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.
Hyun, JS, Carmichael, MG, Tran, A, Zhang, S & Liu, D 2019, 'Evaluation of fast, high-detail projected light 3D sensing for robots in construction', Proceedings of the 14th IEEE Conference on Industrial Electronics and Applications, ICIEA 2019, pp. 1262-1267.
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© 2019 IEEE. Robots used on-site in construction need to perceive the surrounding environment to operate autonomously. This is challenging as the construction environment is often less than ideal due to changing lighting conditions, turbid air, and the need to detect fine details. In this work we evaluate a custom made projected light 3D sensor system for suitability and practicality in enabling autonomous robotics for construction. A series of tests are performed to evaluate the sensor based on ability to capture environmental details, operate robustly in challenging lighting conditions, and make accurate geometric measurements. Analysis shows that high fidelity measurements with accuracy in the order of millimeters can be obtained, making the technology a promising solution for robots operating in construction environments.
Jayasuriya, M, Dissanayake, G, Ranasinghe, R & Gandhi, N 2019, 'Leveraging Deep Learning Based Object Detection for Localising Autonomous Personal Mobility Devices in Sparse Maps', 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019, pp. 4081-4086.
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© 2019 IEEE. This paper presents a low cost, resource efficient localisation approach for autonomous driving in GPS denied environments. One of the most challenging aspects of traditional landmark based localisation in the context of autonomous driving, is the necessity to accurately and frequently detect landmarks. We leverage the state of the art deep learning framework, YOLO (You Only Look Once), to carry out this important perceptual task using data obtained from monocular cameras. Extracted bearing only information from the YOLO framework, and vehicle odometry, is fused using an Extended Kalman Filter (EKF) to generate an estimate of the location of the autonomous vehicle, together with it's associated uncertainty. This approach enables us to achieve real-time sub metre localisation accuracy, using only a sparse map of an outdoor urban environment. The broader motivation of this research is to improve the safety and reliability of Personal Mobility Devices (PMDs) through autonomous technology. Thus, all the ideas presented here are demonstrated using an instrumented mobility scooter platform.
Kiss, SH, To, KYC, Yoo, C, Fitch, R & Alempijevic, A 2019, 'Minimally Invasive Social Navigation', Australasian Conference on Robotics and Automation 2019, Australasian Conference on Robotics and Automation, ARAA, Adelaide, Australia, pp. 1-7.
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Integrating mobile robots into human society involves the fundamental problem of navigation in crowds. This problem has been studied by considering the behaviour of humans at the level of individuals, but this representation limits the computational efficiency of motion planning algorithms. We explore the idea of representing a crowd as a flow field, and propose a formal definition of path quality based on the concept of invasiveness; a robot should attempt to navigate in a way that is minimally invasive to humans in its environment. We develop an algorithmic framework for path planning based on this definition and present experimental results that indicate its effectiveness. These results open new algorithmic questions motivated by the flow field representation of crowds and are a necessary step on the path to end-to-end implementations.
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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Lee, KMB, Yoo, C, Hollings, B, Anstee, S, Huang, S & Fitch, R 2019, 'Online estimation of ocean current from sparse GPS data for underwater vehicles', Proceedings - IEEE International Conference on Robotics and Automation, pp. 3443-3449.
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© 2019 IEEE. Underwater robots are subject to position drift due to the effect of ocean currents and the lack of accurate localisation while submerged. We are interested in exploiting such position drift to estimate the ocean current in the surrounding area, thereby assisting navigation and planning. We present a Gaussian process (GP)-based expectation-maximisation (EM) algorithm that estimates the underlying ocean current using sparse GPS data obtained on the surface and dead-reckoned position estimates. We first develop a specialised GP regression scheme that exploits the incompressibility of ocean currents to counteract the underdetermined nature of the problem. We then use the proposed regression scheme in an EM algorithm that estimates the best-fitting ocean current in between each GPS fix. The proposed algorithm is validated in simulation and on a real dataset, and is shown to be capable of reconstructing the underlying ocean current field. We expect to use this algorithm to close the loop between planning and estimation for underwater navigation in unknown ocean currents.
Li, Z, Kim, J & Yu, C 2019, 'Control Design and Analysis of an Epidemic SEIV- Model upon Adaptive Network', European Control Conference, Italy.
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Li, Z, Kim, J & Yu, C 2019, 'Control Design and Stability Analysis of a Two-Infectious-State Awareness Epidemic Model', Asian Control Conference (ASCC), Japan.
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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', Proceedings of the 36th International Symposium on Automation and Robotics in Construction (ISARC 2019), International Symposium on Automation and Robotics in Construction, IAARC, Banff, Canada, pp. 862-869.
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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, Miro, JV, Shi, L & Vidal-Calleja, T 2019, 'Gaussian Mixture Marginal Distributions for Modelling Remaining Pipe Wall Thickness of Critical Water Mains in Non-Destructive Evaluation', IEEE International Conference on Cybernetics and Intelligent Systems, and Robotics, Automation and Mechatronics, Bangkok, Thailand.
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Rapidly estimating the remaining wall thickness (RWT) is paramount for the non-destructive condition assessment evaluation of large critical metallic pipelines. A robotic vehicle with embedded magnetism-based sensors has been developed to traverse the inside of a pipeline and conduct inspections at the location of a break. However its sensing speed is constrained by the magnetic principle of operation, thus slowing down the overall operation in seeking dense RWT mapping. To ameliorate this drawback, this work proposes the partial scanning of the pipe and then employing Gaussian Processes (GPs) to infer RWT at the unseen pipe sections. Since GP prediction assumes to have normally distributed input data - which does correspond with real RWT measurements - Gaussian mixture (GM) models are proven in this work as fitting marginal distributions to effectively capture the probability of any RWT value in the inspected data. The effectiveness of the proposed approach is extensively validated from real-world data collected in collaboration with a water utility from a cast iron water main pipeline in Sydney, Australia.
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.
Piyathilaka, L, Sooriyaarachchi, B, Kodagoda, S & Thiyagarajan, K 2019, 'Capacitive Sensor Based 2D Subsurface Imaging Technology for Non Destructive Evaluation of Building Surfaces.', CoRR.
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Shakor, P, Nejadi, S & Paul, G 2019, 'An Investigation into the Effects of Deposition Orientation of Material on the Mechanical Behaviours of the Cementitious Powder and Gypsum Powder in Inkjet 3D Printing', Proceedings of the 36th International Symposium on Automation and Robotics in Construction (ISARC), 36th International Symposium on Automation and Robotics in Construction, International Association for Automation and Robotics in Construction (IAARC), Banff, AB, Canada.
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Shakor, P, Nejadi, S & Paul, G 2019, 'Effect of Elevated Temperatures as a Means of Curing in Inkjet 3D Printed Mortar Specimens', 29th Biennial National Conference of the Concrete Institute of Australia, Concrete Institute of Australia, Sydney, Australia.
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Sukkar, F, Best, G, Yoo, C & Fitch, R 2019, 'Multi-robot region-of-interest reconstruction with Dec-MCTS', Proceedings - IEEE International Conference on Robotics and Automation, International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada, Canada, pp. 9101-9107.
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© 2019 IEEE. We consider the problem of reconstructing regions of interest of a scene using multiple robot arms and RGB-D sensors. This problem is motivated by a variety of applications, such as precision agriculture and infrastructure inspection. A viewpoint evaluation function is presented that exploits predicted observations and the geometry of the scene. A recently proposed non-myopic planning algorithm, Decentralised Monte Carlo tree search, is used to coordinate the actions of the robot arms. Motion planning is performed over a navigation graph that considers the high-dimensional configuration space of the robot arms. Extensive simulated experiments are carried out using real sensor data and then validated on hardware with two robot arms. Our proposed targeted information gain planner is compared to state-of-the-art baselines and outperforms them in every measured metric. The robots quickly observe and accurately detect fruit in a trellis structure, demonstrating the viability of the approach for real-world applications.
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, Robotic Fabrication in Architecture, Art and Design, Springer, 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.
To, KYC, Lee, KMB, Yoo, C, Anstee, S & Fitch, R 2019, 'Streamlines for motion planning in underwater currents', Proceedings - IEEE International Conference on Robotics and Automation, International Conference on Robotics and Automation, Montreal, QC, Canada, pp. 4619-4625.
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© 2019 IEEE. Motion planning for underwater vehicles must consider the effect of ocean currents. We present an efficient method to compute reachability and cost between sample points in sampling-based motion planning that supports long-range planning over hundreds of kilometres in complicated flows. The idea is to search a reduced space of control inputs that consists of stream functions whose level sets, or streamlines, optimally connect two given points. Such stream functions are generated by superimposing a control input onto the underlying current flow. A streamline represents the resulting path that a vehicle would follow as it is carried along by the current given that control input. We provide rigorous analysis that shows how our method avoids exhaustive search of the control space, and demonstrate simulated examples in complicated flows including a traversal along the east coast of Australia, using actual current predictions, between Sydney and Brisbane.
Ulapane, N, Piyathilaka, L & Kodagoda, S 2019, 'Some convolution and scale transformation techniques to enhance GPR images', Proceedings of the 14th IEEE Conference on Industrial Electronics and Applications, ICIEA 2019, pp. 1453-1458.
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© 2019 IEEE. Locating reinforcement rods embedded inside concrete wall-like structures, as well as locating subsurface features such as voids, cracks, and interfaces is an essential part of structural health monitoring of concrete infrastructure. The Ground Penetrating Radar (GPR) technique has been commonly used as a means of Non-destructive Testing and Evaluation (NDT E) which suits the purpose. In the recent past, the interest of using GPR to assess the crowns (i.e., top) of concrete sewers has been rising. Moisture is well known to be a challenge for GPR imaging as moisture tends to influence GPR waves. This challenge becomes more common and persistent inside sewers since sewer walls contain considerable surface and subsurface moisture as a result of the humid environment created by the waste water flowing through sewers as well as the bacteria and gas induced acid attacks. Forming a part of sewer condition assessment-related research with the objective of assessing moist concrete, this paper presents some preliminary results which demonstrate how some simple scale transformations and convolution can help in enhancing GPR images in grey-scale. A set of raw GPR signals captured on a moist concrete block inside a laboratory environment is considered. The effect of enhancement is demonstrated against a benchmark image constructed by mapping the raw signals directly onto grey-scale.
Vu, TL, Liu, L, Paul, G & Vidal Calleja, T 2019, 'Rectangular-shaped object recognition and pose estimation', Australian Conference on Robotics and Automation, Adelaide.
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Wickramanayake, S, Thiyagarajan, K, Kodagoda, S & Piyathilaka, L 2019, 'Frequency Sweep Based Sensing Technology for Non-destructive Electrical Resistivity Measurement of Concrete', 36th International Symposium on Automation and Robotics in Construction, International Association for Automation and Robotics in Construction, Canada, pp. 1290-1290.
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Yu, H, Lu, W & Liu, D 2019, 'A Unified Closed-Loop Motion Planning Approach For An I-AUV In Cluttered Environment With Localization Uncertainty', 2019 International Conference on Robotics and Automation (ICRA), 2019 International Conference on Robotics and Automation (ICRA), IEEE, Montreal, CA.
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Zhu, H, Leighton, B, Chen, Y, Ke, X, Liu, S & Zhao, L 2019, 'Indoor Navigation System Using the Fetch Robot', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 686-696.
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© 2019, Springer Nature Switzerland AG. In this paper, we present a navigation system, including off-line mapping and on-line localization, for the Fetch robot in an indoor environment using Cartographer. This framework aims to build a practical, robust, and accurate Robot Operating System (ROS) package for the Fetch robot. Firstly, using Cartographer and the fusion of data from a laser scan and RGB-D camera, a two-dimensional (2D) off-line map is built. Then, the Adaptive Monte Carlo Localization (AMCL) ROS package is used to perform on-line localization. We use a simulation to validate this method of mapping and localization, then demonstrate our method live on the Fetch robot. A video about the simulation and experiment is shown in https://youtu.be/oOvxTOowe34.