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.
View/Download from: Publisher's site
View description>>
© 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, pp. 1-1.
View/Download from: Publisher's site
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.
View/Download from: UTS OPUS or Publisher's site
View description>>
© 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.
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.
View/Download from: UTS OPUS or Publisher's site
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.
View/Download from: UTS OPUS or Publisher's site
View description>>
© 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.
View/Download from: UTS OPUS or Publisher's site
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.
View/Download from: UTS OPUS or Publisher's site
View description>>
© 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.
View/Download from: UTS OPUS
Nguyen, L, Valls Miro, J & Qiu, X 2019, 'Multilevel B-Splines based Learning Approach for Sound Source Localization', IEEE Sensors Journal.
View/Download from: UTS OPUS
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.
View/Download from: UTS OPUS or Publisher's site
Shakor, P, Nejadi, S, Paul, G, Sanjayan, J & Nazari, A 2019, 'MECHANICAL PROPERTIES OF THE CEMENT-BASED MATERIALS AND EFFECT OF ELEVATED TEMPERATURE ON 3D PRINTING MORTAR SPECIMENS IN INKJET 3D PRINTING', ACI Materials Journal.
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.
View/Download from: Publisher's site
View description>>
© 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.
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.
View/Download from: UTS OPUS or Publisher's site
View description>>
© 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.
Zhao, L, Huang, S & Dissanayake, G 2019, 'Linear SLAM: Linearising the SLAM problems using submap joining', Automatica, vol. 100, pp. 231-246.
View/Download from: UTS OPUS or Publisher's site
View description>>
© 2018 Elsevier Ltd The main contribution of this paper is a new submap joining based approach for solving large-scale Simultaneous Localization and Mapping (SLAM) problems. Each local submap is independently built using the local information through solving a small-scale SLAM; the joining of submaps mainly involves solving linear least squares and performing nonlinear coordinate transformations. Through approximating the local submap information as the state estimate and its corresponding information matrix, judiciously selecting the submap coordinate frames, and approximating the joining of a large number of submaps by joining only two maps at a time, either sequentially or in a more efficient Divide and Conquer manner, the nonlinear optimization process involved in most of the existing submap joining approaches is avoided. Thus the proposed submap joining algorithm does not require initial guess or iterations since linear least squares problems have closed-form solutions. The proposed Linear SLAM technique is applicable to feature-based SLAM, pose graph SLAM and D-SLAM, in both two and three dimensions, and does not require any assumption on the character of the covariance matrices. Simulations and experiments are performed to evaluate the proposed Linear SLAM algorithm. Results using publicly available datasets in 2D and 3D show that Linear SLAM produces results that are very close to the best solutions that can be obtained using full nonlinear optimization algorithm started from an accurate initial guess. The C/C++ and MATLAB source codes of Linear SLAM are available on OpenSLAM.
Le Gentil, C, Vidal Calleja, T & Huang, S 2019, 'IN2LAMA: INertial Lidar Localisation And MApping', International Conference on Robotics and Automation, Montreal.
View/Download from: UTS OPUS
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.
View/Download from: UTS OPUS
View description>>
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.
Nguyen, L & Valls Miro, J 2019, 'Acoustic Sensor Networks and Mobile Robotics for Sound Source Localization', IEEE International Conference on Control & Automation, Edinburgh, UK.
View/Download from: UTS OPUS