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Associate Professor Shoudong Huang

Image of Shoudong Huang
Associate Professor, School of Elec, Mech and Mechatronic Systems
Core Member, Research Strength Centre for Autonomous Systems
Core Member, GEVI Research Strength
B. Sc. (NEU), M. Sc. (NEU), PhD
Member, Institution of Electrical and Electronic Engineers
 
Phone
+61 2 9514 2964
Room
CB11.09.305
Can supervise: Yes

Book Chapters

Wang, Z., Huang, S. & Dissanayake, G. 2008, 'Tradeoffs in SLAM with sparse information filters' in Christian Laugier, Roland Siegwart (eds), Field and Service Robotics, Springer, Berlin Heidelberg, pp. 339-348.
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Designing filters exploiting the sparseness of the information matrix for efficiently solving the simultaneous localization and mapping (SLAM) problem has attracted significant attention during the recent past. The main contribution of this paper is a review of the various sparse information filters proposed in the literature to date, in particular, the compromises used to achieve sparseness. Two of the most recent algorithms that the authors have implemented, Exactly Sparse Extended Information Filter (ESEIF) by Walter et al. [5] and the D-SLAM by Wang et al. [6] are discussed and analyzed in detail. It is proposed that this analysis can stimulate developing a framework suitable for evaluating the relative merits of SLAM algorithms.
Wang, Z., Huang, S. & Dissanayake, G. 2006, 'Implementation Issues and Experimental Evaluation of D-SLAM' in Peter Corke, Salah Sukkarieh` (eds), Field and Service Robotics, Springer-Verlag, Berlin, Heidelberg, pp. 155-166.
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Books

Wang, Z., Huang, S. & Dissanayake, G. 2011, Simultaneous Localization and Mapping: Exactly Sparse Information Filters, 1, World Scientific, Singapore.
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Simultaneous localization and mapping (SLAM) is a process where an autonomous vehicle builds a map of an unknown environment while concurrently generating an estimate for its location. This book is concerned with computationally efficient solutions to the large scale SLAM problems using exactly sparse Extended Information Filters (EIF). The invaluable book also provides a comprehensive theoretical analysis of the properties of the information matrix in EIF-based algorithms for SLAM. Three exactly sparse information filters for SLAM are described in detail, together with two efficient and exact methods for recovering the state vector and the covariance matrix. Proposed algorithms are extensively evaluated both in simulation and through experiments.

Conference Papers

Zhao, L., Huang, S. & Dissanayake, G. 2014, 'Linear MonoSLAM: A Linear Approach to Large-Scale Monocular SLAM Problems', Hong Kong, China, May 2014 in 2014 IEEE International Conference on Robotics & Automation (ICRA), ed Ning Xi, IEEE, Hong Kong, China, pp. 1517-1523.
This paper presents a linear approach for solving monocular simultaneous localization and mapping (SLAM) problems. The algorithm rst builds a sequence of small initial submaps and then joins these submaps together in a divideand-conquer (D&C) manner. Each of the initial submap is built using three monocular images by bundle adjustment (BA), which is a simple nonlinear optimization problem. Each step in the D&C submap joining is solved by a linear least squares together with a coordinate and scale transformation. Since the only nonlinear part is in the building of the initial submaps, the algorithm makes it possible to solve large-scale monocular SLAM while avoiding issues associated with initialization, iteration, and local minima that are present in most of the nonlinear optimization based algorithms currently used for large-scale monocular SLAM. Experimental results based on publically available datasets are used to demonstrate that the proposed algorithms yields solutions that are very close to those obtained using global BA starting from good initial guess.
Wang, Y. & Huang, S. 2014, 'Motion Segmentation based Robust RGB-D SLAM', Shenyang, China, June 2014 in WCICA 2014 USB Stick Proceedings, ed Tianyou Chai; Tzyh Jong Tarn; Hong Wang, IEEE, Shenyang, China.
Zhao, L., Huang, S. & Dissanayake, G. 2013, 'Linear SLAM: A Linear Solution to the Feature-based and Pose Graph SLAM based on Submap Joining', 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Tokyo, Japan, November 2013 in 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), ed Sugano, S. and Kaneko, M., IEEE, Tokyo, Japan, pp. 24-30.
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This paper presents a strategy for large-scale SLAM through solving a sequence of linear least squares problems. The algorithm is based on submap joining where submaps are built using any existing SLAM technique. It is demonstrated that if submaps coordinate frames are judiciously selected, the least squares objective function for joining two submaps becomes a quadratic function of the state vector. Therefore, a linear solution to large-scale SLAM that requires joining a number of local submaps either sequentially or in a more efficient Divide and Conquer manner, can be obtained. The proposed Linear SLAM technique is applicable to both feature-based and pose graph SLAM, in two and three dimensions, and does not require any assumption on the character of the covariance matrices or an initial guess of the state vector. Although this algorithm is an approximation to the optimal full nonlinear least squares SLAM, simulations and experiments 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 started from an accurate initial value. The C/C++ and MATLAB source codes for the proposed algorithm are available on OpenSLAM.
Hu, G., Khosoussi, K. & Huang, S. 2013, 'Towards a Reliable SLAM Back-End', IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Tokyo, Japan, November 2013 in IEEE/RSJ International Conference on Intelligent Robots and Systems, ed Amato, N;, IEEE, Piscataway, USA, pp. 37-43.
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In the state-of-the-art approaches to SLAM, the problem is often formulated as a non-linear least squares. SLAM back-ends often employ iterative methods such as Gauss-Newton or Levenberg-Marquardt to solve that problem. In general, there is no guarantee on the global convergence of these methods. The back-end might get trapped into a local minimum or even diverge depending on how good the initial estimate is. Due to the large noise in odometry data, it is not wise to rely on dead reckoning for obtaining an initial guess, especially in long trajectories. In this paper we demonstrate how M-estimation can be used as a bootstrapping technique to obtain a reliable initial guess. We show that this initial guess is more likely to be in the basin of attraction of the global minimum than existing bootstrapping methods. As the main contribution of this paper, we present new insights about the similarities between robustness against outliers and robustness against a bad initial guess. Through simulations and experiments on real data, we substantiate the reliability of our proposed method.
Wang, Y. & Huang, S. 2013, 'An Efficient Motion Segmentation Algorithm for Multibody RGB-D SLAM', Australasian Conference on Robotics and Automation, University of New South Wales, Sydney Australia, December 2013 in Proceedings of Australasian Conference on Robotics and Automation, ed Jayantha Katupitiya, Australian Robotics and Automation Association, Sydney, Australia, pp. 1-10.
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A simple motion segmentation algorithm using only two frames of RGB-D data is proposed, and both simulation and experimental segmentation results show its efficiency and reliability. To further verify its usability in multibody SLAM scenarios, we firstly apply it to a simulated typical multibody SLAM problem with only a RGB-D camera, and then utilize it to segment a real RGB-D dataset collected by ourselves. Based on the good results of our motion segmentation algorithm, we can get satisfactory SLAM results for the simulated problem and the segmentation results using real data also enable us to get visual odometry for each motion group thus facilitate the following steps to solve the practical multibody RGB-D SLAM problems.
Wang, Y., Xiong, R., Li, Q. & Huang, S. 2013, 'Kullback-Leibler Divergence based Graph Pruning in Robotic Feature Mapping', European Conference on Mobile Robots, Barcelona, Spain, September 2013 in European Conference on Mobile Robots, ed Juan Andrade-Cetto, IEEE, Piscataway, USA, pp. 32-37.
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In pose feature graph simultaneous localization and mapping, the robot poses and feature positions are treated as graph nodes and the odometry and observations are treated as edges. The size of the graph exerts an important influence on the efficiency of the graph optimization. Conventionally, the size of the graph is kept small by discarding the current frame if it is not spatially far enough from the previous one or not informative enough. However, these approaches cannot discard the already preserved frames when the robot re-visits the previously explored area. We propose a measure derived from Kullbach-Leibler divergence to decide whether a frame should be discarded, achieving an online implementation of the graph pruning algorithm for feature mapping, of which the pruned frame can be any of the preserved frames. The experimental results using real world datasets show that the proposed pruning algorithm can effectively reduce the size of the graph while maintaining the map accuracy.
Kodagoda, S., Alempijevic, A., Huang, S., De La Villefromoy, M.J., Diponio, M. & Cogar, L.J. 2013, 'Moving Away from Simulations: Innovative Assessment of Mechatronic Subjects Using Remote Laboratories', International Conference on Information Technology Based Higher Education and Training, Antalya, Turkey, October 2013 in 2013 International Conference on Information Technology Based Higher Education and Training, ed NA, IEEE, Piscataway, USA, pp. 1-5.
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In response to the rapid growth of online teaching and learning, University of Technology, Sydney (UTS) has been developing a number of remotely accessible laboratories. In this paper, we present our newly developed remote lab robotic rig that uniquely addresses challenges in Mechatronic courses. The rig contains a mobile robotic platform equipped with various sensory modules placed in a maze with a pantograph power system enabling continuous use of the platform. The software architecture employed allows users to develop their simulations using the Player/Stage simulator and subsequently upload the code in the robotic rig for real-time testing. This paper presents the motivation, design concepts and analysis of students' feedback responses to their use of the remote lab robotics rig. Survey results of a pilot study shows the participants highly agreeing that the remote lab contributes to, +deeper understanding of the subject matter+, +flexible learning process+ and +inspire research in robotics+
Kodagoda, S., Alempijevic, A., Huang, S., De La Villefromoy, M.J., Diponio, M. & Cogar, L.J. 2013, 'Innovative Assessment of Mechatronic Subjects Using Remote Laboratories', Antalya, Turkey, October 2013 in International Conference on Information Technology Based Higher Education and Training, ed Okyay Kaynak, IEEE, Antalya, pp. 1-5.
In response to the rapid growth of online teaching and learning, University of Technology, Sydney (UTS) has been developing a number of remotely accessible laboratories. In this paper, we present our newly developed remote lab robotic rig that uniquely addresses challenges in Mechatronic courses. The rig contains a mobile robotic platform equipped with various sensory modules placed in a maze with a pantograph power system enabling continuous use of the platform. The software architecture employed allows users to develop their simulations using the Player/Stage simulator and subsequently upload the code in the robotic rig for real-time testing. This paper presents the motivation, design concepts and analysis of students' feedback responses to their use of the remote lab robotics rig. Survey results of a pilot study shows the participants highly agreeing that the remote lab contributes to, +deeper understanding of the subject matter+, +flexible learning process+ and +inspire research in robotics+.
Himstedt, M., Alempijevic, A., Zhao, L., Huang, S. & Boehme, H. 2012, 'Towards robust vision-based self-localization of vehicles in dense urban environments', Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on, Algarve, Portugal, October 2012 in Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on, ed Nancy, M.A, IEEE, Algarve, Portugal, pp. 3152-3157.
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Self-localization of ground vehicles in densely populated urban environments poses a significant challenge. The presence of tall buildings in close proximity to traversable areas limits the use of GPS-based positioning techniques in such environments. This paper presents an approach to global localization on a hybrid metric-topological map using a monocular camera and wheel odometry. The global topology is built upon spatially separated reference places represented by local image features. In contrast to other approaches we employ a feature selection scheme ensuring a more discriminative representation of reference places while simultaneously rejecting a multitude of features caused by dynamic objects. Through fusion with additional local cues the reference places are assigned discrete map positions allowing metric localization within the map. The self-localization is carried out by associating observed visual features with those stored for each reference place. Comprehensive experiments in a dense urban environment covering a time difference of about 9 months are carried out. This demonstrates the robustness of our approach in environments subjected to high dynamic and environmental changes.
Huang, S., Wang, H., Frese, U. & Dissanayake, G. 2012, 'On the Number of Local Minima to the Point Feature Based SLAM Problem', 2012 IEEE International Conference on Robotics and Automation, Saint Paul, Minnesota, USA, May 2012 in 2012 IEEE International Conference on Robotics and Automation, ed Lynne Parker, IEEE, Saint Paul, Minnesota, USA, pp. 2074-2079.
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Map joining is an efficient strategy for solving feature based SLAM problems. This paper demonstrates that joining of two 2D local maps, formulated as a nonlinear least squares problem has at most two local minima, when the associated uncertainties can be described using spherical covariance matrices. Necessary and sufficient condition for the existence of two minima is derived and it is shown that more than one minimum exists only when the quality of the local maps used for map joining is extremely poor. The analysis explains to some extent why a number of optimization based SLAM algorithms proposed in the recent literature that rely on local search strategies are successful in converging to the globally optimal solution from poor initial conditions, particularly when covariance matrices are spherical. It also demonstrates that the map joining problem has special properties that may be exploited to reliably obtain globally optimal solutions to the SLAM problem.
Wang, H., Hu, G., Huang, S. & Dissanayake, G. 2012, 'On the Structure of Nonlinearities in Pose Graph SLAM', Sydney, Australia, July 2012 in 2012 Robotics: Science and Systems Conference, ed Nicholas Roy, The MIT Press, Sydney, Australia, pp. 1-8.
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Pose graphs have become an attractive representation for solving Simultaneous Localization and Mapping (SLAM) problems. In this paper, we analyze the structure of the nonlinearities in the 2D SLAM problem formulated as the optimizing of a pose graph. First, we prove that finding the optimal configuration of a very basic pose graph with 3 nodes (poses) and 3 edges (relative pose constraints) with spherical covariance matrices, which can be formulated as a six dimensional least squares optimization problem, is equivalent to solving a one dimensional optimization problem. Then we show that the same result can be extended to the optimizing of a pose graph with +two anchor nodes+ where every edge is connecting to one of the two anchor nodes. Furthermore, we prove that the global minimum of the resulting one dimensional optimization problem must belong to a certain interval and there are at most 3 minima in that interval. Thus the globally optimal pose configuration of the pose graph can be obtained very easily through the bisection method and closed-form formulas.
Ahmad, A., Zhao, L., Huang, S. & Dissanayake, G. 2012, 'Convergence comparison of least squares based bearing-only SLAM algorithms using different landmark parametrizations', ICARCV 2012, Guangzhou, China, December 2012 in International Conference on Control, Automation, Robotics & Vision, ed Wang, D., IEEE, Guangzhou, China, pp. 1006-1011.
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This paper compares the convergence of least squares based 2D bearing-only SLAM algorithms using different landmark parametrizations. It is shown that the requirement on the accuracy of the initial value vary significantly when using different landmark parametrizations. Especially, for small scale bearing-only SLAM problems, the region of attraction of the global minimum for Gauss-Newton iteration based bearing-only SLAM algorithm using parallax angle landmark parametrization is significantly larger as compared with those of bearing-only SLAM algorithms using other landmark parametrizations.
Ahmad, A., Huang, S., Wang, J.J. & Dissanayake, G. 2012, 'A new state vector and a map joining algorithm for range-only SLAM', Guangzhou, China, December 2012 in International Conference on Control, Automation, Robotics & Vision, ed Wang, D., IEEE, Guangzhou, China, pp. 1024-1029.
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This paper considers the simultaneous localization and mapping (SLAM) problem where the range-only sensor is used. Landmark initialization is a critical issue in range-only SLAM due to the lack of bearing information from the robot to the landmarks. A new state vector is proposed to be used in solving the range-only SLAM. In the new state vector, the landmark position is represented in different ways under different situations. This new representation avoids the need of multiple hypotheses on the landmark positions implemented in most of the existing range-only SLAM algorithms. Simulation and experimental results demonstrate the effectiveness of the new range-only SLAM algorithm using the new state vector within the least squares framework.
Valls Miro, J., Poon, J. & Huang, S. 2012, 'Low-cost visual tracking with an intelligent wheelchair for innovative assistive care', ICARCV 2012, Guangzhou, China, December 2012 in International Conference on Control, Automation, Robotics & Vision, ed Wang, D., IEEE, Guangzhou, China, pp. 1540-1545.
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This paper presents the development of a lowcost vision-based robotic wheelchair system towards autonomous convoying. The non-holonomic follower vehicle obtains visual real-time pose data of a known coplanar target installed on the back of the leading vehicle. This allows the tracking vehicle to mimic the path of the preceding vehicle, while maintaining a safe distance behind it with the aid of a controller based on the robot+s kinematics constraints. A back-end visual filter is proposed in the planning strategy to overcome the noisy environmental information acquired from the camera as it tracks the vehicle in front. The effectiveness of the approach is evaluated in an indoor setting using data obtained from an instrumented wheelchair platform and a low-cost camera, and validated with observations from a laser range finder and derived (known) maps of the environment
Liu, M., Huang, S., Dissanayake, G. & Wang, H. 2012, 'A convex optimization based approach for pose only SLAM problems', Algarve, Portugal, October 2012 in IEEE/RSJ International Conference on Intelligent Robots and Systems, ed Nancy, A., IEEE, Algarve, Portugal, pp. 1898-1903.
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This paper demonstrates that 2D pose SLAM has an underlining near convex structure when formulated as a least squares (LS) optimization problem. By introducing new variables and some approximations, the LS pose SLAM problem can be formulated as a quadratically constrained quadratic programming (QCQP) problem. The QCQP formulation can then be relaxed into a semi-definite programming (SDP) problem which is convex. Unique solution to the convex SDP problem can be obtained without initial state estimate and can be used to construct a candidate solution to the original LS pose SLAM problem. Simulation datasets and the Intel Research Lab dataset have been used to demonstrate that when the relative pose information contain noises with reasonable level, the candidate solution obtained through the relaxation is very close to the optimal solution to the LS SLAM problem. Thus in practice, the candidate solution can serve as either an approximate solution or a good initial guess for a local optimization algorithm to obtain the optimal solution to the LS pose SLAM problem.
Hu, G., Huang, S., Zhao, L., Alempijevic, A. & Dissanayake, G. 2012, 'A Robust RGB-D SLAM algorithm', Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on, Algarve, Portugal, October 2012 in Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on, ed Nancy, M.A, IEEE, Algarve, Portugal, pp. 1174-1179.
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Recently RGB-D sensors have become very popular in the area of Simultaneous Localisation and Mapping (SLAM). The major advantage of these sensors is that they provide a rich source of 3D information at relatively low cost. Unfortunately, these sensors in their current forms only have a range accuracy of up to 4 metres. Many techniques which perform SLAM using RGB-D cameras rely heavily on the depth and are restrained to office type and geometrically structured environments. In this paper, a switching based algorithm is proposed to heuristically choose between RGB-BA and RGBD-BA based local maps building. Furthermore, a low cost and consistent optimisation approach is used to join these maps. Thus the potential of both RGB and depth image information are exploited to perform robust SLAM in more general indoor cases. Validation of the proposed algorithm is performed by mapping a large scale indoor scene where traditional RGB-D mapping techniques are not possible.
Zhao, L., Huang, S., Yan, L. & Dissanayake, G. 2011, 'Parallax angle parametrization for monocular SLAM', IEEE International Conference on Robotics and Automation, Shanghai, China, May 2011 in Proceedings of the 2011 IEEE International Conference on Robotics and Automation, ed Bicchi, Antonio, IEEE, Piscataway, NJ, USA, pp. 3117-3124.
This paper presents a new unified feature parametrization approach for monocular SLAM. The parametrization is based on the parallax angle and can reliably represent both nearby and distant features, as well as features in the direction of camera motion and features observed only once. A new bundle adjustment (BA) algorithm using the proposed parallax angle parametrization is developed and shown to be more reliable as compared with existing BA algorithms that use Euclidean XYZ or inverse depth parametrizations. A new map joining algorithm that allows combining a sequence of local maps generated using BA with the proposed parametrization, that avoids the large computational cost of a global BA, and can automatically optimize the relative scales of the local maps without any loss of information, is also presented. Extensive simulations and a publicly available large-scale real dataset with centimeter accuracy ground truth are used to demonstrate the accuracy and consistency of the BA and map joining algorithms using the new parametrization. Especially, since the relative scales are optimized automatically in the proposed BA and map joining algorithms, there is no need to compute any relative scales even for a loop more than 1km.
Liu, M., Huang, S. & Dissanayake, G. 2011, 'Feature based SLAM using laser sensor data with maximized information usage', IEEE International Conference on Robotics and Automation, Shanghai, China, May 2011 in Proceedings of the 2011 IEEE International Conference on Robotics and Automation, ed Bicchi, Antonio, IEEE, Shanghai, China, pp. 1811-1816.
This paper formulates the SLAM problem using 2D laser data as an optimization problem. The environment is modeled as a set of curves and the variables of the optimization problem are the robot poses as well as the parameters describing the curves. There are two key differences between this SLAM formulation and existing SLAM methods. First, the environment is represented by continuous curves instead of point clouds or occupancy grids. Second, all the laser readings, including laser beams which returns its maximum range value, have been included in the objective function. As the objective function to be optimized contains discontinuities, it can not be solved by standard gradient based approaches and thus a Genetic Algorithm (GA) based method is applied. Matching of laser scans acquired from relatively far apart robot poses is achieved by applying GA on top of the Iterative closest point (ICP) algorithm. The new SLAM formulation and the use of a global optimization algorithm successfully avoid the convergence to local minimum for both the scan matching and the SLAM problem. Both simulated and experimental data are used to demonstrate the effectiveness of the proposed techniques.
Ahmad, A., Huang, S., Wang, J.J. & Dissanayake, G. 2011, 'A new state vector for range-only SLAM', Chinese Control and Decision Conference, Mianyang, Sichuan, China, May 2011 in Proceedings of the 2011 Chinese Control and Decision Conference, ed Guanghong Yang, IEEE, Mianyang, Sichuan, China, pp. 3413-3418.
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This paper considers the simultaneous localization and mapping (SLAM) problem where the range-only sensor is used. Landmark initialization is a critical issue in rangeonly SLAM due to the lack of bearing information from the robot to the landmarks. A new state vector is proposed to be used in solving the range-only SLAM. In the new state vector, the landmark position is represented in different ways under different situations. This new representation avoids the need of multiple hypotheses on the landmark positions implemented in most of the existing range-only SLAM algorithms. Simulation and experimental results demonstrate the effectiveness of the new range-only SLAM algorithm using the new state vector within the least squares framework.
Cai, B., Huang, S., Liu, D., Yuan, S., Dissanayake, G., Lau, h. & Pagac, D. 2011, 'Optimisation model and exact algorithm for Autonomous Straddle Carrier Scheduling at automated container terminals', IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, California, USA, September 2011 in Proceedings of 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, ed Nancy M. Amato, IEEE, San Francisco, California, USA, pp. 3686-3693.
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In this paper, an optimisation model based on Pickup and Delivery Problem with Time Windows (PDPTW), and an exact algorithm based on Branch-and-Bound with Column Generation (BBCG), are presented for Autonomous Straddle Carriers Scheduling (ASCS) problem at automated container terminals. The ASCS problem is firstly modeled into a PDPTW, which is formulated as a Binary Integer Programming (BIP) and then solved by Column Generation (CG) in the Branch-and-Bound (BB) framework. The BBCG algorithm is also compared to another two exact algorithms [i.e., Binary integer Programming with Dynamic Programming (BPDP) and Exhaustive Search with Permutation and Combination (ESPC)] for the ASCS problem solving. Based on the map of an actual automated container terminal, simulation results and discussions are presented to demonstrate the effectiveness and efficiency of the presented model and algorithm for autonomous vehicle scheduling.
Dissanayake, G., Huang, S., Wang, Z. & Ranasinghe, R.S. 2011, 'A Review of Recent Developments in Simultaneous Localization and Mapping', International Conference on Industrial and Information Systems, Sri Lanka, August 2011 in Proceedings of the 6th International Conference on Industrial and Information Systems, ed IEEE, IEEE, Sri Lanka, pp. 477-482.
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Simultaneous Localization and Mapping (SLAM) problem has been an active area of research in robotics for more than a decade. Many fundamental and practical aspects of SLAM have been addressed and some impressive practical solutions have been demonstrated. The aim of this paper is to provide a review of the current state of the research on feature based SLAM, in particular to examine the current understanding of the fundamental properties of the SLAM problem and associated issues with the view to consolidate recent achievements.
Yuan, S., Skinner, B., Huang, S., Liu, D., Dissanayake, G., Lau, H., Pagac, D. & Pratley, T. 2010, 'Mathematical Modelling of Container Transfers for a Fleet of Autonomous Straddle Carriers', IEEE International Conference on Robotics and Automation, Anchorage, Alaska, USA, May 2010 in Proceedings of the 2010 IEEE International Conferences on Robotics and Automation, ed Nancy M. Amato, Oliver Brock, Christian Laugier, Spiridon (Spyros) Reveliotis, Yasushi Yagi, Hirohiko Arai, Stefano Chiaverini, Allison M. Okamura, Gaurav S. Sukhatme, IEEE, Alaska, USA, pp. 1261-1266.
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The main contribution of this paper is a mathematical model describing performance metrics for coordinating multiple mobile robots in a seaport container terminal. The scenario described here requires dealing with many difficult practical challenges such as the presence of multiple levels of container stacking and sequencing, variable container orientations, and vehicular dynamics that require finite acceleration and deceleration times. Furthermore, in contrast to the automatically guided vehicle planning problem in a manufacturing environment, the container carriers described here are free ranging. Although, the port structure imposes a set of ++virtual+ roadways along which the vehicles are allowed to travel, path planning is essential in preventing contention and collisions. A performance metric which minimises total yard-vehicle usage, while producing robust traffic plans by encouraging both early starting and finishing of jobs is presented for different vehicle fleet sizes and job allocation scenarios.
Ahmad, A., Huang, S. & Dissanayake, G. 2010, 'Accurate Large-Scale Bearing-Only SLAM by Map Joining', Proceedings of the Australasian Conference on Robotics and Automation, Brisbane, Queensland, Australia, December 2010 in Proceedings of the Australasian Conference on Robotics and Automation 2010 (ACRA 2010), ed Gordon Wyeth, Australasian Conference on Robotics and Automation, Brisbane, Australia, pp. 1-10.
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This paper presents a bearing-only SLAM algorithm that generates accurate and consistent maps of large environments by joining a series of small local maps. The local maps are built by least squares optimization with a proper landmark initialization technique. The local maps are then combined to build global map using Iterated Sparse Local Submap Joining Filter (I-SLSJF). The accuracy and consistency of the proposed algorithm is evaluated using simulation data sets. The algorithm is also tested using the DLR-Spatial-Cognition data set and the preprocessed Victoria Park data where the range information is ignored. The global map results are very similar to the result of full least squares optimization starting with very accurate initial values. As I-SLSJF is able to join a given set of local maps and associated uncertainties efficiently without any information loss, these results demonstrate that focusing on generating accurate local maps is a promising direction for solving large-scale bearing-only SLAM problems.
Zhao, L., Huang, S., Yan, L., Wang, J.J., Hu, G. & Dissanayake, G. 2010, 'Large-Scale Monocular SLAM by Local Bundle Adjustment and Map Joining', Int. Conf. Control, Automation, Robotics and Vision, Singapore, December 2010 in Proc. of the 11th. Int. Conf. Control, Automation, Robotics and Vision (ICARCV 2010), ed IEEE Technical Committee, IEEE, Singapore, pp. 431-436.
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This paper first demonstrates an interesting property of bundle adjustment (BA), ++scale drift correction+ . Here ++scale drift correction+ means that BA can converge to the correct solution (up to a scale) even if the initial values of the camera pose translations and point feature positions are calculated using very different scale factors. This property together with other properties of BA makes it the best approach for monocular Simultaneous Localization and Mapping (SLAM), without considering the computational complexity. This naturally leads to the idea of using local BA and map joining to solve large-scale monocular SLAM problem, which is proposed in this paper. The local maps are built through Scale-Invariant Feature Transform (SIFT) for feature detection and matching, random sample consensus (RANSAC) paradigm at different levels for robust outlier removal, and BA for optimization. To reduce the computational cost of the large-scale map building, the features in each local map are judiciously selected and then the local maps are combined using a recently developed 3D map joining algorithm. The proposed large-scale monocular SLAM algorithm is evaluated using a publicly available dataset with centimeter-level ground truth.
Liu, M., Huang, S., Dissanayake, G. & Kodagoda, S. 2010, 'Towards a Consistent SLAM Algorithm using B-Splines to Represent Environments', IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan, October 2010 in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, ed Ren C. Luo, Hajime Asama, IEEE, Taiwan, pp. 2065-2070.
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This paper presents a statistically consistent SLAM algorithm where the environment is represented using a collection of B-Splines. The use of B-Splines allow environment to be represented without having to extract specific geometric features such as lines or points. Our previous work proposed a new observation model that enables raw measurements taken from a laser range finder to be transferred into relative position information between the control points of a B-Spline and the robot pose where the observation is made. One of the unresolved issues in the work was the estimation of the observation covariance, which is addressed through an analytical approach in this paper. As the uncertainty associated with the observation model is accurately defined and an optimization approach is used in the estimation process, the proposed SLAM algorithm can produce consistent estimates. Both simulation and experimental data are used for evaluation of the results.
Huang, S., Lai, Y., Frese, U. & Dissanayake, G. 2010, 'How far is SLAM from a linear least squares problem?', IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan, October 2010 in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, ed Ren C. Luo, Hajime Asama, IEEE, Taiwan, pp. 3011-3016.
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Most people believe SLAM is a complex nonlinear stimation/optimization problem. However, recent research shows that some simple iterative methods based on linearization can sometimes provide surprisingly good solutions to SLAM without being trapped into a local minimum. This demonstrates that hidden structure exists in the SLAM problem that is yet to be understood. In this paper, we first analyze how far SLAM is from a convex optimization problem. Then we show that by properly choosing the state vector, SLAM problem can be formulated as a nonlinear least squares problem with many quadratic terms in the objective function, thus it is clearer how far SLAM is from a linear least squares problem. Furthermore, we explain that how the map joining approaches reduce the nonlinearity/nonconvexity of the SLAM problem.
Behrens, M.J., Huang, S. & Dissanayake, G. 2010, 'Models for pushing objects with a mobile robot using single point contact', IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan, October 2010 in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, ed Ren C. Luo, Hajime Asama, IEEE, Taiwan, pp. 2964-2969.
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In many mobile robotic manipulation tasks it is desirable to interact with the robots surroundings without actually grasping the object being manipulated. Non-prehensile manipulation allows a robot to interact in situations which would otherwise be impossible due to size or weight. This paper presents the derivation of a mathematical model of an object pushed by a single point and sliding in the presence of friction where the dynamic effects of mass and inertia are significant. This model is validated using numerical simulation. The derived dynamic model is also compared with a kinematic approximation from literature, showing that under certain conditions, the motion of a pushed object is similar to the motion of a non-holonomic vehicle. Finally, the results of experimental investigations are discussed and promising directions for further work are proposed.
Hu, G., Huang, S. & Dissanayake, G. 2010, 'Evaluation of Pose Only SLAM', IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan, October 2010 in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, ed Ren C. Luo, Hajime Asama, IEEE, Taiwan, pp. 3732-3737.
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In recent SLAM (simultaneous localization and mapping) literature, Pose Only optimization methods have become increasingly popular. This is greatly supported by the fact that these algorithms are computationally more efficient, as they focus more on the robots trajectory rather than dealing with a complex map. Implementation simplicity allows these to handle both 2D and 3D environments with ease. This paper presents a detailed evaluation on the reliability and accuracy of Pose Only SLAM, and aims at providing a definitive answer to whether optimizing poses is more advantages than optimizing features. Focus is centered around TORO, a Tree based network optimization algorithm, which has gained increased recognition within the robotics community. We compare this with Least Squares, which is often considered one of the best Maximum Likelihood method available. Results are based on both simulated and real 2D environments, and presented in a way where our conclusions can be substantiated.
Su, S.W., Nguyen, H.T., Jarman, R., Zhu, J., Lowe, D.B., McLean, P.B., Huang, S., Nguyen, T., Nicholson, R.S. & Weng, K. 2009, 'Model Predictive Control of Gantry Crane with Input Nonlinearity Compensation', International Conference on Control, Automation and Systems Engineering, Penang, Malaysia, February 2009 in International Conference on Control, Automation and Systems Engineering, ed Ardil C, World Academy of Science, Engineering and Technology, Penang, Malaysia, pp. 312-316.
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This paper proposed a nonlinear model predictive control (MPC) method for the control of gantry crane. One of the main motivations to apply MPC to control gantry crane is based on its ability to handle control constraints for multivariable systems. A pre-compensator is constructed to compensate the input nonlinearity (nonsymmetric dead zone with saturation) by using its inverse function. By well tuning the weighting function matrices, the control system can properly compromise the control between crane position and swing angle. The proposed control algorithm was implemented for the control of gantry crane system in System Control Lab of University of Technology, Sydney (UTS), and achieved desired experimental results.
Yuan, S., Lau, H., Liu, D., Huang, S., Dissanayake, G., Pagac, D. & Pratley, T. 2009, 'Simultaneous dynamic scheduling and collision-free path planning for multiple autonomous vehicles', IEEE International Conference on Information and Automation, Zhuhai/Macau, China, June 2009 in Proceedings of the 2009 IEEE International Conference on Information and Automation (ICIA-2009), ed Hong Zhang, James Mills, Yangmin Li, IEEE, IEEE, pp. 522-527.
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When autonomous vehicles are deployed to perform transportation tasks within a confined space and strict time constraint, the problem of optimizing the assignment of tasks to vehicles is complicated by the need to ensure safety (they do not collide with or impede each other) and maximize the efficiency and productivity. With the increasing number of autonomous vehicles in practical settings, the ability to schedule tasks in a manner that inherently considers the effects of task allocations on space contention (which in turn compromises efficiency) is important to performance improvement. The main contribution of this paper is an approach to simultaneously conduct dynamic task allocation and collision-free path planning in an environment where multiple autonomous vehicles operate on a network of paths and where each path segment can only be occupied by one vehicle at a given instant. In particular, a generic algorithm for effective task allocation is investigated and applied in conjunction with an application-specific objective function. The proposed approach is able to solve the dynamic scheduling, planning and collision avoidance problem in an integrated way such that the overall productivity of the transportation system is improved. Simulation results based on a real-world industrial material handling environment demonstrate the feasibility and effectiveness of the proposed method.
Yuan, S., Huang, S., Liu, D., Lau, H., Pagac, D. & Pratley, T. 2009, 'Problem formulation of simultaneously dynamic scheduling and collision-free path planning for multiple autonomous vehicles in automated container terminals', International Symposium for Maritime Logistics and Supply Chain Systems, Singapore, April 2009.
This work is motivated by an actual application of dispatching and path planing for a fleet of autonomous straddle carriers operated by Patrick Stevedores at the fully automated container terminal located in Brisbane, Australia. In this application, the straddle carriers can localize themselves and navigate autonomously within in the seaport. They can also pick up, transport, and drop down the containers without any human interference. The container transportation tasks performed by the fully autonomous straddle carriers include moving containers from quay crane area to yard, from yard to quay crane area, from yard to yard, from truck area to yard and from yard to truck area.
Wang, J.J., Hu, G., Huang, S. & Dissanayake, G. 2009, '3D Landmarks Extraction from a Range Imager Data for SLAM', Australasian Conference on Robotics and Automation, Sydney, Australia, December 2009 in Proceedings of the 2009 Australasian Conference on Robotics and Automation, ed Steve Scheding, Australian Robotics and Automation Association Inc., Sydney, Australia, pp. 1-8.
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This paper introduces a new 3D landmark extraction method using the range and intensity images captured by a single range camera. Speeded up robust features (SURF) detection and matching is used to extract and match features from the intensity images. The range image information is used to transfer the selected 2D features into 3D points. The range measurement bias and uncertainty of the range camera are analysed, and their models are developed for improving the range estimation. After outliers++ detection and removal using random sampling consensus (RANSAC), reliable 3D points are obtained. 3D landmarks for imultaneous localisation and mapping (SLAM) are selected from the 3D points considering several factors, such as the uncertainty and geometry of their locations. Because of the availability of the SURF descriptor, the data association in SLAM has been performed using both the geometry and the descriptor information. The proposed method is tested in unstructured indoor environments, where the range camera moves in six degrees of freedom. Experimental results demonstrate the success of the proposed 3D landmark extraction method for SLAM.
Liu, M., Huang, S. & Dissanayake, G. 2009, 'A new observation model for B-Spline SLAM', Australasian Conference on Robotics and Automation, Sydney, Australia, December 2009 in Proceedings of the 2009 Australasian Conference on Robotics and Automation, ed Steve Scheding, Australian Robotics and Automation Association Inc., Sydney, Australia, pp. 1-8.
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Recently, a novel laser data based SLAM algorithm using B-Spline as features has been developed in [Pedraza et al., 2007]. EKF is used in the proposed BS-SLAM algorithm and the state vector contains the current robot pose together with the control points of the splines. The obervation model used for the EKF update is the intersections of the laser beams with the splines contained in the map. In this paper, we propose a new observation model for B-Spline SLAM. By properly defining the control points for the splines, the observation model can be expressed as a function of relative positions between control points and the robot pose, which is the same format as what used in point feature based SLAM. This new observation model make it possible to apply optimization based techniques to B-Spline SLAM, which has the potential to resolve the inconsistency issues of B-Spline SLAM.
Hu, G., Huang, S. & Dissanayake, G. 2009, '3D I-SLSJF: A consistent sparse local submap joining algorithm for building large-scale 3D Map', IEEE Conference on Decision and Control, Shanghai, China, December 2009 in Proceedings of the 48th IEEE Conference on Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009., ed IEEE Technical Committees, IEEE, China, pp. 6040-6045.
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This paper presents an efficient and reliable algorithm for autonomous robots to build large-scale three dimensional maps by combining small local submaps. The algorithm is a generalization of our recent work on two dimensional map joining algorithm ++ Iterated Sparse Local Submap Joining Filter (I-SLSJF). The 3D local submap joining problem is formulated as a least squares optimization problem and solved by Extended Information Filter (EIF) together with smoothing and iterations. The resulting information matrix is exactly sparse and this makes the algorithm efficient. The smoothing and iteration steps improve the accuracy and consistency of the estimate. The consistency and efficiency of 3D I-SLSJF is demonstrated by comparing the algorithm with some existing algorithms using computer simulations.
Ren, T., Kwok, N., Liu, D. & Huang, S. 2008, 'Path Planning for a Robotic Arm Sand-blasting System', IEEE International Conference on Information and Automation, Zhangjiajie City, Hunan, China, June 2008 in Proceedings of the IEEE International Conference on Information and Automation, ed Y. F. Liu, IEEE, China, pp. 1067-1072.
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Steel bridges are vulnerable to corrosion and their surfaces have to be de-rusted and repainted regularly. Since the process is complicated, expensive and the removed paints are harmful to human workerspsila health; the use of an automatic robotic system would be an attractive alternative. This paper presents an approach for planning paths for a robotic arm used in the sand-blasting operation. A hexagonal topology-based coverage pattern is adopted to reduce the amount of un-blasted areas and an editing process is included to confine the blasted areas within desirable boundaries. Furthermore, a genetic algorithm is employed to obtain an effective path with minimum arm travel distances and magnitude of turns. Collisions to obstacles are alleviated by making use of the force-field strategy. The effectiveness of the proposed methods is verified by simulations based on an industrial robot arm model and a complex bridge environment.
Huang, S., Wang, Z. & Dissanayake, G. 2008, 'Exact state and covariance submatrix recovery for submap based sparse EIF SLAM algorithms', IEEE International Conference on Robotics and Automation, Pasadena, California, May 2008 in Proceeding of 2008 IEEE International Conference on Robotics and Automation, ed IEEE, IEEE, Pasadena, California, pp. 1868-1873.
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This paper provides a novel state vector and covariance sub-matrix recovery algorithm for a recently developed submap based exactly sparse extended information filter (EIF) SLAM algorithm - sparse local submap joining filter (SLSJF). The algorithm achieves exact recovery instead of approximate recovery. The recovery algorithm is very efficient because of an incremental Cholesky factorization approach and a natural reordering of the global state vector. Simulation results show that the computation cost of the SLSJF is much lower as compared with the sequential map joining algorithm using extended Kalman filter (EKF). The SLSJF with the proposed recovery algorithm is also successfully applied to the Victoria Park data set.
Leung, C., Huang, S. & Dissanayake, G. 2008, 'Active SLAM for structured environments', IEEE International Conference on Robotics and Automation, Pasadena, USA, May 2008 in Proceeding of 2008 IEEE International Conference on Robotics and Automation, ed IEEE, IEEE, Piscataway, USA, pp. 1898-1903.
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This paper considers the trajectory planning problem for line-feature based SLAM in structured indoor environments. The robot poses and line features are estimated using smooth and mapping (SAM) which is found to provide more consistent estimates than the extended Kalman filter (EKF) The objective of trajectory planning is to minimise the uncertainty of the estimates and to maximise coverage. Trajectory planning is performed using model predictive control (MPC) with an attractor incorporating long term goals. This planning is demonstrated both in simulation and in a real-time experiment with a Pioneer2DX robot.
Huang, S. & Su, S.W. 2008, 'Robust Control for Nonlinear Discrete-Time Systems with Quantitative Input to State Stability Requirement', IFAC World Congress, Seoul, Korea, July 2008 in Proceedings of the 17th World Congress, The International Federation of Automatic Control (IFAC), ed Chung, Myung Jin, Misra, Pradeep, 2008 IFAC, Seoul, Korea, pp. 14186-14191.
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In this paper, we consider state feedback robust control problems for discretetime nonlinear systems subject to disturbances. The objective of the control is to minimize a performance function while guaranteeing a prescribed quantitative input to state stability (ISS) property for the closed-loop systems. By introducing the concept of ISS control invariant set, a sufficient condition for the problem to be feasible is given. Built on the sufficient condition, a computationally efficient control design algorithm based on one-step min-max optimization is developed. An example is given to illustrate the proposed strategy.
Huang, S., Wang, Z., Dissanayake, G. & Frese, U. 2008, 'Iterated SLSJF: A Sparse Local Submap Joining Algorithm with Improved Consistency', Australasian Conference on Robotics and Automation, Canberra, Australia, December 2008 in Proceeding of ACRA, ed ARAA, ARAA, Canberra, Australia, pp. 1-9.
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This paper presents a new local submap joining algorithm for building large-scale feature based maps. The algorithm is based on the recently developed Sparse Local Submap Joining Filter (SLSJF) and uses multiple iterations to improve the estimate and hence is called Iterated SLSJF (I-SLSJF). The input to the I-SLSJF algorithm is a sequence of local submaps. The output of the algorithm is a global map containing the global positions of all the features as well as all the robot start/end poses of the local submaps.
Wang, Z., Huang, S. & Dissanayake, G. 2007, 'DSLAM: Decoupled Localization and Mapping for Autonomous Robots', International Symposium on Robotics Research, San Francisco, USA, October 2005 in Robotics Research: Springer tracts in Advanced Robotics Vol 28 - 2005 International Symposium of Robotics Research Proceedings, ed N/A, Springer, San Francisco, USA, pp. 203-213.
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The main contribution of this paper is the reformulation of the simultaneous localization and mapping (SLAM) problem for mobile robots such that the mapping and localization can be treated as two concurrent yet separated processes: D-SLAM (decoupled SLAM). It is shown that SLAM can be decoupled into solving a non-linear static estimation problem for mapping and a low-dimensional dynamic estimation problem for localization. The mapping problem can be solved using an Extended Information Filter where the information matrix is shown to be exactly sparse. A significant saving in the computational effort can be achieved for large scale problems by exploiting the special properties of sparse matrices. An important feature of D-SLAM is that the correlation among landmarks are still kept and it is demonstrated that the uncertainty of the map landmarks monotonically decrease. The algorithm is illustrated through computer simulations and experiments.
Su, S.W., Nguyen, J., Jarman, R., Huang, S., Chen, W., Celler, B.G., Bao, J., Lee, P. & Weng, K. 2007, 'A new decentralized fault tolerant control strategy and the fault accommodation of coupled drives', International Conference on Intelligent Technologies, Sydney, Australia, December 2007 in Proceedings of the 8th International Conference on Intelligent Technologies (InTech'07), ed Q. P. Ha and N. M. Kwok, University of Technology, Sydney, Sydney Australia, pp. 313-317.
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Su, S.W., Huang, S., Wang, L., Celler, B.G., Savkin, A.V., Guo, Y. & Cheng, T.M. 2007, 'Nonparametric Hammerstein Model Based Model Predictive Control for Heart Rate Regulation', IEEE Engineering in Medicine and Biology Society Annual Conference, Lyon, France, August 2007 in Proceedings of the 29th International Conference of the IEEE Engineering in Medicine and Biology Society, ed A. Dittmar, J. Clark, N. Lovell, E. McAdams, Medicine and Biology Society, U.S.A, pp. 2984-2987.
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This paper proposed a novel nonparametric model based model predictive control approach for the regulation of heart rate during treadmill exercise. As the model structure of human cardiovascular system is often hard to determine, nonparametric modelling is a more realistic manner to describe complex behaviours of cardiovascular system. This paper presents a new nonparametric Hammerstein model identification approach for heart rate response modelling. Based on the pseudo-random binary sequence experiment data, we decouple the identification of linear dynamic part and input nonlinearity of the Hammerstein system. Correlation analysis is applied to acquire step response of linear dynamic component. Support Vector Regression is adopted to obtain a nonparametric description of the inverse of input static nonlinearity that is utilized to form an approximate linear model of the Hammerstein system. Based on the established model, a model predictive controller under predefined speed and acceleration constraints is designed to achieve safer treadmill exercise. Simulation results show that the proposed control algorithm can achieve optimal heart rate tracking performance under predefined constraints.
Liu, D., Huang, S. & Brown, T.A. 2007, 'Supporting Teaching and Learning of Optimisation Algorithms with Visualisation Techniques', AAEE - Annual Conference of Australasian Association for Engineering Education, Melbourne, Australia, December 2007 in Proceedings of the 18th Conference of the Australasian Association for Engineering Education, ed Harald Sondergaard and Roger Hadgraft, AAEE, Melbourne, Australia, pp. 1-6.
Lau, H., Huang, S. & Dissanayake, G. 2007, 'Multi-Agent Search with Interim Positive Information', IEEE/RSJ International Conference on Intelligent Robots and Systems, San Diego, USA, October 2007 in IEEE/RSJ International Conference on Intelligent Robots and Systems, ed IEEE, Omnipress, San Diego, USA, pp. 3791-3796.
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A problem of searching with multiple searchers and scouts is presented. Unlike most search problems that terminate as soon as the target is found, successful detection by scouts only improve on the current knowledge of the moving target's location, such that the searchers can more effectively find and service the target in the future. The team must correspondingly plan not only to maximize the probability of the searchers directly finding the target, but also give them the best chance of exploiting any new information from potential scout detections. It is shown that this need to plan for replanning can be addressed by equivalently solving a series of simpler detection search problems that always do terminate on detection. Optimal and heuristic solution methods for this searcher/scout problem are derived, such that the capabilities of all the sensing platforms in a search task are harnessed even when only a subset are capable of actually servicing the target.
Wang, Z., Huang, S. & Dissanayake, G. 2007, 'Multi-robot simultaneous localization and mapping using D-SLAM framework', International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Melbourne, December 2007 in The Third International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ed ARC Research Network on Sensor Networks, ARC Research Network on Sensor Networks, Melbourne, pp. 317-322.
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This paper presents an algorithm for the multi-robot simultaneous localization and mapping (SLAM) problem with the robot initial locations completely unknown. Each robot builds its own local map using the traditional extended Kalman filter (EKF) SLAM algorithm. We provide a new method to fuse the local maps into a jointly maintained global map by first transforming the local map state estimate into relative location information and then conducting the fusion using the decoupled SLAM (D-SLAM) framework (Wang et al., 2007). An efficient algorithm to find the map overlap and corresponding beacons across the maps is developed from a point feature based medical image registration method and the joint compatibility test. By adding the robot initial pose of each local map into the global map state, the algorithm shows valuable properties. Simulation results are provided to illustrate the effectiveness of the algorithm.
Wang, Z.Z., Huang, S. & Dissanayake, G. 2006, 'Implementation issues and experimental evaluation of D-SLAM', International Conference on Field and Service Robotics, Port Douglas, Australia, July 2005 in Field And Service Robotics, ed Corke, P; Sukkarieh, S, Springer-Verlag Berlin, Berlin, Germany, pp. 155-166.
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D-SLAM algorithm first described in [1] allows SLAM to be decoupled into solving a non-linear static estimation problem for mapping and a three-dimensional estimation problem for localization. This paper presents a new version of the D-SLAM algorithm tha
Huang, S., Wang, Z.Z. & Dissanayake, G. 2006, 'Mapping large scale environments using relative position information among landmarks', IEEE International Conference on Robots and Automation, Orlando, FL, May 2006 in Proceedings of the 2006 IEEE International Conference on Robotics and Automation, ed N/A, IEEE, New York, USA, pp. 2297-2302.
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The main contribution of this paper is a new SLAM algorithm for the mapping of large scale environments by combining local maps. The local maps can be generated by traditional Extended Kalman Filter (EKF) based SLAM. Relationships between the locations o
Lau, H., Huang, S. & Dissanayake, G. 2006, 'Probabilistic Search for a Moving Target in an Indoor Environment', IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China, October 2006 in 2006 IEE/RSJ International Conference on Intelligent Robots and Systems, ed N/A, IEEE, Beijing, China, pp. 3393-3398.
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We consider a search for a target moving within a known indoor environment partitioned into interconnected regions of varying sizes. The knowledge of target location is described as a probability distribution over the regions, and the searcher can only move from one region to another as the structure allows. The objective is to find a feasible path through the regions that maximizes the probability of locating the target within fixed time. This problem generalizes the existing optimal searcher path problem (OSP) by additionally stipulating a minimum amount of time that a finite-speed searcher must spend to travel through a region before reaching the next. We propose a technique to obtain the upper bound of detection for solving the problem in a branch and bound framework. Comparisons show that the technique is also superior to known bounding methods for the original optimal searcher path problem
Huang, S. & Dissanayake, G. 2006, 'Convergence Analysis for Extended Kalman Filter based SLAM', IEEE International Conference on Robots and Automation, Orlando, USA, May 2006 in Proceedings of the 2006 IEEE International Conference on Robotics and Automation, ed N/A, IEEE, Piscataway, USA, pp. 412-417.
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The main contribution of this paper is a theoretical analysis of the extended Kalman filter (EKF) based solution to the simultaneous localisation and mapping (SLAM) problem. The convergence properties for the general nonlinear two-dimensional SLAM are provided. The proofs clearly show that the robot orientation error has a significant effect on the limit and/or the lower bound of the uncertainty of the landmark location estimates. Furthermore, some insights to the performance of EKF SLAM and a theoretical analysis on the inconsistencies in EKF SLAM that have been recently observed are given
Leung, C., Huang, S. & Dissanayake, G. 2006, 'Active SLAM Using Model Predictive Control and Attractor Based Exploration', IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China, October 2006 in 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, ed N/A, IEEE, Beijing, China, pp. 5026-5031.
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Active SLAM poses the challenge for an autonomous robot to plan efficient paths simultaneous to the SLAM process. The uncertainties of the robot, map and sensor measurements, and the dynamic and motion constraints need to be considered in the planning process. In this paper, the active SLAM problem is formulated as an optimal trajectory planning problem. A novel technique is introduced that utilises an attractor combined with local planning strategies such as model predictive control (a.k.a. receding horizon) to solve this problem. An attractor provides high level task intentions and incorporates global information about the environment for the local planner, thereby eliminating the need for costly global planning with longer horizons. It is demonstrated that trajectory planning with an attractor results in improved performance over systems that have local planning alone
Lau, H., Huang, S. & Dissanayake, G. 2005, 'Optimal Search for Multiple Targets in a Built Environment', IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, Canada, August 2005 in 2005 IEE/RSJ International Conference on Intelligent Robots and Systems, ed N/A, IEEE Press, Edmonton, Canada, pp. 228-233.
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The main contribution of this paper is an algorithm for autonomous search that minimizes the expected time for detecting multiple targets present in a known built environment. The proposed technique makes use of the probability distribution of the target(s) in the environment, thereby making it feasible to incorporate any additional information, known a-priori or acquired while the search is taking place, into the search strategy. The environment is divided into a set of distinct regions and an adjacency matrix is used to describe the connections between them. The costs of searching any of the regions as well as the cost of travel between them can be arbitrarily specified. The search strategy is derived using a dynamic programming algorithm. The effectiveness of the algorithm is illustrated using an example based on the search of an office environment. An analysis of the computational complexity is also presented.
Leung, C., Huang, S., Dissanayake, G. & Furukawa, T. 2005, 'Trajectory Planning for Multiple Robots in Bearing Only Target Localisation', IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, Canada, August 2005 in 2005 IEE/RSJ International Conference on Intelligent Robots and Systems, ed N/A, IEEE, Edmonton, Canada, pp. 2312-2317.
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This paper provides a solution to the optimal trajectory planning problem in target localisation for multiple heterogeneous robots with bearing-only sensors. The objective here is to find robot trajectories that maximise the accuracy of the locations of the targets at a prescribed terminal time. The trajectory planning is formulated as an optimal control problem for a nonlinear system with a gradually identified model and then solved using nonlinear model predictive control (MPC). The solution to the MPC optimisation problem is computed through exhaustive expansion tree search (EETS) plus sequential quadratic programming (SQP). Simulations were conducted using the proposed methods. Results show that EETS alone performs considerably faster than EETS+SQP with only minor differences in information gain, and that a centralised approach outperforms a decentralised one in terms of information gain. We show that a centralised EETS provides a near optimal solution. We also demonstrate the significance of using a matrix to represent the information gathered.
Huang, S., Kwok, N., Dissanayake, G., Ha, Q.P. & Fang, G. 2005, 'Multi-Step Look-Ahead Trajectory Planning in SLAM: Possibility and Necessity', IEEE International Conference on Robots and Automation, Barcelona, Spain, April 2005 in Proceedings of 2005 IEEE International Conference on Robotics and Automation, ed N/A, The Institute of Electrical and Electronic Engineers Inc, Barcelona, Spain, pp. 1103-1108.
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In this paper, the possibility and necessity of multistep trajectory planning in Extended Kalman Filter (EKF) based SLAM is investigated. The objective of the trajectory planning here is to minimize the estimation error of the robot and landmark locations subject to a given time horizon. We show that the problem can be regarded as an optimization problem for a gradually identified model. A numerical method is proposed for trajectory planning using a variant of the nonlinear Model Predictive Control (MPC). The proposed method is optimal in the sense that the control action is computed using all the information available at the time of decision making. Simulation results are included to compare the results from the one-step look-ahead trajectory planning and the proposed multi-step lookahead technique
Fang, G., Dissanayake, G., Kwok, N. & Huang, S. 2005, 'Near Minimum Time Path Planning for Bearing-Only Localisation and Mapping', IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, Canada, August 2005 in 2005 IEE/RSJ International Conference on Intelligent Robots and Systems, ed N/A, The Institute of Electrical and Electronic Engineers Inc, Edmonton, Canada, pp. 2763-2768.
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The main contribution of this paper is an algorithm for integrating motion planning and simultaneous localisation and mapping (SLAM). Accuracy of the maps and the robot locations computed using SLAM is strongly dependent on the characteristics of the environment, for example feature density, as well as the speed and direction of motion of the robot. Appropriate control of the robot motion is particularly important in bearing-only SLAM, where the information from a moving sensor is essential. In this paper a near minimum time path planning algorithm with a finite planning horizon is proposed for bearing-only SLAM. The objective of the algorithm is to achieve a predefined mapping precision while maintaining acceptable vehicle location uncertainty in the minimum time. Simulation results have shown the effectiveness of the proposed method.
Wang, Z., Huang, S. & Dissanayake, G. 2005, 'Decoupling Localization and Mapping in SLAM Using Compact Relative Maps', IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, Canada, August 2005 in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, ed N/A, IEEE, Piscataway, USA, pp. 1041-1046.
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In this paper, we propose a new algorithm for SLAM that makes use of a state vector consisting of quantities that describes the relative locations among features. In contrast to previous relative map strategies, the new state vector is compact and always consists of 2n 3 elements (in a 2-D environment) where n is the number of features in the map. It is also shown that the information from observations can be transformed and grouped into two parts: first one containing the information about the map and the second one containing the information about the robot location relative to the features in the map. Therefore the SLAM can be decoupled into two processes where mapping uses the first part of the transformed observation vector and localization becomes a 3-dimensional estimation problem. It is also shown that the information matrix of the map is exactly sparse, resulting in potential computational savings when an information filter is used for mapping. The new decoupled SLAM algorithm is called D-SLAM and is illustrated using simulation.
Nguyen, A., Ha, Q.P., Huang, S. & Trinh, H.M. 2004, 'Observer-Based Decentralized Approach to Robotic Formation Control', Australasian Conference on Robotics and Automation, Canberra, Australia, December 2004 in Conference Proceedings, Australasian Conference on Robotics and Automation (ACRA 2004), ed Nick Barnes and David Austin, ARAA Australian Robotics & Automation Association, Canberra, Australia, pp. 1-8.
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Huang, S., James, M.R. & Jiang, Z. 2004, 'L -Bounded Robust Control of Nonlinear Cascade Systems', Asian Control Conference, Melbourne, Australia, July 2004 in Proceedings of the 5th Asian Control Conference, ed ASCC2004, ASCC 2004, Melbourne, Australia, pp. 531-538.
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Huang, S., Wang, Z. & Dissanayake, G. 2004, 'Time Optimal Robot Motion Control in Simultaneous Localization and Map Building (SLAM) Problem', IEEE/RSJ International Conference on Intelligent Robots and Systems, Sendai, Japan, September 2004 in Proceedings of 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, ed Deguchi, K; Hashimoto, K; and Chen P., Publication Committee of IROS 2004, Sendai, Japan, pp. 3110-3115.
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This paper provides a technique for minimal time robot motion control in the estimation-theoretic based simultaneous localizations and map building (SLAM) problem. We consider the scenario that the robot needs to go to a destination which is a prescribed location in the coordinate system referenced by its starting position. The task of the robot is to reach the destination within minimal time while localizing itself and building a map of the environment with a prescribed accuracy. This task may be a real navigation task or may be a subtask in a SLAM problem of a large unknown environment. A global sub-optimal control law is derived using dynamic programming techniques
Huang, S., James, M.R., Nesic, D. & Dower, P. 2004, 'Measurement Feedback Controller Design to Achieve Input to State Stability', IEEE Conference on Decision and Control, Atlantis, Paradise Island, Bahamas, December 2004 in 43rd IEEE Conference on Decision and Control, ed Felicia - cant find, IEEE, http://control.bu.edu/ieee/cdc04, pp. 2515-2520.
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Journal Articles

Zhou, J., Zhang, Q., Men, B. & Huang, S. 2014, 'Input-to-state stability of a class of descriptor systems', International Journal Of Robust And Nonlinear Control, vol. 24, pp. 97-109.
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This paper studies the input-to-state stability (ISS) of descriptor systems with exogenous disturbances. on the basis of the ISS theory of standard state-space nonlinear systems, a sufficient condition for a class of nonlinear descriptor system to be ISS is proved. Furthermore, a design method of the state feedback controllers is given to make the closed-loop system ISS. A numerical example is given to illustrate the effectiveness of the controller design.
Cai, B., Huang, S., Liu, D. & Dissanayake, G. 2014, 'Rescheduling policies for large-scale task allocation of autonomous straddle carriers under uncertainty at automated container terminals', Robotics And Autonomous Systems, vol. 62, pp. 506-514.
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This paper investigates replanning strategies for container transportation task allocation of autonomous Straddle Carriers (SC) at automated container terminals. The strategies address the problem of large-scale scheduling in the context of uncertainty (especially uncertainty associated with unexpected events such as the arrival of a new task). Two rescheduling policies + Rescheduling New arrival Jobs (RNJ) policy and Rescheduling Combination of new and unexecuted Jobs (RCJ) policy + are presented and compared for long-term Autonomous SC Scheduling (ASCS) under the uncertainty of new job arrival. The long-term performance of the two rescheduling policies is evaluated using a multi-objective cost function (i.e., the sum of the costs of SC travelling, SC waiting, and delay of finishing high-priority jobs). This evaluation is conducted based on two different ASCS solving algorithms + an exact algorithm (i.e., branch-and-bound with column generation (BBCG) algorithm) and an approximate algorithm (i.e., auction algorithm) + to get the schedule of each short-term planning for the policy. Based on the map of an actual fully-automated container terminal, simulation and comparative results demonstrate the quality advantage of the RCJ policy compared with the RNJ policy for task allocation of autonomous straddle carriers under uncertainty. Long-term testing results also show that although the auction algorithm is much more efficient than the BBCG algorithm for practical applications, it is not effective enough, even when employed by the superior RCJ policy, to achieve high-quality scheduling of autonomous SCs at the container terminals.
Sun, Y., Zhao, L., Huang, S., Yan, L. & Dissanayake, G. 2014, 'L2-SIFT: SIFT feature extraction and matching for large images in large-scale aerial photogrammetry', ISPRS Journal of Photogrammetry and Remote Sensing, vol. 91, pp. 1-16.
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The primary contribution of this paper is an efficient feature extraction and matching implementation for large images in large-scale aerial photogrammetry experiments. First, a Block-SIFT method is designed to overcome the memory limitation of SIFT for extracting and matching features from large photogrammetric images. For each pair of images, the original large image is split into blocks and the possible corresponding blocks in the other image are determined by pre-estimating the relative transformation between the two images. Because of the reduced memory requirement, eatures can be extracted and matched from the original images without down-sampling. Next, a red-black tree data structure is applied to create a feature relationship to reduce the search complexity when matching tie points. Meanwhile, tree key exchange and segment matching methods are proposed to match the tie points along-track and across-track. Finally, to evaluate the accuracy of the features extracted and matched from the proposed L2-SIFT algorithm, a bundle adjustment with parallax angle feature parametrization (ParallaxBA) is applied to obtain the Mean Square Error (MSE) of the feature reprojections, where the feature extraction and matching result is the only information used in the nonlinear optimisation system. Seven different experimental aerial photogrammetric datasets are used to demonstrate the efficiency and validity of the proposed algorithm. It is demonstrated that more than 33 million features can be extracted and matched from the Taian dataset with 737 images within 21h using the L2-SIFT algorithm. In addition, the ParallaxBA involving more than 2.7 million features and 6 million image points can easily converge to an MSE of 0.03874. The C/C++ source code for the proposed algorithm is available at http://services.eng.uts.edu.au/~sdhuang/research.htm.
Yang, C., Zhang, Q. & Huang, S. 2013, 'Input-to-state stability of a class of Lur'e descriptor systems', International Journal Of Robust And Nonlinear Control, vol. 23, no. 12, pp. 1324-1337.
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This paper considers a class of Lur'e descriptor systems (LDS) subject to exogenous disturbances. The concept of input-to-state stability (ISS) is generalized to descriptor systems. Such a notion characterizes the robust stability of the full state of the systems. Based on the conventional ISS theory, a sufficient condition expressed by linear matrix inequalities (LMIs) for the LDS to be ISS is derived. It is further shown that this condition also guarantees a special class of LDS to be of index one. Then, a state feedback controller is designed to make the closed-loop system ISS. Finally, an example is given to illustrate the obtained results.
Cai, B., Huang, S., Liu, D., Yuan, S., Dissanayake, G., Lau, H. & Pagac, D. 2013, 'Multi-objective optimization for autonomous straddle carrier scheduling at automated container terminals', IEEE Transactions on Automation Science and Engineering, vol. 10, no. 3, pp. 711-725.
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A multiobjective optimization model is presented in this paper for the Autonomous Straddle Carriers Scheduling (ASCS) problem in automated container terminals, which is more practical than the single objective model. The model considers three objectives [i.e., Straddle Carriers (SCs) traveling time, SC waiting time and finishing time of high-priority container-transferring jobs], and their weighted sum is investigated as the representative example. The presented model is formulated as a pickup and delivery problem with time windows in the form of binary integer programming. An exact algorithm based on Branch-and-Bound with Column Generation (BBCG) is employed for solving the multiobjective ASCS problem. Based on the map of an actual fully automated container terminal, simulation results are compared with the single-objective scheduling to demonstrate the effectiveness and flexibility of the presented multiobjective model, as well as the efficacy of the BBCG algorithm for autonomous SC scheduling.
Skinner, B., Yuan, S., Huang, S., Liu, D., Cai, B., Dissanayake, G., Lau, h., Bott, A. & Pagac, D. 2013, 'Optimisation for job scheduling at automated container terminals using genetic algorithm', Computers and Industrial Engineering, vol. 64, no. 1, pp. 511-523.
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This paper presents a genetic algorithm (GA)-based optimisation approach to improve container handling operations at the Patrick AutoStrad container terminal located in Brisbane Australia. In this paper we focus on scheduling for container transfers and encode the problem using a two-part chromosome approach which is then solved using a modified genetic algorithm. In simulation experiments, the performance of the GA-based approach and a sequential job scheduling method are evaluated and compared with different scheduling scenarios. The experimental results show that the GA-based approach can find better solutions which improve the overall performance. The GA-based approach has been implemented in the terminal scheduling system and the live testing results show that the GA-based approach can reduce the overall time-related cost of container transfers at the automated container terminal
Skinner, B., Yuan, S., Huang, S. & Liu, D. 2013, 'A new crossover approach for solving the multiple travelling salesmen problem using genetic algorithms', European Journal Of Operational Research, vol. 228, no. 1, pp. 72-82.
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This paper proposes a new crossover operator called two-part chromosome crossover (TCX) for solving the multiple travelling salesmen problem (MTSP) using a genetic algorithm (GA) for near-optimal solutions. We adopt the two-part chromosome representation technique which has been proven to minimise the size of the problem search space. Nevertheless, the existing crossover method for the two-part chromosome representation has two limitations. Firstly, it has extremely limited diversity in the second part of the chromosome, which greatly restricts the search ability of the GA. Secondly, the existing crossover approach tends to break useful building blocks in the first part of the chromosome, which reduces the GA++s effectiveness and solution quality. Therefore, in order to improve the GA search performance with the two-part chromosome representation, we propose TCX to overcome these two limitations and improve solution quality. Moreover, we evaluate and compare the proposed TCX with three different crossover methods for two MTSP objective functions, namely, minimising total travel distance and minimising longest tour. The experimental results show that TCX can improve the solution quality of the GA compared to three existing crossover approaches.
Wang, H., Huang, S., Frese, U. & Dissanayake, G. 2013, 'The nonlinearity structure of point feature SLAM problems with spherical covariance matrices', Automatica, vol. 49, pp. 3112-3119.
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This paper proves that the optimization problem of one-step point feature Simultaneous Localization and Mapping (SLAM) is equivalent to a nonlinear optimization problem of a single variable when the associated uncertainties can be described using spherical covariance matrices. Furthermore, it is proven that this optimization problem has at most two minima. The necessary and sufficient conditions for the existence of one or two minima are derived in a form that can be easily evaluated using observation and odometry data. It is demonstrated that more than one minimum exists only when the observation and odometry data are extremely inconsistent with each other. A numerical algorithm based on bisection is proposed for solving the one-dimensional nonlinear optimization problem. It is shown that the approach extends to joining of two maps, thus can be used to obtain an approximate solution to the complete SLAM problem through map joining.
Yuan, S., Skinner, B., Huang, S., Liu, D., Dissanayake, G., Lau, H. & Pagac, D. 2011, 'A job grouping approach for planning container transfers at automated seaport container terminals', Advanced Engineering Informatics, vol. 25, no. 3, pp. 413-426.
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This paper proposes a practical job grouping approach, which aims to enhance the time related performance metrics of container transfers in the Patrick AutoStrad container terminal, located in Brisbane, Australia. It first formulates a mathematical model of the automated container transfers in a relatively complex environment. Apart from the consideration on collision avoidance of a fleet of large vehicles in a confined area, it also deals with many other difficult practical challenges such as the presence of multiple levels of container stacking and sequencing, variable container orientations, and vehicular dynamics that require finite acceleration and deceleration times. The proposed job grouping approach aims to improve the makespan of the schedule for yard jobs, while reducing straddle carrier waiting time by grouping jobs using a guiding function. The performance of the current sequential job allocation method and the proposed job grouping approach are evaluated and compared statistically using a pooled t-test for 30 randomly generated yard configurations. The experimental results show that the job grouping approach can effectively improve the schedule makespan and reduce the total straddle carrier waiting time.
Su, S.W., Huang, S., Wang, L., Celler, B.G., Savkin, A.V., Guo, Y. & Cheng, T.M. 2010, 'Optimizing Heart Rate Regulation For Safe Exercise', Annals Of Biomedical Engineering, vol. 38, no. 3, pp. 758-768.
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Safe exercise protocols are critical for effective rehabilitation programs. This paper aims to develop a novel control strategy for an automated treadmill system to reduce the danger of injury during cardiac rehabilitation. We have developed a control-or
Huang, S., Wang, Z., Dissanayake, G. & Frese, U. 2009, 'Iterated D-SLAM map joining: evaluating its performance in terms of consistency, accuracy and efficiency', Autonomous Robots, vol. 27, no. 4, pp. 409-429.
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This paper presents a new map joining algorithm and a set of metrics for evaluating the performance of mapping techniques. The input to the new map joining algorithm is a sequence of local maps containing the feature positions and the final robot pose in a local frame of reference. The output is a global map containing the global positions of all the features but without any robot poses. The algorithm is built on the D-SLAM mapping algorithm (Wang et al. in Int. J. Robot. Res. 26(2):187-204, 2007) and uses iterations to improve the estimates in the map joining step. So it is called Iterated D-SLAM Map Joining (I-DMJ). When joining maps I-DMJ ignores the odometry information connecting successive maps. This is the key to I-DMJ efficiency, because it makes both the information matrix exactly sparse and the size of the state vector bounded by the number of features. The paper proposes metrics for quantifying the performance of different mapping algorithms focusing on evaluating their consistency, accuracy and efficiency. The I-DMJ algorithm and a number of existing SLAM algorithms are evaluated using the proposed metrics. The simulation data sets and a preprocessed Victoria Park data set used in this paper are made available to enable interested researchers to compare their mapping algorithms with I-DMJ.
Lau, H., Huang, S. & Dissanayake, G. 2008, 'Discounted MEAN bound for the optimal searcher path problem with non-uniform travel times', European Journal Of Operational Research, vol. 190, no. 2, pp. 383-397.
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We consider an extension of the optimal searcher path problem (OSP), where a searcher moving through a discretised environment may now need to spend a non-uniform amount of time travelling from one region to another before being able to search it for the
Huang, S., Wang, Z. & Dissanayake, G. 2008, 'Sparse Local Submap Joining Filter for Building Large-Scale Maps', IEEE Transactions On Robotics, vol. 24, no. 5, pp. 1121-1130.
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This paper presents a novel local submap joining algorithm for building large-scale feature-based maps: sparse local submap joining filter (SLSJF). The input to the filter is a sequence of local submaps. Each local submap is represented in a coordinate f
Zhang, H., Huang, S. & James, M.R. 2008, 'H-infinity control for discrete-time nonlinear switching systems', International Journal Of Robust And Nonlinear Control, vol. 18, no. 15, pp. 1451-1481.
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This paper formulates and solves the robust H-infinity control problem for discrete-time nonlinear switching, systems. The H-infinity control problem is interpreted as the l(2) finite gain control problem and is studied using a dissipative systems theory
Wang, Z.Z., Huang, S. & Dissanayake, G. 2007, 'D-SLAM: A decoupled solution to simultaneous localization and mapping', International Journal Of Robotics Research, vol. 26, no. 2, pp. 187-204.
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The main contribution of this paper is the reformulation of the simultaneous localization and mapping (SLAM) problem for mobile robots such that the mapping and localization can be treated as two concurrent yet separated processes D-SLAM (decoupled SLAM)
Huang, S. & Dissanayake, G. 2007, 'Convergence And Consistency Analysis For Extended Kalman Filter Based Slam', IEEE Transactions On Robotics, vol. 23, no. 5, pp. 1036-1049.
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This paper investigates the convergence properties and consistency of Extended Kalman Filter (EKF) based simultaneous localization and mapping (SLAM) algorithms. Proofs of convergence are provided for the nonlinear two-dimensional SLAM problem with point
Kwok, N., Ha, Q.P., Huang, S., Dissanayake, G. & Fang, G. 2007, 'Mobile robot localization and mapping using a Gaussian sum filter', International Journal of Control, Automation, and Systems, vol. 5, no. 3, pp. 251-268.
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Ellekilde, L.P., Huang, S., Valls Miro, J. & Dissanayake, G. 2007, 'Dense 3D Map Construction for Indoor Search and Rescue', Journal of Field Robotics, vol. 24, no. 1/2, pp. 71-89.
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The main contribution of this paper is a new simultaneous localization and mapping (SLAM) algorithm for building dense three-dimensional maps using information acquired from a range imager and a conventional camera, for robotic search and rescue in unstr
Leung, C., Huang, S., Kwok, N. & Dissanayake, G. 2006, 'Planning under Uncertainty Using Model Predictive Control for Information Gathering', Robotics and Autonomous Systems, vol. 54, no. 11, pp. 898-910.
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This paper considers trajectory planning problems for autonomous robots in information gathering tasks. The objective of the planning is to maximize the information gathered within a finite time horizon. It is assumed that either the Extended Kalman Filter (EKF) or the Extended Information Filter (EIF) is applied to estimate the features of interest and the information gathered is expressed by the covariance matrix, or information matrix. It is shown that the planning process can be formulated as an optimal control problem for a nonlinear control system with a gradually identified model. This naturally leads to the Model Predictive Control (MPC) planning strategy, which uses the updated knowledge about the model to solve a finite horizon optimal control problem at each time step and only executes the first control action. The proposed MPC framework is demonstrated through solutions to two challenging information gathering tasks: (1) Simultaneous planning, localization, and map building (SPLAM) and (2) Multi-robot Geolocation. It is shown that MPC can effectively deal with dynamic constraints, multiple robots/features and a range of objective functions.
Huang, S., James, M.R., Nesic, D. & Dower, P. 2005, 'A unified approach to controller design for achieving ISS and related properties', IEEE Transactions On Automatic Control, vol. 50, no. 11, pp. 1681-1697.
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A unified approach to the design of controllers achieving various specified input-to-state stability (ISS) like properties is presented. Both full state and measurement feedback cases are considered. Synthesis procedures based on dynamic programming are
Huang, S., James, M.R. & Jiang, Z. 2005, 'L-infinity-bounded robust control of nonlinear cascade systems', Systems & Control Letters, vol. 54, no. 3, pp. 215-224.
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In this paper, we consider the L-infinity-bounded robust control problem for a class of nonlinear cascade systems with disturbances. Sufficient conditions are provided under which a hard bound is imposed on the system performance measure. The backsteppin
Huang, S., James, M.R., Nesic, D. & Dower, P. 2005, 'Analysis of input-to-state stability for discrete time nonlinear systems via dynamic programming', Automatica, vol. 41, no. 12, pp. 2055-2065.
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The input-to-state stability (ISS) property for systems with disturbances has received considerable attention over the past decade or so, with many applications and characterizations reported in the literature. The main purpose of this paper is to presen
Huang, S. & James, M.R. 2003, 'I-infinity bounded robustness for nonlinear systems: analysis and synthesis', IEEE Transactions on Automatic Control, vol. 48, no. 11, pp. 1875-1891.
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The purpose of this paper is to describe systematic analysis and design tools for robust control problems with l? criteria. We first generalize the Hill-Moylan-Willems framework for dissipative systems to accommodate l? criteria, and then derive state feedback and measurement feedback synthesis procedures for l? robust control problems. The information state framework is used for the measurement feedback robust control problem. Necessary and sufficient conditions are proved, and new synthesis procedures using dynamic programming are presented.
Wang, H., Lam, J., Xu, S. & Huang, S. 2002, 'Robust H-infinity reliable control for a class of uncertain neutral delay systems', International Journal of Systems Science, vol. 33, no. 7, pp. 611-622.
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This paper deals with the problem of robust reliable control for a class of uncertain neutral delay systems. The aim was to design a state feedback controller such that the plant remained stable for all admissible uncertainties as well as actuator faults among a prespecified subset of actuators or sector-type actuator non-linearity, independently of the delay time. A linear matrix inequality approach was developed to solve the problem addressed with an H [sub ?] norm bound constraint on disturbance attenuation.
Huang, S. & Lam, J. 2002, 'Saturated linear-quadratic regulation of uncertain linear systems: stability region estimation and controller design', International Journal of Control, vol. 75, no. 2, pp. 97-110.
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This paper considers the problems of estimating the stability region (domain of attraction) and controller design for uncertain linear continuous-time systems with input saturation when linear quadratic (LQ) optimal controller is used. By exploiting the structure of the LQ controller and the property of saturation functions, it is established that the estimation of stability region can be obtained by solving linear matrix inequality (LMI) problems. Moreover, an iterative LMI (ILMI) algorithm is presented to design an LQ controller such that the largest estimated stability region can be obtained. Two examples are given to compare our results with existing ones.