A novel optimal path-planning and following algorithm for wheeled robots on deformable terrains |
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| Institution: | 1. School of Mechanical, Aerospace and Automotive Engineering, Coventry University, Coventry CV1 2JH, United Kingdom;2. Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Montreal, QC H3G 1M8, Canada;3. Department of Mechanics, Mathematics & Management, Polytechnic of Bari, Via Orabona 4, 70125 Bari, Italy;1. Department of Electrical and Computer Engineering, Concordia University, Montreal, QC H3G1M8, Canada;2. NASA Ames Intelligent Robotics Group (KBR, Inc), United States;1. Advanced Science and Automation Corp., Indianapolis, IN, United States;2. US Army CCDC GVSC, Warren, MI, United States;1. Terramechanics, Multibody, and Vehicle Systems (TMVS) Laboratory, Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA 24061, United States;2. Engineer Research and Development Center, Vicksburg, MS 39180-6199, United States;3. Jaca & Sierra Testing Laboratories, Trujillo Alto 00976, Puerto Rico;1. Department of Mechanical Engineering and Centre for Intelligent Machines, McGill University, Montreal, Canada;2. CM Labs Simulations, Montreal, Canada |
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| Abstract: | An immense body of research has focused on path-planning and following of wheeled robots in unstructured surfaces. Nonholonomic robots traveling over deformable terrains together with complex operating conditions, however, pose further challenges in terms of a higher demand for robustness and optimality. In this paper, a Chaos-enhanced Accelerated Particle Swarm Optimization (CAPSO) algorithm is employed for planning an optimal path of a wheeled robot, so as to ensure shortest path from the starting point to the target location together with safety through guaranteed avoidance of collisions with static and dynamic obstacles. The fundamental terramechanics concepts are employed to derive essential forces and moments acting on the wheeled robot. Subsequently, a kineto-dynamic model of the robot is developed for designing a novel robust control algorithm based on an exponential-integral-sliding mode (EISMC) scheme and a RBF-NN approximator. The results revealed that the proposed algorithm is responsive and robust to withstand adverse effects of structured and unstructured uncertainties by using the designed adaptation law according to the Lyapunov stability theorem. The effectiveness of the proposed algorithm is also validated against several reported frameworks. |
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| Keywords: | Terramechancis Path-planning PSO Terrain Artificial Intelligence |
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