A multi-objective PSO-based algorithm for robot path planning

In this paper a novel method is presented for robot motion planning with respect to two objectives, the shortest and smoothest path criteria. A Particle Swarm Optimization (PSO) algorithm is employed for global path planning, while the Probabilistic Roadmap method (PRM) is used for obstacle avoidance (local planning). The two objective functions are incorporated in the PSO equations in which the path smoothness is measured by the difference of the angles of the hypothetical lines connecting the robot's two successive positions to its goal. The PSO and PRM are combined by adding good PSO particles as auxiliary nodes to the random nodes generated by the PRM. The proposed algorithm is compared in path length and runtime with the mere PRM method searched by Dijkstra's algorithm, and the results showed that the generated paths are shorter and smoother and are calculated in less time.

[1]  B. Faverjon,et al.  Probabilistic Roadmaps for Path Planning in High-Dimensional Con(cid:12)guration Spaces , 1996 .

[2]  Kikuo Fujimura.  Path planning with multiple objectives , 1996, IEEE Robotics Autom. Mag..

[3]  Jin-Hui Zhu,et al.  Obstacle avoidance with multi-objective optimization by PSO in dynamic environment , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[4]  Li Wang,et al.  Obstacle-avoidance Path Planning for Soccer Robots Using Particle Swarm Optimization , 2006, 2006 IEEE International Conference on Robotics and Biomimetics.

[5]  Ellips Masehian,et al.  Classic and Heuristic Approaches in Robot Motion Planning A Chronological Review , 2007 .

[6]  Z. Zenn Bien,et al.  Multiobjective Navigation of a Guide Mobile Robot for the Visually Impaired Based on Intention Inference of Obstacles , 2001, Auton. Robots.

[7]  O. Weck,et al.  A COMPARISON OF PARTICLE SWARM OPTIMIZATION AND THE GENETIC ALGORITHM , 2005 .

[8]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[9]  Simon Parsons,et al.  Principles of Robot Motion: Theory, Algorithms and Implementations by Howie Choset, Kevin M. Lynch, Seth Hutchinson, George Kantor, Wolfram Burgard, Lydia E. Kavraki and Sebastian Thrun, 603 pp., $60.00, ISBN 0-262-033275 , 2007, The Knowledge Engineering Review.

[10]  Gang Liu,et al.  Multi-Objective Optimal Trajectory Planning of Space Robot Using Particle Swarm Optimization , 2008, ISNN.

[11]  De-Bao Sun,et al.  Path planning for mobile robot using the particle swarm optimization with mutation operator , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

[12]  Yangmin Li,et al.  Smooth Path Planning of a Mobile Robot Using Stochastic Particle Swarm Optimization , 2006, 2006 International Conference on Mechatronics and Automation.