Robot path planning in an environment with many terrains based on interval multi-objective PSO

In order to solve the problem of path planning in an environment with many terrains, we propose a method based on interval multi-objective Particle Swarm Optimization (PSO). First, the environment is modeled by the line partition method, and then, according to the distribution of the polygonal lines which form the robot path and taking the velocity's disturbance into consideration, robot's passing time is formulated as an interval by combining Local Optimal Criterion (LOC), and the path's danger degree is estimated through the area ratio between the robot path and the danger source. In addition, the path length is also calculated as an optimization objective. As a result, the robot path planning problem is modeled as an optimization problem with three objectives. Finally, the interval multiobjective PSO is employed to solve the problem above. Simulation and experimental results verify the effectiveness of the proposed method.

[1]  M.A. Shoorehdeli,et al.  Path planning of mobile robot using integer GA with considering terrain conditions , 2008, 2008 IEEE International Conference on Systems, Man and Cybernetics.

[2]  Dunwei,et al.  Solving Interval Multi-objective Optimization Problems Using Evolutionary Algorithms with Lower Limit of Possibility Degree , 2013 .

[3]  Xiujuan Lei,et al.  Mobile Robot Path Planning with Complex Constraints Based on the Second-Order Oscillating Particle Swarm Optimization Algorithm , 2009, 2009 WRI World Congress on Computer Science and Information Engineering.

[4]  Michael Zyda,et al.  Solving global two-dimensional routing problems using snell's law and a search , 1987, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[5]  Seid H. Pourtakdoust,et al.  Optimal maneuver-based motion planning over terrain and threats using a dynamic hybrid PSO algorithm , 2013 .

[6]  Terrence P. Fries.  Evolutionary Terrain-Based Navigation of Autonomous Mobile Robots , 2009 .

[7]  Joseph S. B. Mitchell,et al.  An Algorithmic Approach to Some Problems in Terrain Navigation , 1988, Artif. Intell..

[8]  Chia-Ju Wu,et al.  A time-scaling method for near-time-optimal control of an omni-directional robot along specified paths , 2008, Artificial Life and Robotics.

[9]  K. K. SoundraPandian,et al.  Traversability Assessment of Terrain for Autonomous Robot Navigation , 2010 .

[10]  Mauro Birattari,et al.  Swarm Intelligence , 2012, Lecture Notes in Computer Science.

[11]  I. Parberry,et al.  Optimal Path Planning for Mobile Robot Navigation , 2008, IEEE/ASME Transactions on Mechatronics.

[12]  Ellips Masehian,et al.  Multi-objective robot motion planning using a particle swarm optimization model , 2010, Journal of Zhejiang University SCIENCE C.

[13]  Sang Heon Lee,et al.  A faster path planner using accelerated particle swarm optimization , 2012, Artificial Life and Robotics.

[14]  Zafer Bingul,et al.  Dynamic identification of Staubli RX-60 robot using PSO and LS methods , 2011, Expert Syst. Appl..

[15]  Xiaoyan Sun,et al.  Evolutionary algorithms for multi-objective optimization problems with interval parameters , 2010, 2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA).

[16]  Guohui Tian,et al.  Optimal robot path planning based on danger degree map , 2009, 2009 IEEE International Conference on Automation and Logistics.