A Specialized Particle Swarm Optimization for global path planning of mobile robots

A specialized global path planning algorithm for mobile robot based on Guaranteed Convergence Particle Swarm Optimization (GCPSO) is proposed. An environmental map was set up and a path connecting the start node and the goal node was coded as a particle. Then, a particular “active region” for particles was mapped out according to the location of obstacles. The initial particle population was generated within this region and particles flied in the “active region” to search for the optimum path. In the search process, both acceleration coefficients and inertia weight of particle swarm optimization algorithm are self-adaptively adjusted and invalid particles are replaced by global optima or local optima in the adjacent area. The simulation studies in both simple environment and complicated environment are carried out and the simulation results show that the proposed algorithm has advantages such as faster search speed and higher search quality.

[1]  Russell C. Eberhart,et al.  Parameter Selection in Particle Swarm Optimization , 1998, Evolutionary Programming.

[2]  Osamu Takahashi,et al.  Motion planning in a plane using generalized Voronoi diagrams , 1989, IEEE Trans. Robotics Autom..

[3]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[4]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.

[5]  Yuhui Shi,et al.  Co-evolutionary particle swarm optimization to solve min-max problems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[6]  Shi Hongbo,et al.  Path planning for mobile robot based on particle swarm optimization , 2008, 2008 Chinese Control and Decision Conference.

[7]  Gao Yibo.  Path Planning Method for Mobile Robot Based on Particle Swarm Algorithm , 2007 .

[8]  Huang Shao-rong,et al.  Survey of particle swarm optimization algorithm , 2009 .

[9]  Tomás Lozano-Pérez,et al.  Automatic Planning of Manipulator Transfer Movements , 1981, IEEE Transactions on Systems, Man, and Cybernetics.

[10]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[11]  Li Ning,et al.  PATH PLANNING FOR MOBILE ROBOT BASED ON PARTICLE SWARM OPTIMIZATION , 2004 .

[12]  Xi Yu-geng.  Particle Swarm Optimization Based Global Path Planning for Mobile Robots , 2005 .

[13]  A. Engelbrecht,et al.  A new locally convergent particle swarm optimiser , 2002, IEEE International Conference on Systems, Man and Cybernetics.

[14]  Li Xiao-an.  Fast Path Planning Based on Grid Model of Robot , 2005 .

[15]  Cai Zi-xing.  Current Status and Future Development of Mobile Robot Path Planning Technology , 2005 .