Improved dynamic double mutation particle swarm optimization for mobile robot path planning

This paper presents a new path planning approach, in which the MAKLINK graph is constructed to describe the complex environment of the mobile robot, the enumeration method ideas into Dijkstra algorithm is used to obtain the shortest path, and the dynamic double mutation particle swarm optimization algorithm is adopted to get the optimal path. Finally, simulation results are used to illustrate the validity of the proposed method.

[1]  Zhang Wanx Path planning for intelligent robots based on improved particle swarm optimization algorithm , 2014 .

[2]  S. G. Ponnambalam,et al.  Mobile robot path planning using ant colony optimization , 2009, 2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics.

[3]  M.K.A. Ahamed Khan,et al.  Mobile robot path planning using Ant Colony Optimization , 2016, 2016 2nd IEEE International Symposium on Robotics and Manufacturing Automation (ROMA).

[4]  Chin-Teng Lin,et al.  A Hybrid of Cooperative Particle Swarm Optimization and Cultural Algorithm for Neural Fuzzy Networks and Its Prediction Applications , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[5]  Max Q.-H. Meng,et al.  An efficient neural network approach to dynamic robot motion planning , 2000, Neural Networks.

[6]  Feng Qian,et al.  A new particle swarm optimization and the application in the soft sensor modeling , 2010, IEEE ICCA 2010.

[7]  Ching-Chih Tsai,et al.  Parallel Elite Genetic Algorithm and Its Application to Global Path Planning for Autonomous Robot Navigation , 2011, IEEE Transactions on Industrial Electronics.

[8]  Rosli Syauqi Path Planning For Mobile Robot Navigation , 2007 .

[9]  Huang Xin An Improved Ant Colony Algorithm For Mobile Robot Path Planning , 2008 .

[10]  Inés María Galván,et al.  AMPSO: A New Particle Swarm Method for Nearest Neighborhood Classification , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[11]  Peter J. Angeline,et al.  Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences , 1998, Evolutionary Programming.

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

[13]  Oscar Castillo,et al.  Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation , 2009, Appl. Soft Comput..

[14]  Chang-Hwan Im,et al.  Multimodal function optimization based on particle swarm optimization , 2006, IEEE Transactions on Magnetics.