Global path planning for lunar rover based on Particle Swarm Optimization algorithm
Song Peng, Jia Yang · 2011
The Particle Swarm Optimization algorithm is improved and used in the global navigation points planning, to get the global path in the remote control system of the lunar rover. In the improved algorithm, the velocity inertial weight is deleted, but the self and society cognitive coefficients are kept, with the aim to make the algorithm converge quickly in path planning. Also the variation coefficient in genetic algorithm is introduced to enhance the global optimization ability. Simulation results prove the improved algorithm is simple and has good capability to find the best path. Also simulation tests are done in several different lunar terrain maps, and optimization methods are given to make the planning result better.