Genetic Algorithm Using Sub-path Codes for Mobile Robot Path Planning


In this paper, a new method for finding global optimal path planning isproposed using a Genetic Algorithm (GA). A map of known static environment as wellas a start node and a target node connecting an optimal path which is required to befound are given beforehand. The chosen nodes in a known static environment areconnected by sub-paths among each other. Each path is represented by a series of subpathswhich connect the sequential nodes to form this path. Each sub-path radiatingfrom each node is labeled by an integer. The chromosome code of a path is a string ofseries integers that represent the labels of sub-paths which are passed through travelingfrom start node to target node. Two factors are integrated into a fitness function of theproposed genetic algorithm: the feasibility of collision avoidance path and the shortestdistance of path. Two examples of known static environment maps are taken in thisstudy with different numbers of obstacles and nodes. Simulation results show theeffectiveness and feasibility of the proposed GA using sub-path codes to find optimumpath planning for mobile robot.