Robot Path Planning Using an Improved Genetic Algorithm with Ordered Feasible Subpaths
Xianfeng Tan, Deming Lei, Dongrui Wu, Zheng Li · 2018
This paper proposes an improved genetic algorithm to enhance the search efficiency and robustness of robot path planning. Its three main contributions are: 1)initialization: free grids are divided into multiple sets according to the main diagonal direction, and all feasible subpaths in each set are found. From a given starting point, feasible subpaths are randomly selected in each set and orderly connected to reach the end point. This guarantees that each initial path is feasible. 2)mutation: a new mutation operator is proposed to ensure the generated paths are feasible. 3)simplification: a simplification operator is proposed to shorten the path while maintaining its feasibility. Experimental results demonstrate the speed and performance of the proposed algorithm.