Mobile Robot Path Planning with η 3 – Splines Using Spatial - Fitness - Sharing Variable - length Genetic Algorithm

This paper presents a novel evolutionary G–continuous (continuous-differentiable curvature) path planner for nonholonomic wheeled mobile robots. The evolutionary path planner generates intermediate configurations connected via η-splines that yields a collision-free G–continuous path, which each point on the path has a closed-form expression. The path planner is implemented as architecture of island parallel genetic algorithm (IPGA) running a variable-length genetic algorithm in each island. The techniques of spatial fitness-sharing in search space and a crowded measure of generated paths are integrated in the planner to implement a high-diversity evolutionary optimizer of paths that could self-adjust the spacing and number of intermediate configurations. The experimental result demonstrates the robustness and self-adjusting capability of evolutionary path planner in discovering shorter and smoother composite η -splines paths in complex environments.