基于改进模糊自适应遗传算法的移动机器人路径规划

针对现有移动机器人路径规划方法运行效率低的问题，提出一种基于改进模糊自适应遗传算法的路径规划方法。基于领域知识对初始路径进行可行性筛选，提高可行路径比例。采用模糊逻辑控制器动态整定遗传算法运行参数，提高路径寻优速度，避免陷入局部最优路径；综合考虑机器人运行安全性要求，引入余弦函数平滑度评价因子，对不同的路径夹角施以不同的惩罚项，以改善路径平滑度。仿真结果验证了改进算法解决路径规划问题的有效性。

Mobile Robot Path Planning Based on Improved Fuzzy Adaptive Genetic Algorithm

Aiming at the low efficiency of existing mobile robot path planning methods, a path planning method based on improved fuzzy adaptive genetic algorithm was proposed. Based on domain knowledge, the feasibility of the initial path was screened to improve the proportion of feasible paths. Fuzzy logic controller was used to dynamically adjust the running parameters of genetic algorithm to improve the speed of path optimization and avoid falling into the local optimal path; the operation safety requirements of the robot was considered comprehensively, the cosine function smoothness evaluation factor was introduced, and different penalties were applied to different path angles to improve path smoothness. The simulation results show that the improved algorithm is effective to solve the path planning problem.