基于融合Bezier优化遗传算法的路径规划

Path Planning Based on Genetic Algorithm Fused with Bezier Optimization

针对传统遗传算法(GA)在路径规划过程中解质量欠佳的问题,提出一种融合Bezier优化的遗传算法。该方法首先将贝塞尔(Bezier)曲线引入GA,以优化其初始及交叉、变异过程中产生的路径,消除尖峰拐点并减少冗余节点,从而提高路径平滑性;其次,通过在GA适应度函数中增加安全距离与自适应惩罚因子,以保障机器人移动过程中的安全性;最后,在栅格地图中进行移动机器人路径规划仿真实验,结果表明,与传统路径规划方法相比,所提算法能够搜索到一条距离更短且更光滑的路径。

Aiming at the problem of poor solution quality of traditional genetic algorithm(GA)in the path planning process,a algorithm called genetic algorithm fused with Bezier optimization(GA-B-Q)is proposed.Firstly,the Bezier curve is introduced into GA to optimize the path generated during initialization,crossover and mutation,and eliminate the peak inflection point and reduce the redundant nodes,thereby improving the smoothness of the planning path.Secondly,the safety distance and adaptive penalty factor are added into the fitness function of GA to ensure the safety of the moving robot.Finally,Experiment on the mobile robot path planning in a grid map demonstrates that the proposed algorithm can search for a shorter and smoother path than the traditional path planning methods.