RRT算法路径优化及仿真验证

RRT algorithm path optimization based on MATLAB simulation

针对RRT算法在路径规划过程中搜索路径质量不高、搜索代价过大、迭代效率慢等问题,提出了改进的RRT算法,建立约束范围内的父节点重选策略和随机树剪枝方法,并基于三角形原理和最小转弯半径约束对路径进行优化,最后由Matlab仿真平台对改进的RRT算法进行仿真验证,并与经典RRT算法及A^(*)引导RRT算法的迭代时长和路径长度进行对比。实验结果表明:改进后的RRT算法可以在保持较高的搜索效率的同时规划出路径更短、更平滑的安全路径。

Aiming at the problems of a low search path quality,a high search cost and a slow iteration efficiency of Rapid-exploration Random Tree(RRT)algorithm in the process of path planning,this paper proposes an improved RRT algorithm.This algorithm carries out a parent node re-selection strategy within the constraint range and a pruning method for random trees.Besides,the path is optimized based on the triangle principle and the constraint of minimum vehicle turning radius.Finally,the improved RRT algorithm is simulated and verified by MATLAB simulation platform,and the research results like the iteration time and path length are compared with those conducted under the classic RRT algorithm or A^(*)-guided RRT algorithm.The experiment results show that,after algorithm optimization,the improved RRT algorithm can plan a shorter and smoother safe path while maintaining a high search efficiency.