预规划最优快速探索随机树

Preplanned Rapidly-exploring Random Tree Star

移动机器人的路径规划在应急救援、自主开采、仓库管理等各个领域起到了重要的作用。提出一种基于预规划抽样的路径规划方法,对于多障碍物环境下进行路径规划有良好的表现。该算法由预先路径生成过程和路径重规划过程组成。使用最优快速探索随机树(RRT^(*))算法获得初始路径作为先验知识,然后在此基础上通过蚁群算法重新优化规划得出最优路径。相比直接使用RRT^(*)算法路径长度更短,路径平滑度更好。

The path planning of mobile robots plays an important role in emergency rescue,autonomous mining,warehouse management and other fields.This paper proposes a path planning method based on pre-planned sampling,which performs well in path planning for environments with multiple obstacles.The algorithm consists of a pre-path generation process and a path re-planning process.It utilizes the Optimal Rapidly-exploring Random Tree(RRT^(*))algorithm to generate an initial path as prior knowledge.Then,it further optimizes the planning by applying the Ant Colony Algorithm on this basis,resulting in the optimal path.Compared to directly using the RRT^(*) algorithm,this approach yields shorter path length and smoother trajectory.