Hubei University of Technology
针对传统RRT*全局路径规划算法在多障碍物复杂环境中搜索效率低、占用内存过大、搜索的路径不平滑等问题，本文提出了一种基于简化地图的区域采样RRT*算法(Simplified Map-based Regional Sampling RRT*, SMRS-RRT*)， 首先简化处理全局栅格地图，在简化后的全局栅格地图的基础上寻找到从起点到目标点的最优路径点集合，并将该路径作为引导路径，通过智能采样因子扩大引导路径，得到智能采样区域，不断在智能采样区域中迭代搜索，得到一条从起点到目标点的代价小、无碰撞路径，最后结合最小转弯半径约束的路径修剪和基于B样条曲线的路径优化，生成一条路径平滑且曲率连续的优化路径，从而使移动机器人沿着该全局优化路径快速、平稳、安全的到达目标点。仿真实验表明，该算法有效的提高了传统RRT*搜索效率、加快了收敛速度、降低了内存消耗。
Application of the traditional global path planning algorithm RRT* would result in low search efficiency, high memory usage and unsmooth search path. This research proposed a simplified map-based regional sampling RRT* algorithm (SMRS-RRT*) to overcome the abovementioned problems. First, the global grid map was simplified and used to identify the optimal path point set from the starting point to the target point. In addition, intelligent sampling factors were used to expand the guide path for the intelligent sampling area. After iterative search in the intelligent sampling area, an optimized path can be achieved which is a low cost and collision-free path from the starting point to the target point. Finally, based on the path trimming under the minimum turning radius constraint and the B sample curve, a smooth path with continuous curve were generated. Therefore, the mobile robot can move to the target point quickly, smoothly and safely along the global optimized path. The results from simulation experiments demonstrated that the proposed algorithm can effectively improve the efficiency of the traditional RRT*, speed up the convergence speed, and reduce memory usage.