基于启发式的快速扩展随机树路径规划算法

Algorithm of Path Planning of Rapidly-Exploring Random Tree Based on Heuristics

针对基于随机采样的路径规划缺乏确定性的问题,提出一种具有启发式的多自由度机器人路径规划算法。该算法在快速扩展随机树算法的基础上,引入了启发式估价函数,使扩展随机树有利于朝目标点方向进行生长。仿真结果表明。提高了复杂环境下机器人路径规划的效率,保证了规划的路径接近于最短路径,对同一任务的规划具有一定的可重复性。

Owing to lack of certainty in path planning of random sampling, an algorithm of multi-degree of freedom robot path planning based on heuristics has been put forward. Based on the algorithm of rapidly-exploring random tree, this algorithm has introduced the heuristic evaluation function so that the exploring random tree wiLl grow in the direction of target point. The simulation result shows that the algorithm has improved the efficiency of robot path planning in complex environment and ensured that the planned path is almost the shortest path. There is certain repeatability for the planning of the same task.