改进RRT算法在机器人路径规划中的应用

Application of Improved RRT Algorithm in Robot Path Planning

为了解决快速搜索随机树(Rapid-exploration Random Tree,RRT)算法在机器人路径规划中效率低、复杂度高、趋向性差等问题,提出了一种目标偏向性的改进RRT算法。首先,建立复杂的多障碍物环境模型,并利用KD-Tree算法将待规划空间进行多级分割;其次,在RRT算法的基础上引入变权重的人工势场法,算法的主要作用是实现避开障碍物和启发式搜索;最后,实现对RRT算法的改进。通过对改进的RRT算法进行仿真验证,结果表明:该算法缩短了路径规划的时间,减少采样点数目,使生成的路径更加平滑,更适用于机器人在多障碍物环境中的路径规划。

In order to solve the problems of low efficiency, high complexity and poor tendency of RRT algorithm in robot path planning, an improved RRT algorithm based on target bias was proposed. Firstly, a complex multi-obstacle environment model is established, and the space to be planned is divided into multiple levels by using KD-Tree algorithm;Then the artificial potential field method with variable weight is introduced on the basis of the RRT algorithm, and the main function of the algorithm is to realize obstacle avoidance and heuristic search;Thereby realizing the improvement of the RRT algorithm. The simulation results show that the improved RRT algorithm shortens the path planning time, reduces the number of sampling points, and makes the generated path smoother, which is more suitable for robot path planning in multi-obstacle environment.