复杂环境下的改进RRT算法路径规划

Improved RRT algorithm path planning in complex environment

针对快速扩展随机树算法(rapidly-exploring trees,RRT)在一些复杂环境中存在搜索效率低、收敛速度慢、生成的路径冗余节点多等问题,提出一种改进的RRT算法。首先引入自适应目标概率策略,实时调整对目标点的采样概率;其次引入节点转向策略,提高单次采样的成功率;最后对生成的路径进行冗余节点裁剪,使路径更符合实际应用需求。在MATLAB中进行仿真实验,并与RRT算法、RRTGoalBias算法进行对比。实验结果表明,改进算法在多种不同环境下具有较好的适应性,在寻路时间、采样次数和采样成功率3个方面均有较大提升,最终平均路径长路降低了21.1%,平均节点数降低了75.3%,证明了改进算法的优越性和实用性。

Aiming at the problems of rapidly exploring trees(RRT)in some complex environments,such as low search efficiency,slow convergence rate and many redundant nodes,an improved RRT algorithm is proposed.Firstly,the adaptive target probability strategy is introduced to adjust the sampling probability of the target point in real time.Secondly,node turning strategy is introduced to improve the success rate of single sampling.Finally,redundant nodes are clipped to the generated path to make the path more in line with the actual application requirements.Simulation experiments are carried out in MATLAB and compared with RRT algorithm and RRTGoalBias algorithm.The experimental results show that the improved algorithm has good adaptability in a variety of different environments,and the running time,sampling times and sampling success rate are greatly improved.The final average path length is reduced by 21.1%,and the average node number is reduced by 75.3%,which proves the superiority and practicability of the improved algorithm.