基于RRT 优化算法的移动机器人路径规划

针对快速扩展随机树算法(rapidly-exploring random trees，RRT)存在的不足，以移动机器人的研究为背景，对RRT 算法进行改进优化。依据RRT 算法扩展原理，构建仿真环境地图，通过完成基于概率P 的RRT 优化算法和基于双向RRT 优化算法仿真实验，将这2 种优化算法结合，提出了基于概率P-双向的RRT 优化算法，并进行了仿真实验验证。仿真结果表明，经过优化后的RRT 算法在复杂障碍环境中有着良好的收敛效果和鲁棒性。

Path Planning Based on RRT Optimization Algorithm for Mobile Robot

Aiming at the shortcomings of the rapid exploration of random tree (RRT), the RRT algorithm is improvedand optimized based on the research of mobile robot. According to the extension principle of the RRT, the simulationenvironment map is constructed, and the simulation experiments of the RRT optimization method based on probability Pand based on bidirectional RRT are completed. A probabilistic P-bidirectional RRT optimization algorithm is proposed bycombining 2 methods, and verified by simulation experiments. The simulation results show that the optimized RRTalgorithm has good convergence and robustness in complex obstacle environments.