基于改进Informed-RRT^(*)的路径规划算法

Improved Informed-RRT^(*)Based Path Planning Algorithm

针对Informed-RRT^(*)算法在路径规划中存在盲目性、收敛速度慢和优化效率低等问题,提出了一种改进的Informed-RRT^(*)算法。首先,寻找初始路径时引入双向贪婪搜索,加快了初始路径寻找速率;其次,在树的生长过程中引入自适应步长代替固定步长进行生长,使得算法面对不同环境都能找到较优路径;最后,用懒惰采样代替原本的随机采样,在对算法进行处理前删除没有作用的节点,减小了算法运行压力,也加速了算法收敛。实验结果表明,面对复杂环境,优化后的算法能够快速找到较优路径。

An improved Informed-RRT^(*)algorithm is proposed to address the problems of blindness,slow convergence and low optimization efficiency of the Informed-RRT^(*)algorithm in path planning.First,a two-way greedy search is introduced when finding the initial path,which speeds up the initial path finding rate.Then,adaptive step size is introduced in the tree growth process instead of fixed step size for growth,so that the algorithm can find better paths in the face of different environments.Finally,lazy sampling is used instead of the original random sam-pling to remove the useless nodes before the algorithm is processed,which reduces the operational pressure of the algorithm and also speeds up the convergence of the algorithm.The experimental results show that the optimized algorithm can quickly find a better path in the face of the complex environment.