Abstract Aiming at the problems of low exploration efficiency and high optimal solution cost for existing robot path planning methods, a robot path planning optimization method based on heuristic multi-directional rapidly-exploring tree is implemented. In high-dimensional configuration space, drawing on the Rapid-exploration Random Tree (RRT) related algorithm idea, directional sampling control module works under the guidance of the robot goal course as a heuristic exploration. A flexible multi-directional rapidly-exploring tree construction method is used due to a degree of directional instability. In accordance with the principle of the centripetal growth of the tree, new multi-directional trees will be built on demand to arrive at specific coverage of the space. Then based on the previous path exploration vertices, through the merging method of the trees, a closed loop path is formed and optimized to finally generate a relative optimal path. Simulation experiment results show that this method could effectively improve the exploring efficiency with low computational cost.

中文翻译：

---

基于启发式多向快速探索树的机器人路径规划优化方法

摘要 针对现有机器人路径规划方法探索效率低、优化求解成本高的问题，实现了一种基于启发式多向快速探索树的机器人路径规划优化方法。在高维配置空间中，借鉴Rapid-Exploration Random Tree（RRT）相关算法思想，定向采样控制模块在机器人目标路线的引导下作为启发式探索工作。由于一定程度的方向不稳定性，使用灵活的多方向快速探索树构建方法。按照树木向心生长的原则，根据需要新建多向树木，达到空间的特定覆盖范围。然后根据之前的路径探索顶点，通过树的合并方法，形成闭环路径并进行优化，最终生成相对最优的路径。仿真实验结果表明，该方法能够以较低的计算成本有效提高探索效率。