Abstract This paper is concentrated on path planning for robots working in a dynamic environment to satisfy real-time needs. An efficient bias-goal factor RRT (EBG-RRT), which is multiple-query sampling-based replanning algorithm, is proposed with rapid response and high success rate. Specifically, a relay node method is proposed to get a position where the robot and dynamic obstacles will be no-collision and help robots to move without suspended. Based on the relay node method, Connection strategy performs minimal modifications to maintain the interrupted path. In order to overcome the short of Waypoint Cache method, an efficient and optimal Waypoint Cache (EOWC) method is proposed to make use of potential cache information and find an optimal path to repair. The EOWC method is combined with the BG-RRT algorithm according to the iterative characteristics. Finally, the EBG-RRT algorithm is verified on ROS with Aubo-i5 manipulator. Simulation results provide the EBG-RRT algorithm is outperformed both in static and dynamic environments.

中文翻译：

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一种在动态环境中进行路径规划的高效 RRT 缓存方法

摘要 本文重点研究在动态环境中工作的机器人的路径规划以满足实时需求。提出了一种高效的偏置-目标因子RRT（EBG-RRT），它是一种基于多查询采样的重新规划算法，具有快速响应和高成功率的特点。具体而言，提出了一种中继节点方法来获取机器人与动态障碍物不会发生碰撞的位置，并帮助机器人在不悬挂的情况下移动。基于中继节点方法，连接策略执行最少的修改以维护中断的路径。为了克服Waypoint Cache方法的不足，提出了一种高效优化的Waypoint Cache（EOWC）方法，利用潜在的缓存信息，寻找最佳修复路径。EOWC方法根据迭代特性与BG-RRT算法相结合。最后，EBG-RRT算法在带有Aubo-i5机械手的ROS上得到验证。仿真结果表明 EBG-RRT 算法在静态和动态环境中均表现出色。