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一种用于移动机械手的分层运动规划方法。

A Hierarchical Motion Planning Method for Mobile Manipulator.

作者信息

Chen Hanlin, Zang Xizhe, Liu Yubin, Zhang Xuehe, Zhao Jie

机构信息

State Key Laboratory of Robotics and Systems, Harbin Institute of Technology, Harbin 150001, China.

出版信息

Sensors (Basel). 2023 Aug 4;23(15):6952. doi: 10.3390/s23156952.

Abstract

This paper focuses on motion planning for mobile manipulators, which includes planning for both the mobile base and the manipulator. A hierarchical motion planner is proposed that allows the manipulator to change its configuration autonomously in real time as needed. The planner has two levels: global planning for the mobile base in two dimensions and local planning for both the mobile base and the manipulator in three dimensions. The planner first generates a path for the mobile base using an optimized A* algorithm. As the mobile base moves along the path with the manipulator configuration unchanged, potential collisions between the manipulator and the environment are checked using the environment data obtained from the on-board sensors. If the current manipulator configuration is in a potential collision, a new manipulator configuration is searched. A sampling-based heuristic algorithm is used to effectively find a collision-free configuration for the manipulator. The experimental results in simulation environments proved that our heuristic sampling-based algorithm outperforms the conservative random sampling-based method in terms of computation time, percentage of successful attempts, and the quality of the generated configuration. Compared with traditional methods, our motion planning method could deal with 3D obstacles, avoid large memory requirements, and does not require a long time to generate a global plan.

摘要

本文聚焦于移动机械手的运动规划,其中包括移动基座和机械手的规划。提出了一种分层运动规划器,它允许机械手根据需要实时自主改变其构型。该规划器有两个层级:二维的移动基座全局规划和三维的移动基座与机械手局部规划。规划器首先使用优化的A*算法为移动基座生成一条路径。当移动基座带着不变的机械手构型沿着路径移动时,利用从车载传感器获得的环境数据检查机械手与环境之间的潜在碰撞。如果当前机械手构型处于潜在碰撞中,则搜索新的机械手构型。使用一种基于采样的启发式算法来有效地找到机械手的无碰撞构型。在仿真环境中的实验结果证明,我们基于启发式采样的算法在计算时间、成功尝试百分比和生成构型的质量方面优于基于保守随机采样的方法。与传统方法相比,我们的运动规划方法能够处理三维障碍物,避免大量内存需求,并且不需要很长时间来生成全局规划。

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