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一种具有预规划策略的六自由度机械手的改进关节空间A星算法。

An improved joint space Astar algorithm for a 6-DOF manipulator with pre-planning strategy.

作者信息

He Chuangxin, Lian Shuaikai, Shi Qian, Miao Zhonghua

机构信息

School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China.

出版信息

Sci Rep. 2025 May 25;15(1):18164. doi: 10.1038/s41598-025-01010-5.

DOI:10.1038/s41598-025-01010-5
PMID:40414939
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12104326/
Abstract

Efficiency in manipulator path planning and obstacle avoidance represents a crucial technological challenge in manipulator research. Traditional path planning methods have certain limitations when dealing with the obstacle avoidance for links of manipulator, such as large calculational amount or unstable planned paths. To address this, this paper presents an improved joint space Astar algorithm for a 6 degree of freedom (DOF) manipulator with a pre-planning strategy. Firstly, we propose a pre-planning strategy that can refresh start positions and target positions to reduce computational load. Next, we perform a three-dimensional Astar path planning method in joint space, which avoids numerous inverse kinematics computations and singularities, and reduces the amount of high-dimensional calculations. In addition to the obstacle avoidance for manipulator end-effector, we also realize the obstacle avoidance for manipulator link. In simulation and experiments, the effectiveness of the proposed path planning algorithm is validated and the path tends to be optimal compared to the conventional Astar, RRT and Dijkstra algorithms.

摘要

机械手路径规划与避障的效率是机械手研究中的一项关键技术挑战。传统路径规划方法在处理机械手连杆避障时存在一定局限性，如计算量过大或规划路径不稳定。为解决这一问题，本文提出了一种针对具有预规划策略的6自由度机械手的改进关节空间Astar算法。首先，我们提出一种预规划策略，该策略可以刷新起始位置和目标位置以减轻计算负担。接下来，我们在关节空间中执行三维Astar路径规划方法，该方法避免了大量的逆运动学计算和奇异性，并减少了高维计算量。除了机械手末端执行器的避障，我们还实现了机械手连杆的避障。在仿真和实验中，所提出的路径规划算法的有效性得到了验证，并且与传统的Astar、RRT和Dijkstra算法相比，该路径趋于最优。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7970/12104326/dd44f0de54e2/41598_2025_1010_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7970/12104326/a14d627be208/41598_2025_1010_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7970/12104326/d976f41c37ae/41598_2025_1010_Figa_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7970/12104326/2eb5ff79b395/41598_2025_1010_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7970/12104326/af4a7d503ef3/41598_2025_1010_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7970/12104326/af1f4e822f95/41598_2025_1010_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7970/12104326/611c0469ec3c/41598_2025_1010_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7970/12104326/c182d2eded6a/41598_2025_1010_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7970/12104326/dd44f0de54e2/41598_2025_1010_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7970/12104326/a14d627be208/41598_2025_1010_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7970/12104326/93abef55ec4d/41598_2025_1010_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7970/12104326/43e44a28884a/41598_2025_1010_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7970/12104326/df766ff1b9f4/41598_2025_1010_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7970/12104326/d976f41c37ae/41598_2025_1010_Figa_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7970/12104326/c3ad19d953b8/41598_2025_1010_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7970/12104326/2eb5ff79b395/41598_2025_1010_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7970/12104326/af4a7d503ef3/41598_2025_1010_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7970/12104326/af1f4e822f95/41598_2025_1010_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7970/12104326/611c0469ec3c/41598_2025_1010_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7970/12104326/c182d2eded6a/41598_2025_1010_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7970/12104326/dd44f0de54e2/41598_2025_1010_Fig11_HTML.jpg

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本文引用的文献

1
Path Planning for Multi-Arm Manipulators Using Deep Reinforcement Learning: Soft Actor-Critic with Hindsight Experience Replay.使用深度强化学习的多臂机械臂路径规划：带有后见之明经验回放的软动作-评论家。
Sensors (Basel). 2020 Oct 19;20(20):5911. doi: 10.3390/s20205911.