煤矿救援机器人机械臂轨迹规划算法研究

韩涛; 李静; 黄友锐; 徐善永; 许家昌

doi:10.13272/j.issn.1671-251x.17844

煤矿救援机器人机械臂轨迹规划算法研究

doi: 10.13272/j.issn.1671-251x.17844

安徽理工大学电气与信息工程学院, 安徽淮南 232001

基金项目:

安徽省重点研究与开发计划项目（202104g01020012）；安徽省教育厅自然科学研究重点项目（KJ2019A0110）。

详细信息

作者简介:
韩涛(1984-),男,安徽淮南人,高级实验师,硕士,研究方向为计算机视觉和智能控制等,E-mail:than@aust.edu.cn。

中图分类号: TD774
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- 被引次数: 0
出版历程
- 收稿日期: 2021-09-08
- 修回日期: 2021-10-28
- 刊出日期: 2021-11-20

Research on trajectory planning algorithm of manipulator arm of coal mine rescue robot

School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China

摘要

摘要: 针对煤矿井下复杂环境中救援机器人机械臂轨迹规划不合理、规划方法收敛速度慢等问题，提出了一种基于融合杜鹃搜索的灰狼优化(CS-GWO)算法的煤矿救援机器人机械臂轨迹规划算法。以五次多项式插值为基本轨迹规划方法，在机械臂关节空间进行轨迹规划，通过CS-GWO算法对得到的轨迹进行优化，实现机械臂时间-能量最优轨迹规划。CS-GWO算法在灰狼优化(GWO)算法的位置更新方式中融入杜鹃搜索(CS)算法的2次扰动过程，结合CS算法的莱维飞行模式和鸟巢位置随机更新的特点，使得狼群在向猎物逼近的过程中能够随机跳出局部搜索区域，扩大了搜索范围，避免算法陷入局部最优解，增强了GWO算法的全局搜索能力。Matlab仿真结果表明，CS-GWO算法能够有效提高CS算法的收敛速度和GWO算法的全局搜索能力，其稳定性更好，整体性能较优；利用机械臂轨迹规划算法可得到一条时间-能量最优轨迹，各关节角位移、角速度、角加速度曲线均光滑、连续，有效解决了煤矿井下复杂环境中救援机器人机械臂最优轨迹规划问题。
- 煤矿救援机器人 /
- 机械臂 /
- 轨迹规划 /
- 关节角轨迹 /
- 五次多项式插值 /
- 灰狼优化算法 /
- 杜鹃搜索算法
Abstract: In order to solve the problems of unreasonable trajectory planning of rescue robot manipulator arm and slow convergence speed of the planning method in the complex environment of coal mines, a trajectory planning algorithm of manipulator arm of coal mine rescue robot based on grey wolf optimization with cuckoo search(CS-GWO)is proposed.With the quintic polynomial interpolation as the basic trajectory planning method, the trajectory planning is carried out in the manipulator arm joint space, and the obtained trajectory is optimized by the CS-GWO algorithm to realize the time-energy optimal trajectory planning of the manipulator arm.The CS-GWO algorithm integrates the two perturbation process of the cuckoo search(CS)algorithm into the position update method of the grey wolf optimization(GWO)algorithm.Combined with the Lévy flight mode of the CS algorithm and the characteristics of the random update of the nest position, the algorithm enables the wolves to randomly jump out of the local search area in the process of approaching the prey, expands the search range, avoids the algorithm from falling into the local optimal solution, and enhances the GWO algorithm's global search capability.Matlab simulation results show that the CS-GWO algorithm can improve the convergence speed of the CS algorithm and the global search capability of the GWO algorithm effectively, with better stability and better overall performance.The use of the manipulator arm trajectory planning algorithm can obtain a time-energy optimal trajectory.The curves of angular displacement, angular velocity, and angular acceleration of each joint are smooth and continuous, which solves the optimal trajectory planning problem of manipulator arm of rescue robot in the complex environment of coal mines effectively.
- coal mine rescue robot /
- manipulator arm /
- trajectory planning /
- joint angle trajectory /
- quintic polynomial interpolation /
- grey wolf optimization algorithm /
- cuckoo search algorithm

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