面向制造环境的工业机器人节能轨迹规划

doi:10.13973/j.cnki.robot.200489

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赵俊宇, 张平, 李方, 陈昕叶. 面向制造环境的工业机器人节能轨迹规划[J]. 机器人, 2021, 43(6): 653-663.DOI: 10.13973/j.cnki.robot.200489. ZHAO Junyu, ZHANG Ping, LI Fang, CHEN Xinye. Energy Saving Trajectory Planning for Industrial Robot in Manufacturing Environment. ROBOT, 2021, 43(6): 653-663. DOI: 10.13973/j.cnki.robot.200489.

摘要在制造环境中，工业机器人节能轨迹规划的实际应用存在2个问题：机器人动力学参数未知；现有节能轨迹规划方法无法保证结果的稳定性．因此，本文提出了面向制造环境的工业机器人节能轨迹规划，包括基于平行BP（backpropagation）神经网络的近似动力学辨识和基于凸优化（CO）的节能轨迹求解法．以UR3机器人为实验平台，近似动力学模型的均方根误差（RMSE）可收敛至2.05×10^-3 N.m；且凸优化轨迹规划的求解稳定性优于现有的参数化轨迹规划．实验结果表明：本文提出的节能轨迹规划方案，能应对制造环境中机器人动力学参数未知的情况，同时保证轨迹规划结果的稳定性，因此更适用于制造环境中的工业机器人．

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关键词 ：智能制造, 工业机器人, 节能, 轨迹规划, 动力学辨识, 神经网络, 凸优化

Abstract：In the manufacturing environment, there are two problems in the application of energy saving trajectory planning for industrial robot:one is that the robot dynamic parameters are unknown, and the other is that the existing energy saving trajectory planning methods can't guarantee the stability of the results. Therefore, an energy saving trajectory planning for industrial robot in manufacturing environment is proposed, including approximate dynamic identification based on parallel BP (backpropagation) neural networks, and energy saving trajectory solution based on convex optimization (CO). Taking UR3 robot as the experimental platform, the RMSE (root mean squared error) of the approximate dynamic model can converge to 2.05×10^-3 N. m, and the solution stability of convex optimization based trajectory planning is better than the existing parametric trajectory planning. The experimental results show that the proposed energy saving trajectory planning scheme can deal with the problem of unknown robot dynamic parameters in the manufacturing environment, while ensuring the stability of the trajectory planning results, so it is more applicable for industrial robots in the manufacturing environment.

Key words： intelligent manufacturing industrial robot energy saving trajectory planning dynamic identification neural network convex optimization

收稿日期: 2020-11-25 录用日期: 2021-06-24

TP241.2

基金资助:广东省重点领域研发计划（2019B090915002）；国家重点研发计划（2018YFB1700500）；中央高校基本科研业务费

通讯作者: 张平,pzhang@scut.edu.cn E-mail: pzhang@scut.edu.cn

作者简介: 赵俊宇(1995-),男,硕士.研究领域:智能机器人技术.
张平(1964-),男,博士,教授.研究领域:人工智能,机器人,无人智能系统,人机交互技术.
李方(1981-),女,博士,副教授.研究领域:智能机器人技术,信息物理融合技术,嵌入式技术.

链接本文:

https://robot.sia.cn/CN/10.13973/j.cnki.robot.200489 或 https://robot.sia.cn/CN/Y2021/V43/I6/653

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