| 基于深度循环神经网络的协作机器人动力学误差补偿 |
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| 作者姓名： | 徐征 张弓 汪火明 侯至丞 杨文林 梁济民 王建 顾星 |
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| 作者单位： | 1.广州中国科学院先进技术研究所机器人与智能装备中心，广州 511458 |
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| 基金项目： | 国家重点研发计划资助项目（2018YFA0902903）；国家自然科学基金资助项目（62073092）；广东省自然科学基金资助项目（2021A1515012638）；广州市基础研究计划资助项目（202002030320） |
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| 摘 要： | 由于协作机器人的结构比普通工业机器人更为轻巧，一般动力学模型所忽略的复杂特性占比较大，导致协作机器人的计算预测力矩误差较大。据此提出在考虑重力、科里奥利力、惯性力和摩擦力等的基础上，采用深度循环神经网络中的长短期记忆模型对自主研发的六自由度协作机器人动力学模型进行误差补偿。在实验中采用优化后的基于傅里叶级数的激励轨迹驱动机器人运动，以电机电流估算关节力矩，获取的原始数据用来训练长短期记忆模型(LSTM)补偿网络。网络的训练结果和评价指标为预测力矩相比实际力矩的均方根误差。计算与实验结果表明，补偿后的协作机器人动力学模型对实际力矩具有更好的预测效果，各轴预测力矩与实际力矩的均方根误差相比于未补偿的传统模型降低了61.8%至78.9%不等，表明了文中所提出补偿方法的有效性。
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| 关 键 词： | 协作机器人 动力学模型 模型误差补偿 循环神经网络 长短期记忆模型 |
| 收稿时间： | 2020-04-30 |
Error compensation of collaborative robot dynamics based on deep recurrent neural network |
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| Authors: | XU Zheng ZHANG Gong WANG Huo-ming HOU Zhi-cheng YANG Wen-lin LIANG Ji-min WANG Jian GU Xing |
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| Affiliation: | 1.Intelligent Robot & Equipment Center, Guangzhou Institute of Advanced Technology, Chinese Academy of Science, Guangzhou 511458, China2.School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China |
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| Abstract: | Establishing the dynamics model of robot and its parameters is significant for simulation analysis, control algorithm verification, and implementation of human–machine interaction. Especially under various working conditions, the errors of the calculated predicted torque of each axis have the most direct negative effect. The general robot dynamics model rarely takes the minor and complex characteristics into consideration, such as the reducer flexibility, inertia force of motor rotors, and friction. However, as the structure of collaborative robots is lighter and smaller than the ordinary industrial robots, the characteristics neglected by general dynamics models account for a relatively large amount. The above facts result in a large error in the calculation and prediction of collaborative robots analysis. To address the short comings of general robot dynamics model, a network based on long short-term memory (LSTM) in deep recurrent neural network was proposed. The network compensates the general dynamics model of a self-developed six-degree-of-freedom collaborative robot based on the consideration of gravity, Coriolis force, inertial force, and friction force. In the experiment, the nondisassembly experimental measurement combined with least-squares method was used to identify the parameters. The motor current was used to evaluate the joint torque instead of mounting an expensive and inconvenient torque sensor. The excitation trajectory based on the Fourier series was optimized. The raw experimental data were used to train the proposed LSTM network. About the accuracy of the dynamic model and the compensation method for the collaborative robot, the root-mean-square error of the calculated torque relative to the actual measured torque was used to train the network and evaluate the proposed method. The analysis and the results of the experiment show that the compensated collaborative robot dynamics model based on LSTM network displays a good prediction on the actual torque, and the root-mean-square error between predicted and actual torques is reduced from 61.8% to 78.9% compared to the traditional model, the effectiveness of the proposed error compensation policy is verified. |
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