基于人体肌肉激活疲劳模型的人机协作装配序列重规划研究

doi:10.3901/JME.2025.15.339

机械工程学报 ›› 2025, Vol. 61 ›› Issue (15): 339-350.doi: 10.3901/JME.2025.15.339

• 人机协作装配与调度 • 上一篇

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基于人体肌肉激活疲劳模型的人机协作装配序列重规划研究

姚碧涛¹, 向宇光¹, 徐文君^2,3, 叶勋^2,3, 周祖德¹, 王力翚⁴

1. 武汉理工大学机电工程学院武汉 430070;
2. 武汉理工大学信息工程学院武汉 430070;
3. 武汉理工大学宽带无线通信与传感器网络湖北省重点实验室武汉 430070;
4. 瑞典皇家理工学院斯德哥尔摩 SE-11428 瑞典

收稿日期:2025-01-08 修回日期:2025-04-09 发布日期:2025-09-28
作者简介:姚碧涛，男，1986年出生，博士，副教授，硕士研究生导师。主要研究方向为人机协作、机械装备状态监测等。E-mail：bitaoyao@whut.edu.cn;徐文君(通信作者)，男，1983年出生，博士，教授，博士研究生导师。主要研究方向为智能协同制造、数字孪生、工业智能、人机协作、工业互联网。E-mail：xuwenjun@whut.edu.cn
基金资助:
国家自然科学基金(92367108,52005376); 湖北省自然科学基金(2024AFB025); 湖北省青年拔尖人才培养计划资助项目。

Research on Human-robot Collaborative Assembly Sequence Replanning Based on the Human Muscle Activation Fatigue Model

YAO Bitao¹, XIANG Yuguang¹, XU Wenjun^2,3, YE Xun^2,3, ZHOU Zude¹, WANG Lihui⁴

1. School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070;
2. School of Information Engineering, Wuhan University of Technology, Wuhan 430070;
3. Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks, Wuhan University of Technology, Wuhan 430070;
4. KTH Royal Institute of Technology, Stockholm SE-11428, Sweden

Received:2025-01-08 Revised:2025-04-09 Published:2025-09-28

摘要/Abstract

摘要： 在人机协作装配过程中，长时间的重复性工作会导致人体出现肌肉疲劳，从而引起装配序列完成时间的波动，降低整体装配效率。因此，提升机器人对人当前疲劳状态的感知能力，实时评估装配任务中人的肌肉疲劳度，并对装配序列进行优化，对于提高装配效率和减轻人的工作负担具有重要意义。为此，基于人体肌肉激活疲劳模型，研究人机协作装配序列的重规划方法：首先，采集人在装配过程中的动作图像，并利用Open Pose提取动作数据，据此构建人的个性化骨骼肌模型；然后，使用OpenSim软件计算装配动作中的肌肉力变化，结合人体肌肉激活疲劳模型计算最受累肌肉执行装配动作时的疲劳指数，并利用实时获取的关节角变化数据，训练LSTM深度神经网络以快速通过实时关节角变化判断人的动作类别，从而可基于判断的动作类别实时得到人的动作疲劳指数；接下来，利用多目标遗传算法对总装配时间和总人体疲劳两个目标进行优化，并根据人体实时关节角得到人的动作类别以及对应动作的疲劳指数，当人体受累肌肉的疲劳积累时，基于实时监测的人的疲劳与动作耗时重新规划下一轮的装配序列，从而提高整体装配效率并减少人体疲劳。以人机协作装配齿轮减速箱为试验场景，开展了实时疲劳检测与装配序列重规划的试验。试验结果表明，所提出的方法能够识别人的肌肉疲劳，并有效重规划出能够减轻疲劳的装配序列，提升了人机协作装配效率并减轻了人体装配过程中的肌肉疲劳。

关键词: 人机协作装配, 人体肌肉激活疲劳模型, 序列重规划, 疲劳评估, 多目标遗传算法

Abstract: During human-robot collaborative assembly, prolonged repetitive work can lead to muscle fatigue in the human body, causing fluctuations in the completion time of assembly sequences and subsequently reducing overall assembly efficiency. Therefore, enhancing the robot’s ability to perceive the current fatigue state of the human, assessment of muscle fatigue during assembly in real-time and optimization of assembly sequences are of great significance for improving assembly efficiency and alleviating the human’s workload. To this end, this paper studies the replanning method for human-robot collaborative assembly sequences based on a muscle activation fatigue model. Firstly, motion images of the human during the assembly process are collected, and motion data are extracted using OpenPose to construct a personalized skeletal muscle model for the individual. Next, OpenSim is used to calculate the changes in muscle force during assembly actions, and the fatigue index of the most fatigued muscles executing the assembly actions is calculated in conjunction with the muscle activation fatigue model. Real-time joint angle variation data are utilized to train an LSTM deep neural network to quickly determine the action category of the human based on real-time joint angle, and then the fatigue index of the human’s real-time actions can be determined in real-time. Subsequently, a multi-objective genetic algorithm is employed to optimize the total assembly time and total human fatigue. Based on the real-time joint angle, the human action category and the corresponding fatigue index can be obtained. When the fatigue accumulates in the fatigued muscles, the assembly sequence in the next round is re-planned based on the real-time monitored human fatigue and action duration, thereby improving overall assembly efficiency and reducing human fatigue. The human-robot collaborative assembly of gear reducers is adopted as the experimental scenario, real-time fatigue detection and assembly sequence re-planning experiments are conducted. The experimental results indicate that the proposed method can identify muscle fatigue in humans and effectively re-plan assembly sequences that can alleviate fatigue, enhancing the efficiency of human-robot collaborative assembly and reducing muscle fatigue during the assembly.

Key words: human-robot collaborative assembly, human muscle activation fatigue model, sequence replanning, fatigue assessment, multi-objective genetic algorithm

中图分类号:

TP242

姚碧涛, 向宇光, 徐文君, 叶勋, 周祖德, 王力翚. 基于人体肌肉激活疲劳模型的人机协作装配序列重规划研究[J]. 机械工程学报, 2025, 61(15): 339-350.

YAO Bitao, XIANG Yuguang, XU Wenjun, YE Xun, ZHOU Zude, WANG Lihui. Research on Human-robot Collaborative Assembly Sequence Replanning Based on the Human Muscle Activation Fatigue Model[J]. Journal of Mechanical Engineering, 2025, 61(15): 339-350.

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基于人体肌肉激活疲劳模型的人机协作装配序列重规划研究

Research on Human-robot Collaborative Assembly Sequence Replanning Based on the Human Muscle Activation Fatigue Model

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