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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2021, Vol. 55 Issue (11): 2091-2099    DOI: 10.3785/j.issn.1008-973X.2021.11.009
    
Variable stiffiness control for human-robot cooperative transportation based on imitation learning
Zi-lin TANG1,2(),Xiao GAO1,2,Xiao-hui XIAO1,2,*()
1. School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China
2. National Key Laboratory of Human Factors Engineering, China Astronauts Research and Training Center, Beijing 100094, China
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Abstract  

Aiming at human-robot cooperative transportation, current control strategies are difficult to guarantee both the compliant control for cooperative transportation and the accuracy of the end point, and lack sufficient flexibility to different tasks. A variable stiffiness control strategy was proposed for cooperative transportation based on imitation learning. Firstly, several human demonstrations of cooperative transportation were encoded in task parameterized Gaussian mixture model (TP-GMM) and a probabilistic model of trajectories under different transport conditions was learned. Secondly, combined with admittance control, an interactive model of variable stiffness at the end of the manipulator was established for transportation to realize compliant control. Besides, a strategy of switching between different transportation tasks was developed with a threshold method of interaction force. Finally, a cooperative transportation platform was built for experiments. Results show that the proposed approach can improve the position accuracy of the end point to 1.9 mm in specific tasks while achieving compliant control for cooperative transportation, and ensure that the end effector of the robot in specific tasks is controlled to moved in desired space and the switching of transportation tasks is available.

Key words： human-robot cooperative transportation      imitation learning      variable stiffness      admittance control      compliant interaction     
Received: 27 October 2020      Published: 05 November 2021
CLC:  TP 24  
Fund:  人因工程国防科技重点实验室开放基金资助项目（6142222180311）；空间智能控制技术国防科技重点实验室开放基金资助项目（6142208180301）
Corresponding Authors: Xiao-hui XIAO     E-mail: yangbenbo@whu.edu.cn;xhxiao@whu.edu.cn
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Zi-lin TANG
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Cite this article:

Zi-lin TANG,Xiao GAO,Xiao-hui XIAO. Variable stiffiness control for human-robot cooperative transportation based on imitation learning. Journal of ZheJiang University (Engineering Science), 2021, 55(11): 2091-2099.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2021.11.009     OR     https://www.zjujournals.com/eng/Y2021/V55/I11/2091


基于模仿学习的变刚度人机协作搬运控制

针对人-机器人协作搬运，现有的控制策略难以同时保证搬运过程的柔顺性和搬运终点位置的精确性，而且对不同搬运任务适应性不够. 基于模仿学习提出变刚度协作搬运控制策略. 使用任务参数化的高斯混合模型(TP-GMM)对多次搬运示教数据进行编码，学习不同搬运工况下的搬运轨迹概率模型；结合导纳控制建立机械臂末端变刚度交互模型，实现柔性搬运操作，并基于交互力阈值实现不同搬运任务的切换；搭建协作搬运平台进行实验验证. 实验结果表明，提出的策略在实现柔性协作搬运的同时将特定搬运任务的终点位置精度提高到1.9 mm，且保证了特定搬运任务中机械臂末端在期望区域内运动以及搬运任务的切换.


关键词： 人机协作搬运,  模仿学习,  变刚度,  导纳控制,  柔性交互 
Fig.1 Flowchart of TP-GMM
Fig.2 Collection of teaching data of cooperative transportation
Fig.3 BIC curve in different number of Gaussian models
Fig.4 GMM in two different coordinate systems
Fig.5 GMR in two different coordinate systems
Fig.6 Trajectories generated in new end points
Fig.7 Probability model of trajectory
Fig.8 Coordinate systems of Gaussian distribution
Fig.9 Control block diagram of specific transportation tasks
Fig.10 Diagram of probability distribution of mutual force in cooperative transportation
Fig.11 Flow chart of cooperative transportation
Fig.12 Experiment scene of human-robot cooperative transportation
Fig.13 Trajectories corresponding to variable stiffness in ellipsoid of Gaussian distribution
Fig.14 Stiffness estimated by existing variable stiffness methods[12]
Fig.15 Trajectories of constant stiffness method and existing variable stiffness method
实验 终点位置偏差/m 轨迹平均刚度/（N·m?1）
本研究所提变刚度方法 0.001 9 540.48
现有变刚度方法[12] 0.284 2 158.71
${k_{\rm{p}}} = 100\;{{\rm{N/m}}}$ 0.100 8 107.78
${k_{\rm{p}}} = 1\;200\;{{\rm{N/m}}}$ 0.007 2 1 312.20
Tab.1 Effect contrast between variable stiffness method and const stiffness method
Fig.16 Trajectories corresponding to constant and variable force
Fig.17 Trajectory of end effector corresponding to transportation tast switch
Fig.18 Joint velocity of manipulator corresponding to transportation tast switch
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