Human-Robot Collaboration for Unknown Flexible Surface Exploration and Treatment Based on Mesh Iterative Learning Control
Human-Robot Collaboration for Unknown Flexible Surface Exploration and Treatment Based on Mesh Iterative Learning Control
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DOI：
10.1109/iros55552.2023.10341612
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发表时间：
2023-10
期刊：
影响因子：
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通讯作者：
Jingkang Xia;Kithmi N. D. Widanage;Ruiqing Zhang;Rizuwana Parween;Hareesh Godaba;Nicolas Herzig;Romeo Glovnea;Deqing Huang;Yanan Li
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文献类型：
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作者：
Jingkang Xia;Kithmi N. D. Widanage;Ruiqing Zhang;Rizuwana Parween;Hareesh Godaba;Nicolas Herzig;Romeo Glovnea;Deqing Huang;Yanan Li
像打磨和抛光这样的接触式工具操作对机器人和自动化的需求很高，因为人工操作是劳动密集型的，质量不稳定。然而，自动化这些操作仍然是一个挑战，因为它们高度依赖于关于工件几何形状的先验知识。虽然在现有的研究中已经开发了几种方法来自动化几何学习过程和调整接触力，但在这些方法中，工件的标定和机器人运动的路径规划非常需要人工监督。而且，在这些方法中，大多数方法都没有考虑工件的刚度识别。本文提出了一种人-机器人协作(HRC)框架，它能够结合操作者的灵活性和机器人的控制精度对未知对象进行表面探测。操作者沿着目标物体的表面移动机器人，机器人识别表面几何形状和表面刚度，同时通过控制施加所需的接触力。为此，开发了一种网格迭代学习控制(MILC)来学习表面刚度，规划探索路径，并通过基于HRC的重复在线修正来调整接触力。学习收敛的证明、七自由度Sawyer机器人的仿真和实验结果证明了所提出控制器的有效性。
Contact tooling operations like sanding and polishing have been high in demand for robotics and automation, as manual operations are labour-intensive with inconsistent quality. However, automating these operations remains a challenge since they are highly dependent on prior knowledge about the geometry of the workpiece. While several methods have been developed in existing research to automate the geometry learning process and adjust the contact force, human supervision is heavily required in the calibration of workpieces and the path planning of robot motion in such methods. Furthermore, the stiffness identification of the workpiece is not considered in most of these methods. This paper presents a human-robot collaboration (HRC) framework, which is able to perform surface exploration on an unknown object combining the operator's flexibility with the control precision of the robot. The operator moves the robot along the surface of the target object, and the robot recognizes the surface geometry and surface stiffness while exerting a desired contact force through control. For this purpose, a mesh iterative learning control (MILC) is developed to learn the surface stiffness, plan the exploration path, and adjust contact force through repetitive online correction based on HRC. The proof of learning convergence and the results of the simulation and experiments performed using a 7-DOF Sawyer robot demonstrate the validity of the proposed controller.