The control of robots with a compliant joint motion is important for reducing collision forces and improving safety during human robot interactions. In this paper, a multi-hierarchy control framework is proposed for the redundant robot to enable the robot end-effector to physically interact with the unknown environment, while providing compliance to the joint space motion. To this end, an impedance learning method is designed to iteratively update the stiffness and damping parameters of the end-effector with desired performance. In addition, based on a null space projection technique, an extra low stiffness impedance controller is included to improve compliant joint motion behaviour when interaction forces are acted on the robot body. With an adaptive disturbance observer, the proposed controller can achieve satisfactory performance of the end-effector control even with the external disturbances in the joint space. Experimental studies on a 7 DOF Sawyer robot show that the learning framework can not only update the target impedance model according to a given cost function, but also enhance the task performance when interaction forces are applied on the robot body.

中文翻译：

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基于阻抗学习的冗余机器人多层次交互控制

控制具有柔顺关节运动的机器人对于减少碰撞力和提高人类机器人交互过程中的安全性非常重要。在本文中，为冗余机器人提出了一种多层次控制框架，使机器人末端执行器能够与未知环境进行物理交互，同时提供对关节空间运动的顺应性。为此，设计了一种阻抗学习方法，以迭代更新具有所需性能的末端执行器的刚度和阻尼参数。此外，基于零空间投影技术，包括一个额外的低刚度阻抗控制器，以改善当相互作用力作用在机器人身体上时的柔性关节运动行为。使用自适应扰动观测器，即使在关节空间存在外部干扰的情况下，所提出的控制器也能达到令人满意的末端执行器控制性能。对 7 自由度 Sawyer 机器人的实验研究表明，学习框架不仅可以根据给定的成本函数更新目标阻抗模型，还可以在相互作用力作用于机器人本体时提高任务性能。