IEEE Trans Cybern. 2020 Aug;50(8):3740-3751. doi: 10.1109/TCYB.2019.2933019. Epub 2019 Aug 30.
For human-robot co-manipulation by robotic exoskeletons, the interaction forces provide a communication channel through which the human and the robot can coordinate their actions. In this article, an optimization approach for reshaping the physical interactive trajectory is presented in the co-manipulation tasks, which combines impedance control to enable the human to adjust both the desired and the actual trajectories of the robot. Different from previous studies, the proposed method significantly reshapes the desired trajectory during physical human-robot interaction (pHRI) based on force feedback, without requiring constant human guidance. The proposed scheme first formulates a quadratically constrained programming problem, which is then solved by neural dynamics optimization to obtain a smooth and minimal-energy trajectory similar to the natural human movement. Then, we propose an adaptive neural-network controller based on the barrier Lyapunov function (BLF), which enables the robot to handle the uncertain dynamics and the joint space constraints directly. To validate the proposed method, we perform experiments on the exoskeleton robot with human operators for co-manipulation tasks. The experimental results demonstrate that the proposed controller could complete the co-manipulation tasks effectively.
对于机器人外骨骼的人机协作操作，交互力提供了一个通信通道，通过该通道，人类和机器人可以协调他们的动作。在本文中，提出了一种在协作任务中重塑物理交互轨迹的优化方法，该方法结合了阻抗控制，使人类能够调整机器人的期望轨迹和实际轨迹。与以前的研究不同，所提出的方法在基于力反馈的物理人机交互 (pHRI) 期间显著重塑期望轨迹，而不需要持续的人为指导。该方案首先形式化一个二次约束规划问题，然后通过神经动力学优化来解决该问题，以获得类似于自然人体运动的平滑和最小能量轨迹。然后，我们提出了一种基于障碍李雅普诺夫函数 (BLF) 的自适应神经网络控制器，该控制器使机器人能够直接处理不确定的动力学和关节空间约束。为了验证所提出的方法，我们在具有人类操作员的外骨骼机器人上进行了协作操作任务的实验。实验结果表明，所提出的控制器可以有效地完成协作任务。
