遥操作物理人机交互的运动重瞄准与控制

Akshit Kaplish, K. Yamane
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引用次数: 6

摘要

在本文中,我们提出了用于遥操作物理人机交互(pHRI)的运动重定向和控制算法。我们采用单边远程操作,其中配备传感器的操作员与静态对象(如人体模型)交互,以提供运动和力参考。控制器以参考点、机器人当前状态和接触力为输入,输出关节力矩跟踪操作者的接触力,同时保持运动的表达式和风格。提出了一种结合运动重定向算法的分层优化方案,解决了由于不同的运动学参数和身体形状而导致的操作者和机器人接触状态的差异。我们使用预先录制的人类拥抱演示,在具有柔软皮肤和接触力传感器的双臂机器人上演示控制器的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Motion Retargeting and Control for Teleoperated Physical Human-Robot Interaction
In this paper, we present motion retargeting and control algorithms for teleoperated physical human-robot interaction (pHRI). We employ unilateral teleoperation in which a sensor-equipped operator interacts with a static object such as a mannequin to provide the motion and force references. The controller takes the references as well as current robot states and contact forces as input, and outputs the joint torques to track the operator's contact forces while preserving the expression and style of the motion. We develop a hierarchical optimization scheme combined with a motion retargeting algorithm that resolves the discrepancy between the contact states of the operator and robot due to different kinematic parameters and body shapes. We demonstrate the controller performance on a dual-arm robot with soft skin and contact force sensors using pre-recorded human demonstrations of hugging.
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