A Self-Adaptive Robot Control Framework for Improved Tracking and Interaction Performances in Low-Stiffness Teleoperation

Adriano Scibilia, Marco Laghi, E. Momi, L. Peternel, A. Ajoudani
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引用次数: 4

Abstract

The improved adaptability of a robotic teleoperation system to unexpected disturbances in remote environments can be achieved by compliance control. Nevertheless, complying with all types of interaction forces while performing realistic manipulation tasks may deteriorate the teleoperation performance. For instance, the loading effect of the objects and tools that are held and manipulated by the robot can introduce undesired deviations from the reference trajectories in case of low-stiffness (or high payload) teleoperation. Although this can be addressed by updating the robot dynamics with the external loading effect, a sudden loss of the object may also generate undesired and potentially dangerous robot behaviours. To address this problem, we propose a novel and self-adaptive teleoperation framework. The method uses the feedback from robot's force sensors to recognize the interaction aspects that must be compensated by robot dynamics. Thanks to this online compensation, the slave robot reduces the tracking error with respect to the commanded motion by the human operator, while performing complex interactive tasks without the haptic feedback. The robot local controller also includes an energy tank based passivity paradigm to be able to manage unexpected collisions or a contact loss without resulting in an unsafe behaviour. We validate the proposed approach by experiments on a torque-controlled robotic arm performing manipulation tasks that require both object manipulation and environment Interaction.
一种改进低刚度遥操作跟踪与交互性能的自适应机器人控制框架
顺应性控制可以提高机器人远程操作系统对远程环境中意外干扰的适应性。然而,在执行现实操作任务时,遵守各种类型的交互力可能会降低遥操作的性能。例如,在低刚度(或高载荷)遥操作的情况下,机器人持有和操纵的物体和工具的载荷效应可能会引入与参考轨迹的不期望偏差。虽然这可以通过使用外部负载效应更新机器人动力学来解决,但突然丢失物体也可能产生不希望的和潜在危险的机器人行为。为了解决这一问题,我们提出了一种新颖的自适应远程操作框架。该方法利用机器人力传感器的反馈来识别必须由机器人动力学补偿的交互方面。由于这种在线补偿,从机器人在执行复杂的交互式任务时,可以在没有触觉反馈的情况下减少对人类操作员指令运动的跟踪误差。机器人局部控制器还包括一个基于能量罐的被动模式,能够管理意外碰撞或接触丢失,而不会导致不安全的行为。我们通过力矩控制机械臂的实验验证了所提出的方法,该机械臂执行既需要物体操作又需要环境交互的操作任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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