Model Predictive Control (MPC) schemes have proven their efficiency in controlling high degree-of-freedom (DoF) complex robotic systems. However, they come at a high computational cost and an update rate of about tens of hertz. This relatively slow update rate hinders the possibility of stable haptic teleoperation of such systems since the slow feedback loops can cause instabilities and loss of transparency to the operator. This work presents a novel framework for transparent teleoperation of MPC-controlled complex robotic systems. In particular, we employ a feedback MPC approach and exploit its structure to account for the operator input at a fast rate which is independent of the update rate of the MPC loop itself. We demonstrate our framework on a mobile manipulator platform and show that it significantly improves haptic teleoperation's transparency and stability. We also highlight that the proposed feedback structure is constraint satisfactory and does not violate any constraints defined in the optimal control problem. To the best of our knowledge, this work is the first realization of the bilateral teleoperation of a legged manipulator using a whole-body MPC framework.
翻译:模型预测控制(MPC)计划在控制高度自由(DoF)复杂的机器人系统方面已证明了它们的效率,然而,它们的计算成本高,更新率大约为数十赫兹。这种相对缓慢的更新率妨碍了这些系统稳定顺畅的远程操作的可能性,因为缓慢的反馈循环可能会给操作者造成不稳定和失去透明度。这项工作为MPC控制的复杂机器人系统透明的远程操作提供了一个新的框架。特别是,我们采用了反馈的MPC方法,并利用其结构以与MPC环本身更新率无关的快速速度对操作者的投入进行核算。我们展示了我们在一个移动操纵平台上的框架,并表明它大大改善了顺畅的远程操作的透明度和稳定性。我们还强调指出,拟议的反馈结构是令人满意的,并不违反最佳控制问题中界定的任何限制。据我们所知,这项工作是利用一个全机体的MPC框架,首次实现一个腿操纵者的双边远程操作。