Legged robots have shown remarkable advantages in navigating uneven terrain. However, realizing effective locomotion and manipulation tasks on quadruped robots is still challenging. In addition, object and terrain parameters are generally unknown to the robot in these problems. Therefore, this paper proposes a hierarchical adaptive control framework that enables legged robots to perform loco-manipulation tasks without any given assumption on the object's mass, the friction coefficient, or the slope of the terrain. In our approach, we first present an adaptive manipulation control to regulate the contact force to manipulate an unknown object on unknown terrain. We then introduce a unified model predictive control (MPC) for loco-manipulation that takes into account the manipulation force in our robot dynamics. The proposed MPC framework thus can effectively regulate the interaction force between the robot and the object while keeping the robot balance. Experimental validation of our proposed approach is successfully conducted on a Unitree A1 robot, allowing it to manipulate an unknown time-varying load up to $7$ $kg$ ($60\%$ of the robot's weight). Moreover, our framework enables fast adaptation to unknown slopes (up to $20^\circ$) or different surfaces with different friction coefficients.
翻译:然而,对于四重机器人,实现有效的移动和操控任务仍具有挑战性。此外,在这些问题中,机器人一般不知道物体和地形参数。因此,本文件提议了一个等级适应性控制框架,使腿机器人能够在不假定物体质量、摩擦系数或地形斜坡的情况下执行显形管理任务。在我们的方法中,我们首先提出适应性操纵控制,以调控接触力量,在未知地形上操纵未知物体。然后我们引入一个考虑到机器人动态中操纵力的统一模型管理控制(MPC),从而能够有效地调节机器人与物体之间的互动力,同时保持机器人平衡。我们对拟议方法的实验性验证在Uniteree A1机器人上成功进行,允许它操纵一个未知的时间分配负荷,最高达7美元(相当于机器人重量的60美元)。此外,我们的框架可以快速适应未知的斜坡度(相当于20美元/circ值的摩擦值)或不同的表面系数。</s>