论文标题
通过层次模型预测控制的非划分机车操作的接触优化
Contact Optimization for Non-Prehensile Loco-Manipulation via Hierarchical Model Predictive Control
论文作者
论文摘要
关于四倍机器人的最新研究集中在使用机器人臂的运动或移动操作上。腿部的机器人可以使用非划理操纵原语（例如平面推动）来操纵较重和较大的物体，以将对象驱动到所需的位置。在本文中，我们提出了一种新型的层次模型预测控制（MPC），用于操纵任务的接触优化。使用两个级联的MPC，我们将机车操作问题分为两个部分：第一个部分优化机器人和对象之间的接触力和接触位置，而第二个则通过机器人运动来调节所需的相互作用力。我们的方法在模拟和硬件实验中都得到了成功验证。尽管基线运动MPC无法遵循对象的所需轨迹，但我们提出的方法可以有效地控制对象的位置和方向，并以最小的跟踪误差来控制对象的位置和方向。此功能还使我们能够在机车操作任务期间对机器人和对象执行障碍物。
Recent studies on quadruped robots have focused on either locomotion or mobile manipulation using a robotic arm. Legged robots can manipulate heavier and larger objects using non-prehensile manipulation primitives, such as planar pushing, to drive the object to the desired location. In this paper, we present a novel hierarchical model predictive control (MPC) for contact optimization of the manipulation task. Using two cascading MPCs, we split the loco-manipulation problem into two parts: the first to optimize both contact force and contact location between the robot and the object, and the second to regulate the desired interaction force through the robot locomotion. Our method is successfully validated in both simulation and hardware experiments. While the baseline locomotion MPC fails to follow the desired trajectory of the object, our proposed approach can effectively control both object's position and orientation with minimal tracking error. This capability also allows us to perform obstacle avoidance for both the robot and the object during the loco-manipulation task.
