论文标题
多接触MPC用于人形机器人的动态机车操作
Multi-contact MPC for Dynamic Loco-manipulation on Humanoid Robots
论文作者
论文摘要
本文提出了一种通过多接触模型预测性控制（MPC）框架来控制类人形机器人动态机车操作的新方法。提出的框架包括一个多接触动力学模型，该模型能够捕获机车操作中的各种接触模式，例如手动对象触点和脚部接触。我们提出的动力学模型将对象动态表示为作用于系统的外力，这简化了模型，并使其可行地解决MPC问题。在数值验证中，我们的多接触MPC框架只需要每个任务的联系时间，并且所需的状态才能使MPC了解Loco-Manipulation中预测范围内接触模式的变化。提出的框架可以控制类人机器人的机器人，以完成多任务的动态机车操作应用，例如在转弯和行走时有效拾取和掉落物体。
This paper presents a novel method to control humanoid robot dynamic loco-manipulation with multiple contact modes via multi-contact Model Predictive Control (MPC) framework. The proposed framework includes a multi-contact dynamics model capable of capturing various contact modes in loco-manipulation, such as hand-object contact and foot-ground contacts. Our proposed dynamics model represents the object dynamics as an external force acting on the system, which simplifies the model and makes it feasible for solving the MPC problem. In numerical validations, our multi-contact MPC framework only needs contact timings of each task and desired states to give MPC the knowledge of changes in contact modes in the prediction horizons in loco-manipulation. The proposed framework can control the humanoid robot to complete multi-tasks dynamic loco-manipulation applications such as efficiently picking up and dropping off objects while turning and walking.
