A Unified MPC Framework for Whole-Body Dynamic Locomotion and Manipulation

Abstract

In this paper, we propose a whole-body planning framework that unifies dynamic locomotion and manipulation tasks by formulating a single multi-contact optimal control problem. We model the hybrid nature of a generic multi-limbed mobile manipulator as a switched system, and introduce a set of constraints that can encode any pre-defined gait sequence or manipulation schedule in the formulation. Since the system is designed to actively manipulate its environment, the equations of motion are composed by augmenting the robot's centroidal dynamics with the manipulated-object dynamics. This allows us to describe any high-level task in the same cost/constraint function. The resulting planning framework could be solved on the robot's onboard computer in real-time within a model predictive control scheme. This is demonstrated in a set of real hardware experiments done in free-motion, such as base or end-effector pose tracking, and while pushing/pulling a heavy resistive door. Robustness against model mismatches and external disturbances is also verified during these test cases.

Bipedal Locomotion with Nonlinear Model Predictive Control: Online Gait Generation using Whole-Body Dynamics

“…In contrast, Model Predictive Control (MPC) provides a tool for the online synthesis of reference trajectories, allowing feedback of environmental parameters to be incorporated into dynamic motion planning [11], [12]. In particular, by optimizing directly over contact forces, these methods have seen significant use in online motion planning for quadrupedal robotics, with extensive experimental results [13]- [15].…”