Whole-Body MPC for a Dynamically Stable Mobile Manipulator
This work addresses the challenge of dynamic stability in mobile manipulators for robotics applications, representing an incremental improvement by integrating existing MPC methods into a novel whole-body formulation.
The authors tackled the problem of autonomous mobile manipulation for an inherently unstable robot by developing a whole-body Model Predictive Control (MPC) framework that jointly optimizes manipulation, balancing, and interaction as a single optimization problem, and validated it in hardware experiments for tasks like end-effector pose tracking and door opening.
Autonomous mobile manipulation offers a dual advantage of mobility provided by a mobile platform and dexterity afforded by the manipulator. In this paper, we present a whole-body optimal control framework to jointly solve the problems of manipulation, balancing and interaction as one optimization problem for an inherently unstable robot. The optimization is performed using a Model Predictive Control (MPC) approach; the optimal control problem is transcribed at the end-effector space, treating the position and orientation tasks in the MPC planner, and skillfully planning for end-effector contact forces. The proposed formulation evaluates how the control decisions aimed at end-effector tracking and environment interaction will affect the balance of the system in the future. We showcase the advantages of the proposed MPC approach on the example of a ball-balancing robot with a robotic manipulator and validate our controller in hardware experiments for tasks such as end-effector pose tracking and door opening.