A Collision-Free MPC for Whole-Body Dynamic Locomotion and Manipulation

TL;DR

This paper introduces a real-time, collision-free whole-body MPC for legged robots that ensures safe dynamic locomotion and manipulation by integrating collision avoidance as soft constraints, validated through hardware experiments.

Contribution

It presents a novel MPC framework that incorporates soft collision constraints for dynamic whole-body robot control, enabling safe manipulation and locomotion in complex environments.

Findings

Efficient collision avoidance with minimal computational overhead.

Successful execution of dynamic manipulation tasks with potential collisions.

Validation through hardware experiments demonstrating real-world applicability.

Abstract

In this paper, we present a real-time whole-body planner for collision-free legged mobile manipulation. We enforce both self-collision and environment-collision avoidance as soft constraints within a Model Predictive Control (MPC) scheme that solves a multi-contact optimal control problem. By penalizing the signed distances among a set of representative primitive collision bodies, the robot is able to safely execute a variety of dynamic maneuvers while preventing any self-collisions. Moreover, collision-free navigation and manipulation in both static and dynamic environments are made viable through efficient queries of distances and their gradients via a euclidean signed distance field. We demonstrate through a comparative study that our approach only slightly increases the computational complexity of the MPC planning. Finally, we validate the effectiveness of our framework through a set of hardware experiments involving dynamic mobile manipulation tasks with potential collisions, such as locomotion balancing with the swinging arm, weight throwing, and autonomous door opening.