A Collision-Free MPC for Whole-Body Dynamic Locomotion and Manipulation
This addresses the challenge of safe and efficient dynamic locomotion and manipulation for legged robots in cluttered environments, representing an incremental improvement with specific gains.
The paper tackles the problem of real-time collision-free whole-body planning for legged mobile manipulation by enforcing self-collision and environment-collision avoidance as soft constraints in a Model Predictive Control scheme, resulting in a framework that only slightly increases computational complexity and is validated through hardware experiments like dynamic balancing and door opening.
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.