  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.
