  We present a model predictive controller (MPC) that automatically discovers
collision-free locomotion while simultaneously taking into account the system
dynamics, friction constraints, and kinematic limitations. A relaxed barrier
function is added to the optimization's cost function, leading to collision
avoidance behavior without increasing the problem's computational complexity.
Our holistic approach does not require any heuristics and enables legged robots
to find whole-body motions in the presence of static and dynamic obstacles. We
use a dynamically generated euclidean signed distance field for static
collision checking. Collision checking for dynamic obstacles is modeled with
moving cylinders, increasing the responsiveness to fast-moving agents.
Furthermore, we include a Kalman filter motion prediction for moving obstacles
into our receding horizon planning, enabling the robot to anticipate possible
future collisions. Our experiments demonstrate collision-free motions on a
quadrupedal robot in challenging indoor environments. The robot handles complex
scenes like overhanging obstacles and dynamic agents by exploring motions at
the robot's dynamic and kinematic limits.
