Collision-Free MPC for Legged Robots in Static and Dynamic Scenes
This work addresses the problem of safe and efficient locomotion for legged robots in complex, dynamic settings, representing an incremental improvement in collision avoidance methods.
The authors tackled the problem of enabling legged robots to navigate collision-free in static and dynamic environments by developing a model predictive controller that integrates collision avoidance without increasing computational complexity, and demonstrated successful collision-free motions on a quadrupedal robot in challenging indoor scenes.
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.