Collision-Free MPC for Legged Robots in Static and Dynamic Scenes

TL;DR

This paper introduces a collision-free model predictive control approach for legged robots that accounts for static and dynamic obstacles, using a relaxed barrier function and motion prediction to enable safe, whole-body locomotion in complex environments.

Contribution

The proposed holistic MPC method integrates collision avoidance with system dynamics and obstacle prediction without heuristics, improving responsiveness and safety in dynamic scenes.

Findings

Successfully demonstrated collision-free locomotion in complex indoor environments

Handles static and dynamic obstacles with real-time responsiveness

Enables robots to operate at dynamic and kinematic limits

Abstract

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.