Model Predictive Parkour Control of a Monoped Hopper in Dynamically Changing Environments

TL;DR

This paper presents a novel model predictive control approach for a monoped hopper to navigate complex, changing obstacle courses, enabling robust and precise legged robot movements in challenging terrains.

Contribution

It introduces a real-time mixed-integer motion planning controller specifically designed for dynamic obstacle traversal in legged robots.

Findings

Successfully plans optimal paths in real-time obstacle environments

Achieves robust and accurate hopping through a state machine with PD control

Demonstrates improved terrain navigation capabilities

Abstract

A great advantage of legged robots is their ability to operate on particularly difficult and obstructed terrain, which demands dynamic, robust, and precise movements. The study of obstacle courses provides invaluable insights into the challenges legged robots face, offering a controlled environment to assess and enhance their capabilities. Traversing it with a one-legged hopper introduces intricate challenges, such as planning over contacts and dealing with flight phases, which necessitates a sophisticated controller. A novel model predictive parkour controller is introduced, that finds an optimal path through a real-time changing obstacle course with mixed integer motion planning. The execution of this optimized path is then achieved through a state machine employing a PD control scheme with feedforward torques, ensuring robust and accurate performance.