TAMOLS: Terrain-Aware Motion Optimization for Legged Systems

TL;DR

This paper introduces a real-time terrain-aware motion optimization framework for legged robots that jointly plans base pose and footholds using a heightmap, ensuring robustness and efficiency in complex terrains.

Contribution

It presents a novel control pipeline with a joint trajectory optimization approach, graduated optimization, and a stability criterion, enabling real-time navigation over complex terrains.

Findings

Successful stair climbing and stepping stone traversal

Real-time optimization under 10 milliseconds

Enhanced robustness with momentum observer

Abstract

Terrain geometry is, in general, non-smooth, non-linear, non-convex, and, if perceived through a robot-centric visual unit, appears partially occluded and noisy. This work presents the complete control pipeline capable of handling the aforementioned problems in real-time. We formulate a trajectory optimization problem that jointly optimizes over the base pose and footholds, subject to a heightmap. To avoid converging into undesirable local optima, we deploy a graduated optimization technique. We embed a compact, contact-force free stability criterion that is compatible with the non-flat ground formulation. Direct collocation is used as transcription method, resulting in a non-linear optimization problem that can be solved online in less than ten milliseconds. To increase robustness in the presence of external disturbances, we close the tracking loop with a momentum observer. Our experiments demonstrate stair climbing, walking on stepping stones, and over gaps, utilizing various dynamic gaits.