Rolling in the Deep -- Hybrid Locomotion for Wheeled-Legged Robots using Online Trajectory Optimization

TL;DR

This paper introduces an online trajectory optimization framework for wheeled-legged robots, enabling real-time hybrid locomotion that combines walking and driving for versatile and robust navigation on challenging terrains.

Contribution

The paper presents a novel real-time trajectory optimization method for wheeled-legged robots, integrating wheel and base planning for improved robustness and efficiency in hybrid locomotion.

Findings

Successfully executed hybrid gait sequences on rough terrain.

Demonstrated real-time optimization on a quadrupedal robot with wheels.

Validated the approach in DARPA Subterranean Challenge environments.

Abstract

Wheeled-legged robots have the potential for highly agile and versatile locomotion. The combination of legs and wheels might be a solution for any real-world application requiring rapid, and long-distance mobility skills on challenging terrain. In this paper, we present an online trajectory optimization framework for wheeled quadrupedal robots capable of executing hybrid walking-driving locomotion strategies. By breaking down the optimization problem into a wheel and base trajectory planning, locomotion planning for high dimensional wheeled-legged robots becomes more tractable, can be solved in real-time on-board in a model predictive control fashion, and becomes robust against unpredicted disturbances. The reference motions are tracked by a hierarchical whole-body controller that sends torque commands to the robot. Our approach is verified on a quadrupedal robot with non-steerable wheels attached to its legs. The robot performs hybrid locomotion with a great variety of gait sequences on rough terrain. Besides, we validated the robotic platform at the Defense Advanced Research Projects Agency (DARPA) Subterranean Challenge, where the robot rapidly mapped, navigated and explored dynamic underground environments.