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

Marko Bjelonic , Prajish Sekoor Lakshmana Sankar, C. Dario Bellicoso, Heike Vallery, Marco Hutter

Research output: Contribution to journalArticleScientificpeer-review

7 Downloads (Pure)

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 wheeledlegged 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.
Original languageEnglish
Pages (from-to)3626-3633
JournalIEEE Robotics and Automation Letters
Volume5
Issue number2
DOIs
Publication statusPublished - 2020

Keywords

  • Legged Robots
  • Wheeled Robots
  • Motion and Path Planning
  • Optimization and Optimal Control

Fingerprint Dive into the research topics of 'Rolling in the Deep: Hybrid Locomotion for Wheeled-Legged Robots Using Online Trajectory Optimization'. Together they form a unique fingerprint.

  • Cite this