TY - JOUR
T1 - Rolling in the Deep
T2 - Hybrid Locomotion for Wheeled-Legged Robots Using Online Trajectory Optimization
AU - Bjelonic , Marko
AU - Sekoor Lakshmana Sankar, Prajish
AU - Dario Bellicoso, C.
AU - Vallery, Heike
AU - Hutter, Marco
N1 - Accepted Author Manuscript
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Legged Robots
KW - Wheeled Robots
KW - Motion and Path Planning
KW - Optimization and Optimal Control
UR - http://www.scopus.com/inward/record.url?scp=85083039099&partnerID=8YFLogxK
U2 - 10.1109/LRA.2020.2979661
DO - 10.1109/LRA.2020.2979661
M3 - Article
SN - 2377-3766
VL - 5
SP - 3626
EP - 3633
JO - IEEE Robotics and Automation Letters
JF - IEEE Robotics and Automation Letters
IS - 2
ER -