Adaptive robot climbing with magnetic feet in unknown slippery structure - Lee, Jee-eun; Bandyopadhyay, Tirthankar; Sentis, Luis (Frontiers in Robotics and AI, 2022)
Firm foot contact is the top priority of climbing robots to avoid catastrophic events, especially when working at height. This paper proposes a robust planning and control framework for climbing robots that provides robustness to the slippage in unknown environments. The framework includes (i) a center of mass (CoM) trajectory optimization under the estimated contact condition,(ii) a Kalman filter like approach for uncertain environment parameters estimation and subsequent CoM trajectory re-planing (iii) an online weight adaptation approach for whole-body control (WBC) framework that can adjust the ground reaction force (GRF) distribution in real-time. Though the friction and adhesion characteristics are often assumed to be known, the presence of several factors that lead to a reduction in adhesion may cause critical problems for climbing robots. To address this issue safely and effectively, this paper suggests estimating unknown contact parameters in real-time and using the evaluated contact information to optimize climbing motion. Since slippage is crucial behavior and requires instant recovery, the computation time for motion re-planning is also critical. The proposed CoM trajectory optimization algorithm achieved state-of-art fast computation via trajectory parameterization with several reasonable assumptions and linear algebra tricks. Lastly, an online weight adaptation approach is presented in the paper to stabilize slippery motions within the WBC framework. This can help a robot to manage the slippage at the very last control step by redistributing the desired GRF. In order to verify the effectiveness of our method, we have tested our algorithm and provided benchmarks in simulation using a magnetic legged climbing robot, Magneto.