RT Conference Proceedings
T1 Adaptive Neuromechanical Control for Robust Behaviors of Bio-Inspired Walking Robots
A1 Huerta, Carlos Viescas
A1 Xiong, Xiaofeng
A1 Billeschou, Peter
A1 Manoonpong, Poramate
A2 Yang, Haiqin
A2 Pasupa, Kitsuchart
A2 Leung, Andrew Chi-Sing
A2 Kwok, James T.
A2 Chan, Jonathan H.
A2 King, Irwin
AB Walking animals show impressive locomotion. They can also online adapt their joint compliance to deal with unexpected perturbation for their robust locomotion. To emulate such ability for walking robots, we propose here adaptive neuromechanical control. It consists of two main components: Modular neural locomotion control and online adaptive compliance control. While the modular neural control based on a central pattern generator can generate basic locomotion, the online adaptive compliance control can perform online adaptation for joint compliance. The control approach was applied to a dung beetle-like robot called ALPHA. We tested the control performance on the real robot under different conditions, including impact force absorption when dropping the robot from a certain height, payload compensation during standing, and disturbance rejection during walking. We also compared our online adaptive compliance control with conventional non-adaptive one. Experimental results show that our control approach allows the robot to effectively deal with all these unexpected conditions by adapting its joint compliance online.
PB Springer Science and Business Media Deutschland GmbH
SN 9783030638320
SN 0302-9743
YR 2020
FD 2020
LK https://hdl.handle.net/11556/1984
UL https://hdl.handle.net/11556/1984
LA eng
NO Huerta , C V , Xiong , X , Billeschou , P & Manoonpong , P 2020 , Adaptive Neuromechanical Control for Robust Behaviors of Bio-Inspired Walking Robots . in H Yang , K Pasupa , A C-S Leung , J T Kwok , J H Chan & I King (eds) , Neural Information Processing - 27th International Conference, ICONIP 2020, Proceedings . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 12533 LNCS , Springer Science and Business Media Deutschland GmbH , pp. 775-786 , 27th International Conference on Neural Information Processing, ICONIP 2020 , Bangkok , Thailand , 18/11/20 . https://doi.org/10.1007/978-3-030-63833-7_65
NO conference
NO Publisher Copyright: © 2020, Springer Nature Switzerland AG.
NO Acknowledgement. This research was supported by the Human Frontier Science Program under Grant agreement no. RGP0002/2017.
DS TECNALIA Publications
RD 1 ago 2024