TY - GEN
T1 - Adaptive Neuromechanical Control for Robust Behaviors of Bio-Inspired Walking Robots
AU - Huerta, Carlos Viescas
AU - Xiong, Xiaofeng
AU - Billeschou, Peter
AU - Manoonpong, Poramate
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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.
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.
KW - Adaptive locomotion
KW - Bio-inspired robotics
KW - Computational intelligence
KW - Muscle models
KW - Robot control
KW - Walking robots
UR - https://www.scopus.com/pages/publications/85097396716
U2 - 10.1007/978-3-030-63833-7_65
DO - 10.1007/978-3-030-63833-7_65
M3 - Conference contribution
AN - SCOPUS:85097396716
SN - 9783030638320
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 775
EP - 786
BT - Neural Information Processing - 27th International Conference, ICONIP 2020, Proceedings
A2 - Yang, Haiqin
A2 - Pasupa, Kitsuchart
A2 - Leung, Andrew Chi-Sing
A2 - Kwok, James T.
A2 - Chan, Jonathan H.
A2 - King, Irwin
PB - Springer Science and Business Media Deutschland GmbH
T2 - 27th International Conference on Neural Information Processing, ICONIP 2020
Y2 - 18 November 2020 through 22 November 2020
ER -