Adaptive Neuromechanical Control for Robust Behaviors of Bio-Inspired Walking Robots

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dc.contributor.authorHuerta, Carlos Viescas
dc.contributor.authorXiong, Xiaofeng
dc.contributor.authorBilleschou, Peter
dc.contributor.authorManoonpong, Poramate
dc.contributor.editorYang, Haiqin
dc.contributor.editorPasupa, Kitsuchart
dc.contributor.editorLeung, Andrew Chi-Sing
dc.contributor.editorKwok, James T.
dc.contributor.editorChan, Jonathan H.
dc.contributor.editorKing, Irwin
dc.contributor.institutionTecnalia Research & Innovation
dc.date.accessioned2024-07-24T11:50:30Z
dc.date.available2024-07-24T11:50:30Z
dc.date.issued2020
dc.descriptionPublisher Copyright: © 2020, Springer Nature Switzerland AG.
dc.description.abstractWalking 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.en
dc.description.sponsorshipAcknowledgement. This research was supported by the Human Frontier Science Program under Grant agreement no. RGP0002/2017.
dc.description.statusPeer reviewed
dc.format.extent12
dc.identifier.citationHuerta , 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
dc.identifier.citationconference
dc.identifier.doi10.1007/978-3-030-63833-7_65
dc.identifier.isbn9783030638320
dc.identifier.issn0302-9743
dc.identifier.urihttps://hdl.handle.net/11556/1984
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85097396716&partnerID=8YFLogxK
dc.language.isoeng
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartofNeural Information Processing - 27th International Conference, ICONIP 2020, Proceedings
dc.relation.ispartofseriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.projectIDHuman Frontier Science Program, HFSP, RGP0002/2017
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsAdaptive locomotion
dc.subject.keywordsBio-inspired robotics
dc.subject.keywordsComputational intelligence
dc.subject.keywordsMuscle models
dc.subject.keywordsRobot control
dc.subject.keywordsWalking robots
dc.subject.keywordsTheoretical Computer Science
dc.subject.keywordsGeneral Computer Science
dc.titleAdaptive Neuromechanical Control for Robust Behaviors of Bio-Inspired Walking Robotsen
dc.typeconference output
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