Towards Hexapod Gait Adaptation using Enumerative Encoding of Gaits: Gradient-Free Heuristics

Victor Parque*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The quest for the efficient adaptation of multilegged robotic systems to changing conditions is expected to render new insights into robotic control and locomotion. In this paper, we study the performance frontiers of the enumerative (factorial) encoding of hexapod gaits for fast recovery to conditions of leg failures. Our computational studies using five nature-inspired gradient-free optimization heuristics have shown that it is possible to render feasible recovery gait strategies that achieve minimal deviation to desired locomotion directives with a few evaluations (trials). For instance, it is possible to generate viable recovery gait strategies reaching 2.5 cm, (10 cm.) deviation on average with respect to a commanded direction with 40 - 60 (20) evaluations/trials. Our results are the potential to enable efficient adaptation to new conditions and to explore further the canonical representations for adaptation in robotic locomotion problems.

Original languageEnglish
Title of host publication2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665467087
DOIs
Publication statusPublished - 2022
Event2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Padua, Italy
Duration: 2022 Jul 182022 Jul 23

Publication series

Name2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings

Conference

Conference2022 IEEE Congress on Evolutionary Computation, CEC 2022
Country/TerritoryItaly
CityPadua
Period22/7/1822/7/23

Keywords

  • enumerative encoding
  • gait adaptation
  • hexapod
  • particle swarm optimization

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computational Mathematics
  • Control and Optimization

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