TY - GEN
T1 - Towards Hexapod Gait Adaptation using Enumerative Encoding of Gaits
T2 - 2022 IEEE Congress on Evolutionary Computation, CEC 2022
AU - Parque, Victor
N1 - Funding Information:
This research was supported by JSPS KAKENHI Grant Number 20K11998.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - enumerative encoding
KW - gait adaptation
KW - hexapod
KW - particle swarm optimization
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U2 - 10.1109/CEC55065.2022.9870257
DO - 10.1109/CEC55065.2022.9870257
M3 - Conference contribution
AN - SCOPUS:85138753227
T3 - 2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings
BT - 2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 18 July 2022 through 23 July 2022
ER -