Coevolving Hexapod Legs to Generate Tripod Gaits

Cameron Angliss, Gary Parker

2023

Abstract

This research is the first step in new research that is investigating the use of an extensive neural network (NN) system to control the gait of a hexapod robot by sending specific position signals to each of the leg actuators. The system is highly distributed with nerve clusters that control each leg and no central controller. The intent is to create a more biologically-inspired system and one that has the potential to dynamically change its gait in real-time to accommodate for unforeseen malfunctions. We approach the evolution of this extensive NN system in a unique way by treating each of the legs as an individual agent and using cooperative coevolution to evolve the team of heterogeneous leg agents to perform the task, which in this initial phase is walking forward on a flat surface. When tested using a simulated hexapod robot modeled after an actual robot, this new method reliably produced a stable tripod gait.

Paper Citation

in Harvard Style

Angliss C. and Parker G. (2023). Coevolving Hexapod Legs to Generate Tripod Gaits. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-623-1, pages 1063-1071. DOI: 10.5220/0011896200003393

in Bibtex Style

@conference{icaart23,
author={Cameron Angliss and Gary Parker},
title={Coevolving Hexapod Legs to Generate Tripod Gaits},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2023},
pages={1063-1071},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011896200003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Coevolving Hexapod Legs to Generate Tripod Gaits
SN - 978-989-758-623-1
AU - Angliss C.
AU - Parker G.
PY - 2023
SP - 1063
EP - 1071
DO - 10.5220/0011896200003393