Decentralized Evolution of Hexapod Gaits with Independent Leg Controllers

Abstract:This paper presents a novel approach to hexapod locomotion by evolving each leg's gait independently through a decentralized evolutionary algorithm. Using the Webots simulator and the Mantis hexapod robot, we optimize individual leg controllers without centralized coordination, allowing emergent behaviors to drive the development of efficient, coordinated locomotion. Our decentralized method is benchmarked against cooperative coevolution, demonstrating improved efficacy in generating stable and adaptive gaits while showing interesting emergent coordination. By enabling independent evolution of leg controllers, this method reduces the complexity of gait optimization and highlights the potential of decentralized strategies for scalable and adaptive robotic systems.

Artificial Intelligence (cs.AI); Robotics (cs.RO)

arXiv:2609.12400 [cs.AI]

(or arXiv:2609.12400v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.12400 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.1007/978-3-032-15635-8_17 Focus to learn more DOI(s) linking to related resources

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