Decentralized Evolution of Hexapod Gaits with Independent Leg Controllers

2026-09-10

AI Summary

本文通过独立进化每条腿的步态，采用去中心化的进化算法解决六足机器人行走问题，优化了个体腿部控制器以实现高效协调的运动。

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.

Problem

• hexapod locomotion
• independent leg controllers
• decentralized evolutionary algorithm
• emergent behaviors

Innovation

• decentralized evolutionary algorithm
• independent leg controllers
• emergent behaviors