G-flocking: Flocking Model Optimization based on Genetic Framework

TL;DR

This paper introduces G-flocking, an optimization framework based on genetic algorithms, to enhance the stability and adaptability of flocking models in robotic swarm autonomous navigation.

Contribution

It proposes a novel genetic framework to optimize flocking models, addressing scalability and conflict issues in robotic swarm navigation.

Findings

Improved stability in robotic swarm navigation

Enhanced adaptability to environmental changes

Effective conflict resolution in flocking behavior

Abstract

Flocking model has been widely used to control robotic swarm. However, with the increasing scalability, there exist complex conflicts for robotic swarm in autonomous navigation, brought by internal pattern maintenance, external environment changes, and target area orientation, which results in poor stability and adaptability. Hence, optimizing the flocking model for robotic swarm in autonomous navigation is an important and meaningful research domain.