Particle Swarm Optimizer-based Attack Strategy with Swarm Robots

Abstract

An environment where a robot swarm attacks a territory protected by another one leads to an attack-defense confrontation problem. Commonly-used deep reinforcement learning-based methods rely on pre-training and become intractable due to the curse of dimensionality. To develop effective attack strategies, inspired by a particle swarm optimizer (PSO), this work proposes a PSO-based strategy for a robot swarm for the first time. During the moving of a robot swarm, each robot obtains situation information through perceiving its nearby peers and enemies and uses such information to construct its fitness function. Then, each robot uses PSO to optimize its fitness function and searches for its optimal attack position, which guides it to move in the next time slot. The experimental analyses show that the PSO-based attack strategy has more potential in solving large-scale confrontational problems than the deep reinforcement learning-based algorithms.