A Probabilistic Guidance Approach to Swarm-to-Swarm Engagement Problem

TL;DR

This paper presents a decentralized probabilistic guidance method for swarm-to-swarm engagement, enabling controlled swarms to converge on adversary swarms and defend a base through a Markov chain-based approach.

Contribution

It formulates swarm engagement as an optimization problem with a Markov chain model, allowing decentralized control and dynamic population management.

Findings

Effective convergence to adversary swarms demonstrated

Decentralized approach enables scalable swarm control

Successful defense of base in simulated scenarios

Abstract

This paper introduces a probabilistic guidance approach for the swarm-to-swarm engagement problem. The idea is based on driving the controlled swarm towards an adversary swarm, where the adversary swarm aims to converge to a stationary distribution that corresponds to a defended base location. The probabilistic approach is based on designing a Markov chain for the distribution of the swarm to converge a stationary distribution. This approach is decentralized, so each agent can propagate its position independently of other agents. Our main contribution is the formulation of the swarm-to-swarm engagement as an optimization problem where the population of each swarm decays with each engagement and determining a desired distribution for the controlled swarm to converge time-varying distribution and eliminate agents of the adversary swarm until adversary swarm enters the defended base location. We demonstrate the validity of proposed approach on several swarm engagement scenarios.