Optimisation of Air-Ground Swarm Teaming for Target Search, using Differential Evolution

TL;DR

This paper introduces an evolutionary approach using differential evolution to optimize the cooperation between ground and UAV swarms for target search, balancing target coverage and network connectivity.

Contribution

It proposes a novel dual-swarm optimization framework that simultaneously enhances target search efficiency and network survivability using differential evolution.

Findings

The evolved system achieves a good balance between target coverage and connectivity.

The differential evolution algorithm effectively optimizes the dual-swarm system.

Results demonstrate improved performance over non-optimized approaches.

Abstract

This paper presents a swarm teaming perspective that enhances the scope of classic investigations on survivable networks. A target searching generic context is considered as test-bed, in which a swarm of ground agents and a swarm of UAVs cooperate so that the ground agents reach as many targets as possible in the field while also remaining connected as much as possible at all times. To optimise the system against both these objectives in the same time, we use an evolutionary computation approach in the form of a differential evolution algorithm. Results are encouraging, showing a good evolution of the fitness function used as part of the differential evolution, and a good performance of the evolved dual-swarm system, which exhibits an optimal trade-off between target reaching and connectivity.