Task Allocation of UAVs for Monitoring Missions via Hardware-in-the-Loop Simulation and Experimental Validation

TL;DR

This paper presents a hybrid genetic algorithm approach for UAV task allocation in industrial monitoring, validated through hardware-in-the-loop simulation and real-world experiments, demonstrating practical effectiveness.

Contribution

It introduces a novel combination of genetic algorithms and local search for UAV task allocation, validated with hardware-in-the-loop simulation and real-world data analysis.

Findings

High correlation between cost function and actual battery consumption.

Validated approach in real industrial environment.

Hardware-in-the-loop simulator effectively models UAV team dynamics.

Abstract

This study addresses the optimisation of task allocation for Unmanned Aerial Vehicles (UAVs) within industrial monitoring missions. The proposed methodology integrates a Genetic Algorithms (GA) with a 2-Opt local search technique to obtain a high-quality solution. Our approach was experimentally validated in an industrial zone to demonstrate its efficacy in real-world scenarios. Also, a Hardware-in-the-loop (HIL) simulator for the UAVs team is introduced. Moreover, insights about the correlation between the theoretical cost function and the actual battery consumption and time of flight are deeply analysed. Results show that the considered costs for the optimisation part of the problem closely correlate with real-world data, confirming the practicality of the proposed approach.