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

2025-06-25

AI Summary

To address low task allocation efficiency and the disconnect between theoretical models and actual energy consumption in industrial multi-UAV monitoring, this paper proposes a hybrid optimization framework integrating genetic algorithms with 2-Opt local search, coupled with a hardware-in-the-loop (HIL) simulation platform supporting multi-UAV coordination. By incorporating realistic flight dynamics and high-fidelity battery models, the framework significantly enhances the physical realizability of path planning. Empirical validation demonstrates strong correlation (>0.96) between the optimized theoretical cost function and measured battery depletion and flight time. In real-world industrial deployments, the approach improves task allocation efficiency by 23.5% while maintaining operational feasibility. This work is the first to systematically reveal the strong coupling between abstract task allocation objectives and hardware-level energy metrics, establishing a verifiable modeling and optimization paradigm for physics-aware intelligent UAV scheduling.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Optimizing UAV task allocation for industrial monitoring missions
• Integrating Genetic Algorithms with 2-Opt for high-quality solutions
• Validating theoretical cost correlation with real-world flight data

Innovation

• Genetic Algorithms with 2-Opt local search
• Hardware-in-the-loop simulation validation
• Correlates cost function with real-world data