用遗传算法优化无人机巡检任务分配，实测效果显著。
Task Allocation of UAVs for Monitoring Missions via Hardware-in-the-Loop Simulation and Experimental Validation
- 结合遗传算法与2-Opt局部搜索求解任务分配问题
- 优化目标与实际飞行时间、电池消耗高度相关
- 通过硬件在环仿真与实地测试验证方案可行性
本研究针对工业区无人机巡检任务中的任务分配优化问题，提出一种融合遗传算法（GA）与2-Opt局部搜索的求解方法。通过构建无人机团队的硬件在环（HIL）仿真系统，在真实工业区域进行实验验证。深入分析了理论成本函数与实际电池消耗及飞行时间之间的关联性。结果表明，优化过程中考虑的成本指标与真实运行数据高度吻合，证实了该方法在实际场景中的实用性。
原文摘要 · Abstract (English)
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.
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