使用多目标遗传算法求解复杂多无人机任务规划问题
Solving Complex Multi-UAV Mission Planning Problems using Multi-objective Genetic Algorithms
AI总结：
本文提出一种多目标遗传算法，结合约束满足与帕累托度量，用于求解多无人机及地面控制站的复杂任务规划问题，实验表明算法有效但复杂问题下最优解难寻。
摘要：
由于近年来无人机技术的蓬勃发展，无人机正被广泛应用于涉及复杂任务的众多领域。其中一些任务对飞行器驾驶员存在高风险，例如火灾监测和救援任务，这使得无人机成为避免人类风险的最佳选择。无人机任务规划是指在一段时间内为飞行器规划位置和动作（装载/卸载载荷、拍摄视频/照片、获取信息）的过程。这些飞行器由地面控制站（GCSs）控制，操作人员使用简陋的系统进行操作。本文提出了一种新的多目标遗传算法，用于解决涉及一组无人机和一组地面控制站的复杂任务规划问题（MPP）。我们设计了一个混合适应度函数，使用约束满足问题（CSP）来检查解的有效性，并采用基于帕累托的度量来寻找最优解。该算法已在多个数据集上进行了测试，优化了任务的不同变量，如完工时间、燃料消耗、距离等。实验结果表明，该新算法能够获得较好的解，但随着问题变得更加复杂，最优解也变得更加难以找到。
英文摘要：
Due to recent booming of UAVs technologies, these are being used in many fields involving complex tasks. Some of them involve a high risk to the vehicle driver, such as fire monitoring and rescue tasks, which make UAVs excellent for avoiding human risks. Mission Planning for UAVs is the process of planning the locations and actions (loading/dropping a load, taking videos/pictures, acquiring information) for the vehicles, typically over a time period. These vehicles are controlled from Ground Control Stations (GCSs) where human operators use rudimentary systems. This paper presents a new Multi-Objective Genetic Algorithm for solving complex Mission Planning Problems (MPP) involving a team of UAVs and a set of GCSs. A hybrid fitness function has been designed using a Constraint Satisfaction Problem (CSP) to check if solutions are valid and Pareto-based measures to look for optimal solutions. The algorithm has been tested on several datasets optimizing different variables of the mission, such as the makespan, the fuel consumption, distance, etc. Experimental results show that the new algorithm is able to obtain good solutions, however as the problem becomes more complex, the optimal solutions also become harder to find.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
