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.

翻译：近年来，随着无人机技术的蓬勃发展，这些设备正被广泛应用于涉及复杂任务的诸多领域。其中一些任务（如火灾监控与救援）对操作员存在高风险，这使得无人机成为规避人类风险的绝佳工具。无人机任务规划是指在特定时间范围内，为飞行器规划位置及动作（如装载/投掷载荷、拍摄视频/图像、采集信息）的过程。这些飞行器由地面控制站（GCS）操控，操作员通过基础系统进行控制。本文提出了一种新型多目标遗传算法，用于解决涉及多无人机团队与多地面控制站的复杂任务规划问题（MPP）。我们设计了一种混合适应度函数：通过约束满足问题（CSP）检验解的可行性，并基于帕累托前沿指标寻找最优解。该算法已在多个数据集上测试，针对任务的不同变量（如完工时间、燃油消耗、飞行距离等）进行优化。实验结果表明，该算法能获得优质解，但随着问题复杂度增加，最优解的获取难度也随之提升。