A Task Selection Approach for Multiple Unmanned Aerial Vehicles
Mei Ni, Yin Sheng, Lipeng Chen, Mengmeng Zhang · 2024
Unmanned Aerial Vehicles (UAVs) equipped with multiple tasking capabilities will play an increasingly important role in future warfare scenarios. As a battlefield environment may have various of tasks and corresponding constraints, the strategic selection of tasks is important to improve the performance of the group of UAVs. This paper addresses this problem through the Environment Classes, Agents, Roles, Groups, and Objects (E-CARGO) model. Considering constraints of task requirements and resource limitations, we propose a hybrid approach that integrates genetic algorithms and linear programming to generate an optimal set of task choices. Our experimental results highlight the effectiveness of linear programming in efficiently achieving optimal solutions within small teams. At the same time, the combination of genetic algorithms and linear programming proves effective in ensuring satisfactory solutions within an acceptable time for larger teams. This research paves the way for optimizing the task selection in complex operational environments.