Mission-oriented drone networks have been widely used for structural inspection, disaster monitoring, border surveillance, etc. Due to the limited battery capacity of drones, mission execution strategy impacts network performance and mission completion. However, collaborative execution is a challenging problem for drones in such a dynamic environment as it also involves efficient trajectory design. We leverage multi-agent reinforcement learning (MARL) to manage the challenge in this study, letting each drone learn to collaboratively execute tasks and plan trajectories based on its current status and environment. Simulation results show that the proposed collaborative execution model can successfully complete the mission at least 80% of the time, regardless of task locations and lengths, and can even achieve a 100% success rate when the task density is not way too sparse. To the best of our knowledge, our work is one of the pioneer studies on leveraging MARL on collaborative execution for mission-oriented drone networks; the unique value of this work lies in drone battery level driving our model design.

翻译：面向任务的无人机网络已广泛应用于结构检测、灾害监测、边境巡查等领域。由于无人机电池容量有限，任务执行策略直接影响网络性能与任务完成度。然而，在动态环境中实现协同执行对无人机而言是一个具有挑战性的问题，因为这还涉及高效的轨迹规划。本研究利用多智能体强化学习（MARL）应对这一挑战，使每架无人机能够根据自身当前状态与环境，学习协同执行任务并规划轨迹。仿真结果表明，无论任务位置与长度如何，所提出的协同执行模型均能以至少80%的概率成功完成任务；当任务密度不过于稀疏时，成功率甚至可达100%。据我们所知，本研究是面向任务的无人机网络中利用MARL实现协同执行的先驱性工作之一；本研究的独特价值在于将无人机电池电量作为模型设计的核心驱动因素。