Abstract
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.
Abstract (translated)
任务导向的无人机网络已被广泛用于结构检查、灾害监测、边境监控等。由于无人机电池容量有限，任务执行策略会影响网络性能和任务完成情况。然而，在这种动态环境中，协作执行对无人机来说是一个挑战性问题，因为它也涉及高效的轨迹设计。在这项研究中，我们利用多智能体强化学习（MARL）来应对这一挑战，使每架无人机能够基于其当前状态和环境学会协同执行任务并规划轨迹。仿真结果显示，所提出的协作执行模型至少能在80%的情况下成功完成任务，无论任务位置和长度如何，并且当任务密度不是特别稀疏时，甚至能实现100%的成功率。据我们所知，我们的工作是利用MARL进行任务导向的无人机网络中协同执行的先驱研究之一；这项工作的独特价值在于无人机电池电量驱动了我们的模型设计。
URL
https://arxiv.org/abs/2410.22578
