用强化学习让无人机协作执行任务，电池电量驱动策略优化。
Energy-Aware Multi-Agent Reinforcement Learning for Collaborative Execution in Mission-Oriented Drone Networks
- 基于多智能体强化学习，让每架无人机根据自身电量和环境自主协作。
- 在任务密度适中时，任务完成率可达100%；即使任务位置和长度变化，成功率仍超80%。
- 首次将电池电量作为核心驱动力设计模型，适合应急救援等高动态场景。
面向结构检测、灾害监测、边境监控等任务的无人机网络广泛应用。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率。在动态环境中实现无人机间高效协作与轨迹规划极具挑战。本文采用多智能体强化学习（MARL），使每架无人机基于当前状态与环境，自主学习协同执行任务与路径规划。仿真结果表明，所提模型在任意任务位置与长度下，任务成功完成率不低于80%；当任务密度不过于稀疏时，成功率可达到100%。据我们所知，本工作是首批将MARL应用于任务导向型无人机网络协同执行的研究之一，其独特价值在于以无人机电池电量为核心驱动力设计模型。
原文摘要 · Abstract (English)
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.
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