面向任务型无人机网络中用于协同执行的能量感知多智能体强化学习
Energy-Aware Multi-Agent Reinforcement Learning for Collaborative Execution in Mission-Oriented Drone Networks
AI总结：
针对任务型无人机网络协同执行的能量与轨迹设计挑战，提出基于多智能体强化学习的协同执行模型，以电池电量驱动设计，实现高任务成功率。
摘要：
面向任务的无人机网络已被广泛应用于结构检测、灾害监测、边境巡逻等场景。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，无人机的协同执行是一个具有挑战性的问题，因为它还涉及高效的轨迹设计。本研究中我们利用多智能体强化学习（MARL）来应对这一挑战，让每架无人机学习根据自身当前状态和环境协同执行任务并规划轨迹。仿真结果表明，所提出的协同执行模型无论任务位置和长度如何，都能以至少80%的概率成功完成任务；当任务密度不是过于稀疏时，甚至可以达到100%的成功率。据我们所知，本研究是最早将MARL应用于面向任务型无人机网络协同执行的研究之一，其独特价值在于无人机电池电量驱动了我们的模型设计。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
