论文标题
无人机的协调：分散的能源感知的群体智能，用于时空感应
Coordination of Drones at Scale: Decentralized Energy-aware Swarm Intelligence for Spatio-temporal Sensing
论文作者
论文摘要
智能城市应用程序（例如交通监控和灾难响应）经常使用众多智能和合作无人机来有效收集感兴趣和时间跨度的传感器数据。但是，当所需的传感变成时空较大且变化时，将传感任务的集体安排与大量电池限制和分布式无人机进行了挑战。为了解决这个问题，本文介绍了一种可扩展的能量感知模型，用于计划和协调时空感应。协调模型建立在分散的多代理集体学习算法（EPOS）的基础上，以确保缺乏现有方法的可扩展性，弹性和灵活性。实验结果说明了该方法与最新方法相比的出色性能。分析结果对无人机的协调迁移率如何影响感应性能有了更深入的了解。这种新颖的协调解决方案应用于使用现实世界数据的交通监控，以证明$ 46.45 \％$ $准确，并且$ 2.88 \％$ $ $有效地检测到了无人机的数量成为稀缺资源。
Smart City applications, such as traffic monitoring and disaster response, often use swarms of intelligent and cooperative drones to efficiently collect sensor data over different areas of interest and time spans. However, when the required sensing becomes spatio-temporally large and varying, a collective arrangement of sensing tasks to a large number of battery-restricted and distributed drones is challenging. To address this problem, this paper introduces a scalable and energy-aware model for planning and coordination of spatio-temporal sensing. The coordination model is built upon a decentralized multi-agent collective learning algorithm (EPOS) to ensure scalability, resilience, and flexibility that existing approaches lack of. Experimental results illustrate the outstanding performance of the proposed method compared to state-of-the-art methods. Analytical results contribute a deeper understanding of how coordinated mobility of drones influences sensing performance. This novel coordination solution is applied to traffic monitoring using real-world data to demonstrate a $46.45\%$ more accurate and $2.88\%$ more efficient detection of vehicles as the number of drones become a scarce resource.
