High-Performance UAV Crowdsensing: A Deep Reinforcement Learning Approach
High-Performance UAV Crowdsensing: A Deep Reinforcement Learning Approach
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DOI：
10.1109/jiot.2022.3160887
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发表时间：
2022-10-01
影响因子：
10.6
通讯作者：
Guo, Song
中科院分区：
文献类型：
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作者：
Wei, Kaimin;Huang, Kai;Guo, Song
路径规划对于实现高性能无人机（UAV）群智感知系统至关重要，该系统可以部署在现实世界中执行大规模任务，特别是在地震和泥石流等紧急情况下。深度强化学习（DRL）最近证明了其在路径设计方面的优越性。然而，它通常是在目标区域的整个状态可用的假设下应用的，这在实践中很难实现。相反，应该努力确保多架无人机的高效飞行，以便收集特定地点观测不完整的数据。在这项工作中，我们着手通过将 DRL 与部分观测相结合来创建高性能无人机群体感知系统。我们提出了一种新颖的基于 DRL 的路径规划算法，称为 DRL-PP。具体来说，我们将注意力机制集成到 actor-critic 技术中，以协助无人机群协作收集数据。我们还设计了一种激励机制来缓解奖励稀疏的问题。此外，我们提供了一个困境检测系统来防止重叠飞行路径的产生。大量模拟的实验结果证明，与最先进的方法相比，所提出的 DRL-PP 可以显着提高数据收集的效率。
Path planning is critical to realizing a high-performance unmanned aerial vehicle (UAV) crowdsensing system, which can be deployed to carry out large-scale tasks in the physical world, especially in emergency scenarios, such as earthquakes and mudslides. Deep reinforcement learning (DRL) has recently proven its superiority in path design. However, it is often applied under the assumption that the entire status of the target region is available, which is hard to achieve in practice. Instead, efforts should be made to ensure the efficient flight of several UAVs in order to collect data with incomplete observations in specified places. In this work, we set out to create a high-performance UAV crowdsensing system by combining DRL with partial observations. We present a novel DRL-based path-planning algorithm called DRL-PP. Specifically, we integrate an attention mechanism into the actor-critic technique to assist UAV swarm collaboration to collect data. We also design an incentive mechanism to ease the problem of sparse reward. Furthermore, we provide a dilemma detection system to prevent the generation of overlapping flight paths. Experimental results from extensive simulations prove that compared with the state-of-the-art approaches, the proposed DRL-PP can significantly improve the efficiency of data collection.