论文标题
使用全球和本地地图信息进行深入强化学习的无人机路径计划
UAV Path Planning using Global and Local Map Information with Deep Reinforcement Learning
论文作者
论文摘要
自动无人机（UAV）的路径规划方法通常是为一种特定类型的任务而设计的。这项工作提出了一种基于深入增强学习（DRL）的自动无人机路径计划的方法，该方法可应用于广泛的任务场景。具体来说，我们比较了覆盖路径计划（CPP），在该计划中，无人机的目标是调查数据收集感兴趣的领域（DH），在该领域中，无人机从分布式的物联网（IoT）传感器设备中收集数据。通过利用环境的结构化地图信息，我们在两个截然不同的任务场景上训练具有相同体系结构的双重深Q-Networks（DDQN），以制定运动决策，以平衡各个任务目标与导航约束。通过引入一种新型方法，利用了一个压缩的环境图，并结合了一个裁切但未压缩的局部图，显示了无人机代理的附近，我们证明了所提出的方法可以有效地扩展到大型环境。我们还扩展了先前的结果，用于概括控制策略，这些策略在情况变化时不需要重新培训，并提供了对关键地图处理参数对路径计划性能的影响的详细分析。
Path planning methods for autonomous unmanned aerial vehicles (UAVs) are typically designed for one specific type of mission. This work presents a method for autonomous UAV path planning based on deep reinforcement learning (DRL) that can be applied to a wide range of mission scenarios. Specifically, we compare coverage path planning (CPP), where the UAV's goal is to survey an area of interest to data harvesting (DH), where the UAV collects data from distributed Internet of Things (IoT) sensor devices. By exploiting structured map information of the environment, we train double deep Q-networks (DDQNs) with identical architectures on both distinctly different mission scenarios to make movement decisions that balance the respective mission goal with navigation constraints. By introducing a novel approach exploiting a compressed global map of the environment combined with a cropped but uncompressed local map showing the vicinity of the UAV agent, we demonstrate that the proposed method can efficiently scale to large environments. We also extend previous results for generalizing control policies that require no retraining when scenario parameters change and offer a detailed analysis of crucial map processing parameters' effects on path planning performance.
