论文标题
使用深厚的强化学习，在不同的功率约束下计划的无人机覆盖路径计划
UAV Coverage Path Planning under Varying Power Constraints using Deep Reinforcement Learning
论文作者
论文摘要
覆盖路径规划（CPP）是设计轨迹的任务，该轨迹使移动代理能够在感兴趣的领域的每个点上旅行。我们提出了一种新方法，以控制无人驾驶汽车（UAV），该方法在CPP任务中携带摄像头，并在包含无飞行区域的环境中具有随机启动位置和多种选择。尽管已经提出了许多方法来解决类似的CPP问题，但我们利用端到端的强化学习（RL）学习控制政策，该控制政策概括了无人机的功率约束。尽管电池技术最近有所改善，但小型无人机的最大飞行范围仍然是严重的限制，这会因无人机消耗的变化而加剧，这很难预测。通过使用类似地图的输入渠道通过卷积网络层为代理提供空间信息，我们可以训练双重深Q网络（DDQN），以对无人机进行控制决策，平衡有限的功率预算和覆盖目标。所提出的方法可以应用于各种环境，并将复杂的目标结构与系统约束相一致。
Coverage path planning (CPP) is the task of designing a trajectory that enables a mobile agent to travel over every point of an area of interest. We propose a new method to control an unmanned aerial vehicle (UAV) carrying a camera on a CPP mission with random start positions and multiple options for landing positions in an environment containing no-fly zones. While numerous approaches have been proposed to solve similar CPP problems, we leverage end-to-end reinforcement learning (RL) to learn a control policy that generalizes over varying power constraints for the UAV. Despite recent improvements in battery technology, the maximum flying range of small UAVs is still a severe constraint, which is exacerbated by variations in the UAV's power consumption that are hard to predict. By using map-like input channels to feed spatial information through convolutional network layers to the agent, we are able to train a double deep Q-network (DDQN) to make control decisions for the UAV, balancing limited power budget and coverage goal. The proposed method can be applied to a wide variety of environments and harmonizes complex goal structures with system constraints.
