论文标题
无人用的搜索和救援行动使用加固学习
UAV Aided Search and Rescue Operation Using Reinforcement Learning
论文作者
论文摘要
由于部署的灵活性提高和制造成本降低，因此对无人机（UAV）的需求预计将在未来几年中飙升。在本文中，我们在室内环境中探索了无人机辅助搜索和救援〜（SAR）操作，在该环境中，GPS信号可能不可靠。我们考虑了一个SAR场景，即无人机试图通过感知受害者拥有的智能设备发出的RF信号来定位被困在室内环境中的受害者。为了尽可能快地找到受害者，我们利用增强学习（RL）的工具。无人机处接收的信号强度〜（RSS）取决于距源，室内阴影和褪色参数以及安装在无人机上的接收器的天线辐射图案的距离。为了使我们的分析更加现实，我们使用商业射线追踪软件对两个室内场景进行了模拟。然后，在每个可能的离散无人机位置处提取并在Q学习框架中使用相应的RSS值。与利用GPS坐标的传统基于位置的导航方法不同，我们的方法使用RSS来定义RL算法的状态和奖励。我们比较了使用定向和全向天线的提出方法的性能。结果表明，定向天线的使用提供了比全向天线更快的收敛速率。
Owing to the enhanced flexibility in deployment and decreasing costs of manufacturing, the demand for unmanned aerial vehicles (UAVs) is expected to soar in the upcoming years. In this paper, we explore a UAV aided search and rescue~(SAR) operation in indoor environments, where the GPS signals might not be reliable. We consider a SAR scenario where the UAV tries to locate a victim trapped in an indoor environment by sensing the RF signals emitted from a smart device owned by the victim. To locate the victim as fast as possible, we leverage tools from reinforcement learning~(RL). Received signal strength~(RSS) at the UAV depends on the distance from the source, indoor shadowing, and fading parameters, and antenna radiation pattern of the receiver mounted on the UAV. To make our analysis more realistic, we model two indoor scenarios with different dimensions using commercial ray-tracing software. Then, the corresponding RSS values at each possible discrete UAV location are extracted and used in a Q-learning framework. Unlike the traditional location-based navigation approach that exploits GPS coordinates, our method uses the RSS to define the states and rewards of the RL algorithm. We compare the performance of the proposed method where directional and omnidirectional antennas are used. The results reveal that the use of directional antennas provides faster convergence rates than the omnidirectional antennas.
