论文标题
预期的人机手机路径计划搜救
Anticipatory Human-Robot Path Planning for Search and Rescue
论文作者
论文摘要
在这项工作中，我们的目标是通过允许自动无人驾驶汽车（UAV）团队与当地的人类搜索者有效合作，扩展现有的搜索范式。我们得出一个框架，其中包括模拟的丢失人行为模型，以及由过去搜索任务收集的数据告知的人类搜索者行为模型。这些模型被一起使用，以创建失落者的位置和预期的搜索者轨迹的概率热图。然后，我们使用带有Gibbs的内核的高斯工艺来准确对有限的视野（FOV）传感器（例如热摄像机）建模，从中我们得出了一个风险度量，以驱动无人机路径优化。我们的框架最终为无人机团队计算一组搜索路径，以自主地补充人类搜索者的努力。
In this work, our goal is to extend the existing search and rescue paradigm by allowing teams of autonomous unmanned aerial vehicles (UAVs) to collaborate effectively with human searchers on the ground. We derive a framework that includes a simulated lost person behavior model, as well as a human searcher behavior model that is informed by data collected from past search tasks. These models are used together to create a probabilistic heatmap of the lost person's position and anticipated searcher trajectories. We then use Gaussian processes with a Gibbs' kernel to accurately model a limited field-of-view (FOV) sensor, e.g., thermal cameras, from which we derive a risk metric that drives UAV path optimization. Our framework finally computes a set of search paths for a team of UAVs to autonomously complement human searchers' efforts.
