论文标题
使用空中车辆的不确定的看法，风险了解地面车辆的风险计划和分配
Risk-Aware Planning and Assignment for Ground Vehicles using Uncertain Perception from Aerial Vehicles
论文作者
论文摘要
我们为在未知环境中的多机器人，多重分配和计划提供了一个风险感知的框架。我们的动机是灾难响应和搜索和救援方案，地面车辆必须尽快到达需求位置。我们考虑仅以空中地理图像的形式获得地形信息的设置。深度学习技术可用于对空中图像的语义分割，以创建用于安全地面机器人导航的成本图。这样的细分可能仍然很吵。因此，我们提出了一个联合计划和感知框架，该框架是由于嘈杂的看法引起的风险。我们的贡献是两个方面：（i）我们展示了如何使用贝叶斯深度学习技术在感知水平上提取风险； （ii）使用风险理论措施CVAR进行风险了解计划和分配。理论上建立了管道，然后通过两个数据集进行经验分析。我们发现，在这两个层面上考虑风险会产生可量化的更安全的路径和任务。
We propose a risk-aware framework for multi-robot, multi-demand assignment and planning in unknown environments. Our motivation is disaster response and search-and-rescue scenarios where ground vehicles must reach demand locations as soon as possible. We consider a setting where the terrain information is available only in the form of an aerial, georeferenced image. Deep learning techniques can be used for semantic segmentation of the aerial image to create a cost map for safe ground robot navigation. Such segmentation may still be noisy. Hence, we present a joint planning and perception framework that accounts for the risk introduced due to noisy perception. Our contributions are two-fold: (i) we show how to use Bayesian deep learning techniques to extract risk at the perception level; and (ii) use a risk-theoretical measure, CVaR, for risk-aware planning and assignment. The pipeline is theoretically established, then empirically analyzed through two datasets. We find that accounting for risk at both levels produces quantifiably safer paths and assignments.
