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.

中文翻译：

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使用来自空中车辆的不确定感知的地面车辆的风险意识规划和分配

我们为未知环境中的多机器人、多需求分配和规划提出了一个风险感知框架。我们的动机是地面车辆必须尽快到达需求地点的灾难响应和搜救场景。我们考虑一种设置，其中地形信息仅以航空地理参考图像的形式提供。深度学习技术可用于航拍图像的语义分割，以创建用于安全地面机器人导航的成本图。这种分割可能仍然是嘈杂的。因此，我们提出了一个联合规划和感知框架，用于解释由于嘈杂感知而引入的风险。我们的贡献有两个方面：(i) 我们展示了如何使用贝叶斯深度学习技术来提取感知级别的风险；(ii) 使用风险理论度量 CVaR，用于风险意识规划和分配。管道在理论上建立，然后通过两个数据集进行实证分析。我们发现，对两个级别的风险进行考虑会产生可量化的更安全的路径和分配。