Risk-Aware Planning and Assignment for Ground Vehicles using Uncertain Perception from Aerial Vehicles
Risk-Aware Planning and Assignment for Ground Vehicles using Uncertain Perception from Aerial Vehicles
复制标题
利用空中车辆的不确定感知对地面车辆进行风险意识规划和分配
DOI：
--
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Pratap Tokekar
中科院分区：
文献类型：
--
作者：
V. Sharma;Maymoonah Toubeh;Lifeng Zhou;Pratap Tokekar
针对未知环境下的多机器人、多需求分配和规划问题，提出了一种风险感知框架。我们的动机是灾难响应和搜救情景，地面车辆必须尽快到达需求地点。我们考虑这样一种设置，其中地形信息仅以航空地理参考图像的形式可用。深度学习技术可用于航空图像的语义分割，以创建安全地面机器人导航的成本图。这样的分割可能仍然很嘈杂。因此，我们提出了一个联合规划和感知框架，该框架解释了由于噪声感知而引入的风险。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在感知层面提取风险；(Ii)使用风险理论指标CVaR进行风险感知规划和分配。从理论上建立了流水线，并通过两个数据集进行了实证分析。我们发现，考虑到这两个层面的风险，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.