# Risk-Aware Planning and Assignment for Ground Vehicles using Uncertain Perception from Aerial Vehicles Source: [Risk-Aware Planning and Assignment for Ground Vehicles using Uncertain Perception from Aerial Vehicles]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:34:00.401Z Authors: Vishnu D. Sharma, Maymoonah Toubeh, Lifeng Zhou, Pratap Tokekar Published: 2020-03-25T23:23:11.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()