# Risk-Aware Planning and Assignment for Ground Vehicles using Uncertain Perception from Aerial Vehicles

arXiv: https://arxiv.org/abs/2003.11675 · HTML: https://mlsanity.com/paper/2003.11675/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2020.

## Built on by (most cited first, 12 of 12)

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- [On Dynamic Programming Decompositions of Static Risk Measures in Markov Decision Processes](https://mlsanity.com/paper/2304.12477/) (2023)
- [Beyond Robustness: A Taxonomy of Approaches towards Resilient Multi-Robot Systems](https://mlsanity.com/paper/2109.12343/) (2021)
- [Game-Theoretic Planning for Autonomous Driving among Risk-Aware Human Drivers](https://mlsanity.com/paper/2205.00562/) (2022)
- [CVaR-based Flight Energy Risk Assessment for Multirotor UAVs using a Deep Energy Model](https://mlsanity.com/paper/2105.15189/) (2021)
- [Risk-aware Path Planning via Probabilistic Fusion of Traversability Prediction for Planetary Rovers on Heterogeneous Terrains](https://mlsanity.com/paper/2303.01169/) (2023)
- [SafePath: Conformal Prediction for Safe LLM-Based Autonomous Navigation](https://mlsanity.com/paper/2505.09427/) (2025)
- [Robot Navigation in Risky, Crowded Environments: Understanding Human Preferences](https://mlsanity.com/paper/2303.08284/) (2023)
- [Learning a Single Neuron Robustly to Distributional Shifts and Adversarial Label Noise](https://mlsanity.com/paper/2411.06697/) (2024)
- [Distributionally Robust Optimization with Adversarial Data Contamination](https://mlsanity.com/paper/2507.10718/) (2025)
- [SOREL: A Stochastic Algorithm for Spectral Risks Minimization](https://mlsanity.com/paper/2407.14618/) (2024)
- [Improving Zero-Shot ObjectNav with Generative Communication](https://mlsanity.com/paper/2408.01877/) (2024)

## Abstract (arXiv)

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.