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Conference proceeding
Risk-Aware Planning and Assignment for Ground Vehicles using Uncertain Perception from Aerial Vehicles
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 11763-11769
24 Oct 2020
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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.
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Details
- Title
- Risk-Aware Planning and Assignment for Ground Vehicles using Uncertain Perception from Aerial Vehicles
- Creators
- Vishnu D. Sharma - University of Maryland, College ParkMaymoonah Toubeh - Virginia TechLifeng Zhou - Virginia TechPratap Tokekar - University of Maryland, College Park
- Publication Details
- 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 11763-11769
- Publisher
- IEEE
- Number of pages
- 7
- Grant note
- Office of Naval Research (10.13039/100000006)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000724145801053
- Scopus ID
- 2-s2.0-85102413174
- Other Identifier
- 991021945766004721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Automation & Control Systems
- Computer Science, Artificial Intelligence
- Engineering, Electrical & Electronic
- Robotics