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Conference proceeding
Resilient Coverage: Exploring the Local-to-Global Trade-off
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 11740-11747
24 Oct 2020
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
We propose a centralized control framework to select suitable robots from a heterogeneous pool and place them at appropriate locations to monitor a region for events of interest. In the event of a robot failure, our framework repositions robots in a user-defined local neighborhood of the failed robot to compensate for the coverage loss. If repositioning robots locally fails to attain a user-specified level of desired coverage, the central controller augments the team with additional robots from the pool. The size of the local neighborhood around the failed robot and the desired coverage over the region are two objectives that can be varied to achieve a user-specified balance. We investigate the trade-off between the coverage compensation achieved through local repositioning and the computation required to plan the new robot locations. We also study the relationship between the size of the local neighborhood and the number of additional robots added to the team for a given user-specified level of desired coverage. Through extensive simulations and an experiment with a team of seven quadrotors we verify the effectiveness of our framework. We show that to reach a high level of coverage in a neighborhood with a large robot population, it is more efficient to enlarge the neighborhood size, instead of adding additional robots and repositioning them.
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Details
- Title
- Resilient Coverage: Exploring the Local-to-Global Trade-off
- Creators
- Ragesh K. Ramachandran - University of Southern CaliforniaLifeng Zhou - Virginia TechJames A. Preiss - University of Southern CaliforniaGaurav S. Sukhatme - University of Southern California
- Publication Details
- 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 11740-11747
- Publisher
- IEEE
- Number of pages
- 8
- Grant note
- Army Research Laboratory (10.13039/100006754)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000724145801050
- Scopus ID
- 2-s2.0-85102401030
- Other Identifier
- 991021945757904721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Automation & Control Systems
- Computer Science, Artificial Intelligence
- Engineering, Electrical & Electronic
- Robotics