Publikation: Collective Decision-Making with Bayesian Robots in Dynamic Environments
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Collective decision-making enables self-organizing robot swarms to act autonomously on a swarm level and is essential to coordinate their actions as a whole. When robots only share and communicate information locally a distributed and decentralized approach is required. In a previous paper [4], an efficient method based on a distributed Bayesian algorithm was created to distinguish a binary environment. We extended it to have the capability of dealing with dynamic environments. Therefore, it must avoid global lock-in states. In many realistic applications the robot swarm needs to adapt to (collectively) measurable changes at runtime by revising previous collective decisions. The trade-off between decision-making speed and readiness to revise previous decisions is a seemingly unavoidable challenge. We present our extension of the former approach and study how this trade-off can efficiently be balanced.
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PFISTER, Kai, Heiko HAMANN, 2022. Collective Decision-Making with Bayesian Robots in Dynamic Environments. IROS 2022 : IEEE/RSJ International Conference on Intelligent Robots and Systems. Kyoto, Japan, 23. Okt. 2022 - 27. Okt. 2022. In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Piscataway, NJ: IEEE, 2022, S. 7245-7250. ISSN 2153-0858. eISSN 2153-0866. ISBN 978-1-66547-927-1. Verfügbar unter: doi: 10.1109/IROS47612.2022.9982019BibTex
@inproceedings{Pfister2022Colle-66285, year={2022}, doi={10.1109/IROS47612.2022.9982019}, title={Collective Decision-Making with Bayesian Robots in Dynamic Environments}, isbn={978-1-66547-927-1}, issn={2153-0858}, publisher={IEEE}, address={Piscataway, NJ}, booktitle={2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, pages={7245--7250}, author={Pfister, Kai and Hamann, Heiko} }
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