Publikation: Collective Perception of Environmental Features in a Robot Swarm
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2016
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DORIGO, Marco, ed., Mauro BIRATTARI, ed., Xiaodong LI, ed., Manuel LÓPEZ-IBÁÑEZ, ed., Kazuhiro OHKURA, ed., Carlo PINCIROLI, ed., Thomas STÜTZLE, ed.. Swarm Intelligence : 10th International Conference, ANTS 2016, Brussels, Belgium, September 7-9, 2016, Proceedings. Cham: Springer, 2016, pp. 65-76. Lecture Notes in Computer Science. 9882. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-319-44426-0. Available under: doi: 10.1007/978-3-319-44427-7_6
Zusammenfassung
In order to be effective, collective decision-making strategies need to be not only fast and accurate, but sufficiently general to be ported and reused across different problem domains. In this paper, we propose a novel problem scenario, collective perception, and use it to compare three different strategies: the DMMD, DMVD, and DC strategies. The robots are required to explore their environment, estimate the frequency of certain features, and collectively perceive which feature is the most frequent. We implemented the collective perception scenario in a swarm robotics system composed of 20 e-pucks and performed robot experiments with all considered strategies. Additionally, we also deepened our study by means of physics-based simulations. The results of our performance comparison in the collective perception scenario are in agreement with previous results for a different problem domain and support the generality of the considered strategies.
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
Quality Estimate, Dissemination State, Voter Model, Exploration State, Swarm Size
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ANTS 2016 : 10th International Conference on Swarm Intellingence, 7. Sept. 2016 - 9. Sept. 2016, Brussels, Belgium
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VALENTINI, Gabriele, Davide BRAMBILLA, Heiko HAMANN, Marco DORIGO, 2016. Collective Perception of Environmental Features in a Robot Swarm. ANTS 2016 : 10th International Conference on Swarm Intellingence. Brussels, Belgium, 7. Sept. 2016 - 9. Sept. 2016. In: DORIGO, Marco, ed., Mauro BIRATTARI, ed., Xiaodong LI, ed., Manuel LÓPEZ-IBÁÑEZ, ed., Kazuhiro OHKURA, ed., Carlo PINCIROLI, ed., Thomas STÜTZLE, ed.. Swarm Intelligence : 10th International Conference, ANTS 2016, Brussels, Belgium, September 7-9, 2016, Proceedings. Cham: Springer, 2016, pp. 65-76. Lecture Notes in Computer Science. 9882. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-319-44426-0. Available under: doi: 10.1007/978-3-319-44427-7_6BibTex
@inproceedings{Valentini2016Colle-66016,
year={2016},
doi={10.1007/978-3-319-44427-7_6},
title={Collective Perception of Environmental Features in a Robot Swarm},
number={9882},
isbn={978-3-319-44426-0},
issn={0302-9743},
publisher={Springer},
address={Cham},
series={Lecture Notes in Computer Science},
booktitle={Swarm Intelligence : 10th International Conference, ANTS 2016, Brussels, Belgium, September 7-9, 2016, Proceedings},
pages={65--76},
editor={Dorigo, Marco and Birattari, Mauro and Li, Xiaodong and López-Ibáñez, Manuel and Ohkura, Kazuhiro and Pinciroli, Carlo and Stützle, Thomas},
author={Valentini, Gabriele and Brambilla, Davide and Hamann, Heiko and Dorigo, Marco}
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<dcterms:abstract xml:lang="eng">In order to be effective, collective decision-making strategies need to be not only fast and accurate, but sufficiently general to be ported and reused across different problem domains. In this paper, we propose a novel problem scenario, collective perception, and use it to compare three different strategies: the DMMD, DMVD, and DC strategies. The robots are required to explore their environment, estimate the frequency of certain features, and collectively perceive which feature is the most frequent. We implemented the collective perception scenario in a swarm robotics system composed of 20 e-pucks and performed robot experiments with all considered strategies. Additionally, we also deepened our study by means of physics-based simulations. The results of our performance comparison in the collective perception scenario are in agreement with previous results for a different problem domain and support the generality of the considered strategies.</dcterms:abstract>
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