{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T15:45:42Z","timestamp":1779896742933,"version":"3.53.1"},"reference-count":23,"publisher":"Cambridge University Press (CUP)","issue":"9","license":[{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotica"],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Collective decision-making by a swarm of robots is of paramount importance. In particular, the problem of collective perception wherein a swarm of robots aims to achieve consensus on the prevalent feature in the environment. Recently, this problem has been formulated as a discrete collective estimation scenario to estimate their proportion rather than deciding about the prevalent one. Nevertheless, the performance of the existing strategies to resolve this scenario is either poor or depends on higher communication bandwidth. In this work, we propose a novel decision-making strategy based on maximum likelihood estimate sharing (MLES) to resolve the discrete collective estimation scenario. Experimentally, we compare the tradeoff speed versus accuracy of MLES with state-of-the-art methods in the literature, such as direct comparison (DC) and distributed Bayesian belief sharing (DBBS). Interestingly, MLES achieves an accurate consensus nearly 20% faster than DBBS, its communication bandwidth requirement is the same as DC but six times less than DBBS, and its computational complexity is <jats:inline-formula><jats:alternatives><jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" mime-subtype=\"png\" xlink:href=\"S0263574723000668_inline1.png\"\/><jats:tex-math>\n$O(1)$\n<\/jats:tex-math><\/jats:alternatives><\/jats:inline-formula>. Furthermore, we investigate how noisy sensors affect the effectiveness of the strategies under consideration, with MLES showing better sustainability.<\/jats:p>","DOI":"10.1017\/s0263574723000668","type":"journal-article","created":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T09:41:20Z","timestamp":1685612480000},"page":"2754-2773","source":"Crossref","is-referenced-by-count":4,"title":["Maximum likelihood estimate sharing for collective perception in static environments for swarm robotics"],"prefix":"10.1017","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1189-2684","authenticated-orcid":false,"given":"Ahmed","family":"Abdelli","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Yachir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdenour","family":"Amamra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Belkacem","family":"Khaldi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2023,6,1]]},"reference":[{"key":"S0263574723000668_ref9","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/s11721-021-00188-4","article-title":"Negative updating applied to the best-of-n problem with noisy qualities","volume":"15","author":"Lee","year":"2021","journal-title":"Swarm Intell."},{"key":"S0263574723000668_ref5","doi-asserted-by":"publisher","DOI":"10.3389\/frobt.2020.00036"},{"key":"S0263574723000668_ref16","doi-asserted-by":"publisher","DOI":"10.1007\/s11721-021-00201-w"},{"key":"S0263574723000668_ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2019.100565"},{"key":"S0263574723000668_ref21","unstructured":"[21] Strobel, V. , Ferrer, E. C.\u00f3 and Dorigo, M. . Managing Byzantine Robots via Blockchain Technology in a Swarm Robotics Collective Decision Making Scenario. In: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS \u201918, Richland, SC: International Foundation for Autonomous Agents and Multiagent Systems, 2018, 541\u2013549."},{"key":"S0263574723000668_ref11","doi-asserted-by":"crossref","unstructured":"[11] Hamann, H. 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Appl."},{"key":"S0263574723000668_ref6","doi-asserted-by":"crossref","first-page":"e0140950","DOI":"10.1371\/journal.pone.0140950","article-title":"A design pattern for decentralised decision making","volume":"10","author":"Reina","year":"2015","journal-title":"PLOS One"},{"key":"S0263574723000668_ref18","author":"Valentini","year":"2016"},{"key":"S0263574723000668_ref22","unstructured":"[22] Ebert, J. T. , Gauci, M. and Nagpal, R. . Multi-Feature Collective Decision Making in Robot Swarms. In: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS \u201918, Richland, SC: International Foundation for Autonomous Agents and Multiagent Systems, 2018, 1711\u20131719."},{"key":"S0263574723000668_ref13","doi-asserted-by":"crossref","unstructured":"[13] Ebert, J. T. , Gauci, M. , Mallmann-Trenn, F. and Nagpal, R. . Bayes Bots: Collective Bayesian Decision-Making in Decentralized Robot Swarms. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 2020 pp. 7186\u20137192.","DOI":"10.1109\/ICRA40945.2020.9196584"},{"key":"S0263574723000668_ref3","doi-asserted-by":"publisher","DOI":"10.1007\/s11721-012-0075-2"},{"key":"S0263574723000668_ref17","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1007\/978-3-031-20176-9_5","volume-title":"Swarm Intelligence","author":"Shan","year":"2022"},{"key":"S0263574723000668_ref19","doi-asserted-by":"crossref","unstructured":"[19] Shan, Q. , Heck, A. and Mostaghim, S. . Discrete Collective Estimation in Swarm Robotics with Ranked Voting Systems. In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI), (2021) pp. 1\u20138.","DOI":"10.1109\/SSCI50451.2021.9659868"},{"key":"S0263574723000668_ref12","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1007\/s10458-015-9323-3","article-title":"Collective decision with 100 kilobots: Speed versus accuracy in binary discrimination problems","volume":"30a","author":"Valentini","year":"2016","journal-title":"Auton. 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