{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T00:39:31Z","timestamp":1777682371633,"version":"3.51.4"},"reference-count":29,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T00:00:00Z","timestamp":1769558400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of High Speed Networks"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:p>Swarm robotics aims to achieve robust collective behaviors through large numbers of relatively simple robots, but modeling and interpreting these emergent dynamics from real experimental data remains challenging. This work proposes an interpretable machine learning framework for modeling and analyzing swarm robot behaviors using a public IEEE DataPort dataset of swarm robotics experiments (eight robots, 200 time steps, and 1,600 labeled samples). We construct a feature-based representation of local interaction metrics (alignment, cohesion, separation, velocity, and position) and train a Random Forest classifier to recognize four behavioral phases: exploration, aggregation, formation, and foraging. The proposed classifier attains 98.12% overall accuracy and high per-class precision and recall, while feature importance and Shapley additive explanation analyses highlight alignment (31.44%) and cohesion (21.62%) as dominant behavioral drivers. Unsupervised clustering with KMeans and DBSCAN, supported by a Silhouette score of 0.2541 and an adjusted Rand index up to 0.69, reveals moderately separable latent structure consistent with the labeled phases. A Random Forest regressor further links local interaction features to global performance indicators, achieving high results on task-level outcomes. Our framework provides a unified, reproducible, and interpretable pipeline for real multi-robot data that combines classification, clustering, and regression. The results demonstrate that biologically inspired features can support accurate, explainable phase recognition and performance prediction, enabling data-driven design of swarm controllers for applications such as precision agriculture, search and rescue, and environmental monitoring.<\/jats:p>","DOI":"10.1177\/09266801251412545","type":"journal-article","created":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T17:37:47Z","timestamp":1769621867000},"page":"107-125","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["An interpretable machine learning framework for modeling and analysis of\u00a0swarm robot behaviors"],"prefix":"10.1177","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9940-3592","authenticated-orcid":false,"given":"Mohammed","family":"Al-Hubaishi","sequence":"first","affiliation":[{"name":"Hali\u00e7 University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0744-7795","authenticated-orcid":false,"given":"Wail","family":"Zita","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Faculty of Engineering, Hali\u00e7 University, Istanbul, T\u00fcrkiye"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2026,1,28]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"1607978","article-title":"Robust autonomous navigation in vineyards: a survey","volume":"12","author":"Martins JGA","year":"2025","unstructured":"Martins JGA, de Carvalho APLF, Marques L. Robust autonomous navigation in vineyards: a survey. Front Robot AI 2025; 12: 1607978.","journal-title":"Front Robot AI"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.3389\/frobt.2023.1134841"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1093\/nsr\/nwad040"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.3389\/frobt.2020.00036"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2021.3072740"},{"key":"e_1_3_2_7_2","first-page":"1","article-title":"Adaptive control in swarm robotics: a survey","volume":"2","author":"Elshamy M","year":"2013","unstructured":"Elshamy M. Adaptive control in swarm robotics: a survey. Int J Adv Res Comput Eng Technol 2013; 2: 1\u20137.","journal-title":"Int J Adv Res Comput Eng Technol"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1007354"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1007194"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.3390\/machines9100236"},{"key":"e_1_3_2_11_2","unstructured":"USST 906. Swarm robotics experimental data IEEE Dataport September 7 2023. DOI:\u00a0https:\/\/doi.org\/10.21227\/ebzh-dh30."},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2025.3556864"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11721-012-0075-2"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106156"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11721-022-00215-y"},{"key":"e_1_3_2_16_2","doi-asserted-by":"crossref","unstructured":"Gandhe M Otte MW. Decentralized robot swarm clustering: adding resilience to malicious masquerade attacks. In: LaValle SM O\u2019Kane JM Otte M et al. (eds) Algorithmic foundations of robotics XV. WAFR 2022 Springer proceedings in advanced robotics vol.\u00a025 pp.98\u2013114. Cham: Springer 2022.","DOI":"10.1007\/978-3-031-21090-7_7"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2024.0143"},{"key":"e_1_3_2_18_2","doi-asserted-by":"crossref","unstructured":"Al-Hubaishi M Hachana M. Enhanced intrusion detection for IoT networks using machine learning approach. In: 2025 9th International symposium on innovative approaches in smart technologies (ISAS) Gaziantep Turkiye 2025 pp.1\u20137. DOI: https:\/\/doi.org\/10.1109\/ISAS66241.2025.11101771.","DOI":"10.1109\/ISAS66241.2025.11101771"},{"key":"e_1_3_2_19_2","first-page":"64","article-title":"Swarm intelligence-based multi-robotics: a comprehensive review","volume":"4","author":"Nguyen LV","year":"2024","unstructured":"Nguyen LV. Swarm intelligence-based multi-robotics: a comprehensive review. Robotics 2024; 4: 64.","journal-title":"Robotics"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.3390\/drones7040269"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cogr.2023.07.004"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-019-04114-z"},{"key":"e_1_3_2_23_2","doi-asserted-by":"crossref","unstructured":"Rajbhandari P Sofge D. Learning neat emergent behaviors in robot swarms. In: 2024 IEEE international conference on robotics and biomimetics (ROBIO) Bangkok Thailand 2024 pp.414\u2013419. DOI: https:\/\/doi.org\/10.1109\/ROBIO64047.2024.10907512.","DOI":"10.1109\/ROBIO64047.2024.10907512"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.3389\/frobt.2018.00012"},{"key":"e_1_3_2_25_2","doi-asserted-by":"crossref","unstructured":"Foreback M Bohm C Dolson E. Leveraging heterogeneous controller representations for evolutionary swarm robotics. In: 2025 IEEE symposium on computational intelligence in artificial life and cooperative intelligent systems (ALIFE\u2013CIS) Trondheim Norway 2025 pp.1\u20139. DOI: https:\/\/doi.org\/10.1109\/ALIFE-CIS64968.2025.10979834.","DOI":"10.1109\/ALIFE-CIS64968.2025.10979834"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2020.103237"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1177\/09544062241275359"},{"key":"e_1_3_2_28_2","doi-asserted-by":"crossref","unstructured":"Zaman TU Biteng P Lu Q. Evolving adaptive foraging robot swarms with neat in environments with obstacles. In: 2025 8th international conference on intelligent robotics and control engineering (IRCE) Kunming China 2025 pp.33\u201338. DOI: https:\/\/doi.org\/10.1109\/IRCE66030.2025.11203043.","DOI":"10.1109\/IRCE66030.2025.11203043"},{"key":"e_1_3_2_29_2","first-page":"729","article-title":"CNN-based intelligent control synthesis for multi-robot coordination and path planning","volume":"18","author":"Ahmed KA","year":"2025","unstructured":"Ahmed KA, Alqezweeni MM. CNN-based intelligent control synthesis for multi-robot coordination and path planning. Int J Intell Eng Syst 2025; 18: 729\u2013742.","journal-title":"Int J Intell Eng Syst"},{"key":"e_1_3_2_30_2","first-page":"63","article-title":"Forecasting robot movement with sensor readings and multi-layer perceptron models","volume":"3","author":"Sabeeh S","year":"2024","unstructured":"Sabeeh S. Forecasting robot movement with sensor readings and multi-layer perceptron models. Misan J Eng Sci 2024; 3: 63\u201383.","journal-title":"Misan J Eng Sci"}],"container-title":["Journal of High Speed Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/09266801251412545","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/09266801251412545","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/09266801251412545","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T08:42:41Z","timestamp":1777452161000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/09266801251412545"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,28]]},"references-count":29,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["10.1177\/09266801251412545"],"URL":"https:\/\/doi.org\/10.1177\/09266801251412545","relation":{},"ISSN":["0926-6801","1875-8940"],"issn-type":[{"value":"0926-6801","type":"print"},{"value":"1875-8940","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,28]]}}}