{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T09:56:47Z","timestamp":1777715807550,"version":"3.51.4"},"reference-count":37,"publisher":"SAGE Publications","issue":"9","license":[{"start":{"date-parts":[[2008,9,1]],"date-time":"2008-09-01T00:00:00Z","timestamp":1220227200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["The International Journal of Robotics Research"],"published-print":{"date-parts":[[2008,9]]},"abstract":"<jats:p>Research in man-made systems capable of self-diagnosis and self-repair is becoming increasingly relevant in a range of scenarios in which in situ repair\/diagnosis by a human operator is infeasible within an appropriate time frame. In this paper, we present an approach to the multi-robot team diagnosis problem that utilizes gradient-based training of multivariate Gaussian distributions. We then evaluate this approach using a testbed involving modular mobile robots, each assembled from four electromechanically separable modules. The diagnosis algorithm is trained on data obtained from two sources: (1) a computer model of the system dynamics and (2) experimental runs of the physical prototypes. Tests were then performed in which a fault was introduced in one robot in the testbed and the diagnostic algorithm was queried. The results show that the state predicted by the diagnostic algorithm performed well in iden tifying the fault state in the case when the model was trained using the experimental data. Limited convergence was also demonstrated using training data from an imperfect dynamic model and low data sampling frequencies.<\/jats:p>","DOI":"10.1177\/0278364908095700","type":"journal-article","created":{"date-parts":[[2008,8,28]],"date-time":"2008-08-28T11:58:19Z","timestamp":1219924699000},"page":"1069-1090","source":"Crossref","is-referenced-by-count":17,"title":["Toward Cooperative Team-diagnosis in Multi-robot Systems"],"prefix":"10.1177","volume":"27","author":[{"given":"Michael D.M.","family":"Kutzer","sequence":"first","affiliation":[{"name":"Johns Hopkins, University Applied Physics, Laboratory 11100, Johns Hopkins                         Road, Laurel, MD 20723,"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mehran","family":"Armand","sequence":"additional","affiliation":[{"name":"Johns Hopkins, University Applied Physics, Laboratory 11100, Johns Hopkins                         Road, Laurel, MD 20723,"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David H.","family":"Scheid","sequence":"additional","affiliation":[{"name":"Johns Hopkins, University Applied Physics, Laboratory 11100, Johns Hopkins                         Road, Laurel, MD 20723"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ellie","family":"Lin","sequence":"additional","affiliation":[{"name":"Carnegie-Mellon University, Robotics Institute 5000 Forbes Avenue,                         Pittsburgh, Pennsylvania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gregory S.","family":"Chirikjian","sequence":"additional","affiliation":[{"name":"Johns Hopkins University, Department of Mechanical Engineering 3400 North                         Charles Street, Baltimore, Maryland 21218,"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2008,9,1]]},"reference":[{"key":"atypb1","volume-title":"Proceedings of the ISER 7th International Symposium on Experimental Robotics","author":"Bererton, C."},{"key":"atypb2","volume-title":"Proceedings of the 20th National Conference on Artificial Intelligence","author":"Blackmore, L."},{"key":"atypb3","doi-asserted-by":"publisher","DOI":"10.1088\/0960-1317\/13\/4\/301"},{"key":"atypb4","volume-title":"Proceedings of the 8th International Symposium on Experimental Robotics (ISER)","author":"Chirikjian, G.S."},{"key":"atypb5","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1097-4563(199605)13:5<317::AID-ROB5>3.0.CO;2-T"},{"key":"atypb6","volume-title":"Proceedings of IEEE International Conference on Robotics and Automation","author":"Daigle, M."},{"key":"atypb7","volume-title":"Particle filters for Real-Time Fault Detection in Planetary Rovers. 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