{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T07:56:04Z","timestamp":1782460564024,"version":"3.54.5"},"reference-count":44,"publisher":"SAGE Publications","issue":"8","license":[{"start":{"date-parts":[[2005,8,1]],"date-time":"2005-08-01T00:00:00Z","timestamp":1122854400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/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":[[2005,8]]},"abstract":"<jats:p>\n                    <jats:italic toggle=\"yes\">In this paper we describe a Bayesian approach to model selection and state                     estimation for sensor-based robot tasks. The approach is illustrated with a                     hybrid model-state estimation example from force-controlled autonomous compliant                     motion: simultaneous (discrete) contact formation recognition and estimation of                     (continuous) geometrical parameters. Previous research in this area mostly tries                     to solve one of the two subproblems, or treats the contact formation recognition                     problem separately, avoiding integration between the solutions to the contact                     formation recognition and the geometrical parameter estimation problems. A more                     powerful hybrid model, explicitly modeling contact formation transitions, is                     developed to deal with larger uncertainties. This paper demonstrates that Kalman                     filter variants have limits: iterated extended Kalman filters can only handle                     small uncertainties on the geometrical parameters, while the non-minimal state                     Kalman filter cannot deal with model selection. Particle filters can handle the                     increased level of model complexity. Explicit measurement equations for the                     particle filter are derived from the implicit kinematic and energetic                     constraints. The experiments prove that the particle filter approach                     successfully estimates the hybrid joint posterior density of the discrete                     contact formation variable and the 12-dimensional, continuous geometrical                     parameter vector during the execution of an assembly task. The problem shows                     similarities with the well-known problems of data association in simultaneous                     localization and map-building (SLAM) and model selection in global localization.<\/jats:italic>\n                  <\/jats:p>","DOI":"10.1177\/0278364905056196","type":"journal-article","created":{"date-parts":[[2005,8,15]],"date-time":"2005-08-15T09:28:04Z","timestamp":1124098084000},"page":"615-630","source":"Crossref","is-referenced-by-count":45,"title":["Bayesian Hybrid Model-State Estimation Applied to Simultaneous Contact                 Formation Recognition and Geometrical Parameter Estimation"],"prefix":"10.1177","volume":"24","author":[{"given":"K.","family":"Gadeyne","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Katholieke Universiteit Leuven,                         Celestijnenlaan 300B, 3001 Leuven, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"T.","family":"Lefebvre","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Katholieke Universiteit Leuven,                         Celestijnenlaan 300B, 3001 Leuven, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H.","family":"Bruyninckx","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Katholieke Universiteit Leuven,                         Celestijnenlaan 300B, 3001 Leuven, Belgium,"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2005,8]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/70.265932"},{"key":"e_1_2_1_2_1","unstructured":"Bar-Shalom Y. and Fortmann T. 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