{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T01:46:43Z","timestamp":1767923203354,"version":"3.49.0"},"reference-count":64,"publisher":"SAGE Publications","issue":"9","license":[{"start":{"date-parts":[[2012,7,3]],"date-time":"2012-07-03T00:00:00Z","timestamp":1341273600000},"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":["The International Journal of Robotics Research"],"published-print":{"date-parts":[[2012,8]]},"abstract":"<jats:p> This two-part paper discusses the analysis and control of legged locomotion in terms of N-step capturability: the ability of a legged system to come to a stop without falling by taking N or fewer steps. We consider this ability to be crucial to legged locomotion and a useful, yet not overly restrictive criterion for stability. In this part (Part 1), we introduce a theoretical framework for assessing N-step capturability. This framework is used to analyze three simple models of legged locomotion. All three models are based on the 3D Linear Inverted Pendulum Model. The first model relies solely on a point foot step location to maintain balance, the second model adds a finite-sized foot, and the third model enables the use of centroidal angular momentum by adding a reaction mass. We analyze how these mechanisms influence N-step capturability, for any N &gt; 0. Part 2 will show that these results can be used to control a humanoid robot. <\/jats:p>","DOI":"10.1177\/0278364912452673","type":"journal-article","created":{"date-parts":[[2012,7,4]],"date-time":"2012-07-04T22:20:57Z","timestamp":1341440457000},"page":"1094-1113","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":402,"title":["Capturability-based analysis and control of legged locomotion, Part 1: Theory and application to three simple gait models"],"prefix":"10.1177","volume":"31","author":[{"given":"Twan","family":"Koolen","sequence":"first","affiliation":[{"name":"Institute for Human and Machine Cognition, Pensacola, FL, USA"},{"name":"Delft University of Technology, the Netherlands"}]},{"given":"Tomas","family":"de Boer","sequence":"additional","affiliation":[{"name":"Delft University of Technology, the Netherlands"}]},{"given":"John","family":"Rebula","sequence":"additional","affiliation":[{"name":"University of 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