{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T17:29:39Z","timestamp":1778347779956,"version":"3.51.4"},"reference-count":34,"publisher":"SAGE Publications","issue":"13","license":[{"start":{"date-parts":[[2016,7,11]],"date-time":"2016-07-11T00:00:00Z","timestamp":1468195200000},"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":[[2016,11]]},"abstract":"<jats:p>This paper presents a Robust Constrained Learning-based Nonlinear Model Predictive Control (RC-LB-NMPC) algorithm for path-tracking in off-road terrain. For mobile robots, constraints may represent solid obstacles or localization limits. As a result, constraint satisfaction is required for safety. Constraint satisfaction is typically guaranteed through the use of accurate, a priori models or robust control. However, accurate models are generally not available for off-road operation. Furthermore, robust controllers are often conservative, since model uncertainty is not updated online. In this work our goal is to use learning to generate low-uncertainty, non-parametric models in situ. Based on these models, the predictive controller computes both linear and angular velocities in real-time, such that the robot drives at or near its capabilities while respecting path and localization constraints. Localization for the controller is provided by an on-board, vision-based mapping and navigation system enabling operation in large-scale, off-road environments. The paper presents experimental results, including over 5 km of travel by a 900 kg skid-steered robot at speeds of up to 2.0 m\/s. The result is a robust, learning controller that provides safe, conservative control during initial trials when model uncertainty is high and converges to high-performance, optimal control during later trials when model uncertainty is reduced with experience.<\/jats:p>","DOI":"10.1177\/0278364916645661","type":"journal-article","created":{"date-parts":[[2016,5,17]],"date-time":"2016-05-17T20:34:15Z","timestamp":1463517255000},"page":"1547-1563","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":196,"title":["Robust Constrained Learning-based NMPC enabling reliable mobile robot path tracking"],"prefix":"10.1177","volume":"35","author":[{"given":"Chris J.","family":"Ostafew","sequence":"first","affiliation":[{"name":"University of Toronto Institute for Aerospace Studies"}]},{"given":"Angela P.","family":"Schoellig","sequence":"additional","affiliation":[{"name":"University of Toronto Institute for Aerospace 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