{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:59:12Z","timestamp":1760151552895,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,3,28]],"date-time":"2022-03-28T00:00:00Z","timestamp":1648425600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents a local planning approach that is targeted for pseudo-omnidirectional vehicles: that is, vehicles that can drive sideways and rotate on the spot. This local planner\u2014MSDU\u2013is based on optimal control and formulates a non-linear optimization problem formulation that exploits the omni-motion capabilities of the vehicle to drive the vehicle to the goal in a smooth and efficient manner while avoiding obstacles and singularities. MSDU is designed for a real platform for mobile manipulation where one key function is the capability to drive in narrow and confined areas. The real-world evaluations show that MSDU planned paths that were smoother and more accurate than a comparable local path planner Timed Elastic Band (TEB), with a mean (translational, angular) error for MSDU of (0.0028 m, 0.0010 rad) compared to (0.0033 m, 0.0038 rad) for TEB. MSDU also generated paths that were consistently shorter than TEB, with a mean (translational, angular) distance traveled of (0.6026 m, 1.6130 rad) for MSDU compared to (0.7346 m, 3.7598 rad) for TEB.<\/jats:p>","DOI":"10.3390\/s22072588","type":"journal-article","created":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T21:45:51Z","timestamp":1648590351000},"page":"2588","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Local Planner for Accurate Positioning for a Multiple Steer-and-Drive Unit Vehicle Using Non-Linear Optimization"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2953-1564","authenticated-orcid":false,"given":"Henrik","family":"Andreasson","sequence":"first","affiliation":[{"name":"Centre for Applied Autonomous Sensor Systems (AASS), \u00d6rebro University, 701 82 \u00d6rebro, Sweden"}]},{"given":"Jonas","family":"Larsson","sequence":"additional","affiliation":[{"name":"ABB Corporate Research, 722 26 V\u00e4ster\u00e5s, Sweden"}]},{"given":"Stephanie","family":"Lowry","sequence":"additional","affiliation":[{"name":"Centre for Applied Autonomous Sensor Systems (AASS), \u00d6rebro University, 701 82 \u00d6rebro, Sweden"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Siciliano, B., Sciavicco, L., Villani, L., and Oriolo, G. 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