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Fast, continuous state path smoothing to improve navigation accuracy
Örebro universitet, Institutionen för naturvetenskap och teknik. (AASS, MR&O Lab)ORCID-id: 0000-0002-2953-1564
Örebro universitet, Institutionen för naturvetenskap och teknik. (AASS, MR&O Lab)
Örebro universitet, Institutionen för naturvetenskap och teknik. SCANIA AB, Södertälje, Sweden. (AASS, MR&O Lab)
Örebro universitet, Institutionen för naturvetenskap och teknik. (AASS, MR&O Lab)ORCID-id: 0000-0002-6013-4874
Vise andre og tillknytning
2015 (engelsk)Inngår i: IEEE International Conference on Robotics and Automation (ICRA), 2015, IEEE Computer Society, 2015, s. 662-669Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Autonomous navigation in real-world industrial environments is a challenging task in many respects. One of the key open challenges is fast planning and execution of trajectories to reach arbitrary target positions and orientations with high accuracy and precision, while taking into account non-holonomic vehicle constraints. In recent years, lattice-based motion planners have been successfully used to generate kinematically and kinodynamically feasible motions for non-holonomic vehicles. However, the discretized nature of these algorithms induces discontinuities in both state and control space of the obtained trajectories, resulting in a mismatch between the achieved and the target end pose of the vehicle. As endpose accuracy is critical for the successful loading and unloading of cargo in typical industrial applications, automatically planned paths have not be widely adopted in commercial AGV systems. The main contribution of this paper addresses this shortcoming by introducing a path smoothing approach, which builds on the output of a lattice-based motion planner to generate smooth drivable trajectories for non-holonomic industrial vehicles. In real world tests presented in this paper we demonstrate that the proposed approach is fast enough for online use (it computes trajectories faster than they can be driven) and highly accurate. In 100 repetitions we achieve mean end-point pose errors below 0.01 meters in translation and 0.002 radians in orientation. Even the maximum errors are very small: only 0.02 meters in translation and 0.008 radians in orientation.

sted, utgiver, år, opplag, sider
IEEE Computer Society, 2015. s. 662-669
Serie
Proceedings - IEEE International Conference on Robotics and Automation, ISSN 1050-4729
HSV kategori
Forskningsprogram
Datavetenskap
Identifikatorer
URN: urn:nbn:se:oru:diva-47425DOI: 10.1109/ICRA.2015.7139250ISI: 000370974900096Scopus ID: 2-s2.0-84938229043ISBN: 9781479969234 (tryckt)OAI: oai:DiVA.org:oru-47425DiVA, id: diva2:894653
Konferanse
2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA, May 26-30, 2015
Forskningsfinansiär
Knowledge FoundationTilgjengelig fra: 2016-01-15 Laget: 2016-01-15 Sist oppdatert: 2018-01-10bibliografisk kontrollert

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