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Path Following for Autonomous Vehicle Navigation with Inherent Safety and Dynamics Margin
Swiss Federal Institute of Technology Zürich, Switzerland.
Stanford University, California, USA.ORCID-id: 0000-0003-3513-8854
Swiss Federal Institute of Technology Zürich, Switzerland.
2008 (engelsk)Inngår i: IEEE Intelligent Vehicles Symposium, Proceedings, Vols 1-3, Piscataway, N.J.: IEEE Press, 2008, s. 910-915Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

This paper addresses the path following problem for autonomous Ackermann-like vehicle navigation. A control strategy that takes into account both kinodynamic and configuration space constraints of the vehicle, denoted as Traversability-Anchored Dynamic Path Following (TADPF) controller is presented. It ensures secure vehicle commands in presence of obstacles, based on traversability information given by a global navigation function. By additionally using a reference point on the global smooth path, the local vicinity path configuration with respect to the vehicle is taken explicitly into account to ensure smooth and stable path following. Furthermore, a previously developed Sliding Mode Path Following (SMPF) controller that results in fast convergence rate and low path following error but which does not consider kinodynamic constraints, is augmented by the the kinodynamic and configuration space constraints check of the TADPF controller. The new proposed control strategy denoted as TADPF-SMPF controller thus combines advantageous characteristics of both original control strategies for path following, yielding inherent safety and vehicle dynamics margin. All three control strategies are verified in simulation, whereas the TADPF and TADPF-SMPF path following schemes are also verified experimentally. © 2008 IEEE.

sted, utgiver, år, opplag, sider
Piscataway, N.J.: IEEE Press, 2008. s. 910-915
Serie
IEEE Intelligent Vehicles Symposium, ISSN 1931-0587
Emneord [en]
Dynamics, Intelligent vehicle highway systems, Navigation, Navigation systems, Wave functions
HSV kategori
Identifikatorer
URN: urn:nbn:se:hh:diva-20130DOI: 10.1109/IVS.2008.4621276ISI: 000262050800152ISBN: 978-142442569-3 OAI: oai:DiVA.org:hh-20130DiVA, id: diva2:577020
Konferanse
IEEE Intelligent Vehicles Symposium, Eindhoven University of Technology, Eindhoven, The Netherlands, June 4-6, 2008
Tilgjengelig fra: 2012-12-14 Laget: 2012-12-14 Sist oppdatert: 2018-01-12bibliografisk kontrollert

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