Viability-based computation of spatially constrained minimum time trajectories for an autonomous underwater vehicle: implementation and experiments

  • Authors:

  • A. Tinka;S. Diemer;L. Madureira;E. B. Marques;J. Borges De Sousa;R. Martins;J. Pinto;J. Estrela Da Silva;A. Sousa;P. Saint-Pierre;A. M. Bayen

  • Affiliations:

  • Systems Engineering, Department of Civil and Environmental Engineering, University of California at Berkeley;Ecole Nationale Suprieure des Mines de Paris;Electrical and Computer Engineering Department, Faculty of Engineering, Porto University;Electrical and Computer Engineering Department, Faculty of Engineering, Porto University;Electrical and Computer Engineering Department, Faculty of Engineering, Porto University;Electrical and Computer Engineering Department, Faculty of Engineering, Porto University;Electrical and Computer Engineering Department, Faculty of Engineering, Porto University;Electrical Engineering Department, Porto Polytechnic Institute;Electrical and Computer Engineering Department, Faculty of Engineering, Porto University;Department of Mathematics, Université Paris-Dauphine, Paris;Systems Engineering, Department of Civil and Environmental Engineering, University of California at Berkeley

  • Venue:
  • ACC'09 Proceedings of the 2009 conference on American Control Conference
  • Year:
  • 2009

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Abstract

A viability algorithm is developed to compute the constrained minimum time function for general dynamical systems. The algorithm is instantiated for a specific dynamics (Dubin's vehicle forced by a flow field) in order to numerically solve the minimum time problem. With the specific dynamics considered, the framework of hybrid systems enables us to solve the problem efficiently. The algorithm is implemented in C using epigraphical techniques to reduce the dimension of the problem. The feasibility of this optimal trajectory algorithm is tested in an experiment with a Light Autonomous Underwater Vehicle (LAUV) system. The hydrodynamics of the LAUV are analyzed in order to develop a low-dimension vehicle model. Deployment results from experiments performed in the Sacramento River in California are presented, which show good performance of the algorithm.