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Generation of State Transition Models Using Simulations for Unmanned Sea Surface Vehicle Trajectory Planning

[+] Author Affiliations
Atul Thakur, Petr Svec, Satyandra K. Gupta

University of Maryland at College Park, College Park, MD

Paper No. DETC2011-48624, pp. 835-845; 11 pages
  • ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 6: 35th Mechanisms and Robotics Conference, Parts A and B
  • Washington, DC, USA, August 28–31, 2011
  • Conference Sponsors: Design Engineering Division and Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5483-9
  • Copyright © 2011 by ASME


Trajectory planning for unmanned sea surface vehicles (USSVs) in high sea-states is a challenging problem. Large and somewhat stochastic ocean forces can cause significant deviations in the motion of the USSV. Controllers are employed to reject disturbances and get back on the desired trajectory. However, the position uncertainty can be still high and needs to be accounted for during the trajectory planning to circumvent collisions with the obstacles. We model the motion of the USSV as Markov decision process and use a trajectory planning approach based on stochastic dynamic programming. A key component of our approach is the estimation of transition probabilities from one state to another when executing an action. In this paper, we present algorithms to generate state transition model using Monte Carlo simulation of USSV motion. Our simulations are based on potential flow based 6-DOF dynamics. Using this approach, we are able to generate dynamically feasible trajectories for USSVs that exhibit safe behaviors in high sea-states in the vicinity of static obstacles.

Copyright © 2011 by ASME



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