GPU based generation of state transition models using simulations for unmanned surface vehicle trajectory planning

  • Authors:

  • Atul Thakur;Petr Svec;Satyandra K. Gupta

  • Affiliations:

  • Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA;Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA;Department of Mechanical Engineering and Institute for Systems Research, University of Maryland, College Park, MD 20742, USA

  • Venue:
  • Robotics and Autonomous Systems
  • Year:
  • 2012

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Abstract

This paper describes GPU based algorithms to compute state transition models for unmanned surface vehicles (USVs) using 6 degree of freedom (DOF) dynamics simulations of vehicle-wave interaction. A state transition model is a key component of the Markov Decision Process (MDP), which is a natural framework to formulate the problem of trajectory planning under motion uncertainty. The USV trajectory planning problem is characterized by the presence of large and somewhat stochastic forces due to ocean waves, which can cause significant deviations in their motion. Feedback controllers are often employed to reject disturbances and get back on the desired trajectory. However, the motion uncertainty can be significant and must be considered in the trajectory planning to avoid collisions with the surrounding obstacles. In case of USV missions, state transition probabilities need to be generated on-board, to compute trajectory plans that can handle dynamically changing USV parameters and environment (e.g., changing boat inertia tensor due to fuel consumption, variations in damping due to changes in water density, variations in sea-state, etc.). The 6 DOF dynamics simulations reported in this paper are based on potential flow theory. We also present a model simplification algorithm based on temporal coherence and its GPU implementation to accelerate simulation computation performance. Using the techniques discussed in this paper we were able to compute state transition probabilities in less than 10 min. Computed transition probabilities are subsequently used in a stochastic dynamic programming based approach to solve the MDP to obtain trajectory plan. Using this approach, we are able to generate dynamically feasible trajectories for USVs that exhibit safe behaviors in high sea-states in the vicinity of static obstacles.