Autonomous Data Collection with Limited Time for Underwater Vehicles

Autonomous Data Collection with Limited Time for Underwater Vehicles

Abstract:

This paper studies the problem of autonomous data collection where an underwater vehicle is required to reach several target regions within a specified time limit. The proposed approach takes into account the vehicle dynamics, the time-varying ocean currents, and the obstacles in the region in order to effectively plan a collision-free and dynamically feasible trajectory whose time duration does not exceed the time limit. When the time limit makes it impossible to reach every target, the approach seeks to reduce the penalty accrued by the target regions that are not visited. The approach combines sampling-based motion planning with constraint-based solvers. In fact, a constraint-based solver searches a navigation roadmap to compute bounded tours which minimize the accrued penalty. Sampling-based motion planning is then used to expand a motion tree along these tours. Unsuccessful tour expansions are penalized to promote exploration of alternative tours. Simulation and field experiments demonstrate the efficiency of the approach in planning collision-free and dynamically feasible trajectories that reduce the accrued penalty.

 


Comments are closed.