Fuel and Time Optimal USV Trajectory Planning under Flexible Refueling Constraints

TL;DR

This paper develops a dynamic programming approach to optimize the fuel and time efficiency of unmanned surface vehicle trajectories considering ocean currents and flexible refueling, providing Pareto tradeoff analyses.

Contribution

It introduces a novel multi-objective trajectory optimization framework incorporating refueling stops and ocean currents for USVs, solved via dynamic programming.

Findings

Pareto fronts illustrating fuel-time tradeoffs

Optimal trajectories adapt to low fuel and counterproductive currents

Insights into vehicle behavior under various fuel and current conditions

Abstract

This paper addresses the problem of trajectory optimization for an unmanned surface vehicle while considering direction-dependent ocean currents and flexible refueling constraints. This work is motivated by the rising interest in developing autonomous navigation technology for commercial, scientific, and military applications for ocean-bound unmanned surface vehicles. Relevant literature on such vehicles has addressed energy-efficient trajectory optimization and time-efficient trajectory optimization. However, the application of trajectory optimization techniques which include refueling stops and multi-objective optimization remains relatively unexplored. We address this open challenge by formulating the trajectory design problem as a nonconvex mixed-integer optimization program with a multi-objective cost function. Then, we apply dynamic programming to solve this optimization program for fuel and time optimal trajectories. We synthesize these results into a series of Pareto fronts which demonstrates the tradeoff between fuel consumption and trip time for a prototypical route with direction-dependent ocean currents. Furthermore, the optimal trajectories for which we solve illustrate the changes in the vehicle's behavior as the fuel level becomes low, and as the vehicle encounters counterproductive ocean currents. Our results indicate several meaningful insights about the overall trajectory optimization process.