Abstract

The authors develop an approach to a “best” time path for Autonomous Underwater Vehicles conducting oceanographic measurements under uncertain current flows. The numerical optimization tool DIDO is used to compute hybrid minimum time and optimal survey paths for a sample of currents between ebb and flow. A simulated meta-experiment is performed where the vehicle traverses the resulting paths under different current strengths per run. The fastest elapsed time emerges from a payoff table. A multi-objective function is then used to weigh the time to complete a mission versus measurement inaccuracy due to deviation from the desired survey path.

Highlights

  • The Naval Academy operates a variety of Autonomous Underwater Vehicles (AUVs) for educational and research purposes, including Remote Environmental Measuring Units (REMUS), made by Hydroid [1]

  • REMUS can follow any trajectory specified by waypoints from launch to destination; one that has properties in support of its mission such as “minimum time” and “minimum energy.”

  • Simulations indicate that the AUV will traverse the “minimum time” path computed for assumed current strength vw0 0.75vmax in the shortest time, on average, given any actual current strength, vw

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Summary

Introduction

The Naval Academy operates a variety of Autonomous Underwater Vehicles (AUVs) for educational and research purposes, including Remote Environmental Measuring Units (REMUS), made by Hydroid [1]. The authors develop an approach to a “best” time path for Autonomous Underwater Vehicles conducting oceanographic measurements under uncertain current, which is the most significant environmental factor affecting elapsed time or expended energy when following a desired trajectory. A multi-objective function is used to weigh the time to complete a mission versus measurement inaccuracy due to deviation from the desired path. An underwater vehicle can only operate for a finite amount of time before its inertial navigation system develops too much error, causing its survey path to insert even more measurement error.

Modeling Assumptions
Analytical Approach to an Extremal Path
Numerical Approach to the Optimum Path
The Minimum Time Problem
Actual Current vw Coincides with Assumed vw0
Actual Current vw Differs from Assumed vw0
Findings
Conclusions

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