Abstract

Ocean ecosystems have spatiotemporal variability and dynamic complexity that require a long-term deployment of an autonomous underwater vehicle for data collection. A new generation of long-range autonomous underwater vehicles (LRAUVs), such as the Slocum glider and Tethys-class AUV, has emerged with high endurance, long-range, and energy-aware capabilities. These new vehicles provide an effective solution to study different oceanic phenomena across multiple spatial and temporal scales. For these vehicles, the ocean environment has forces and moments from changing water currents which are generally on the order of magnitude of the operational vehicle velocity. Therefore, it is not practical to generate a simple trajectory from an initial location to a goal location in an uncertain ocean, as the vehicle can deviate significantly from the prescribed trajectory due to disturbances resulted from water currents. Since state estimation remains challenging in underwater conditions, feedback planning must incorporate state uncertainty that can be framed into a stochastic energy-aware path planning problem. This article presents an energy-aware feedback planning method for an LRAUV utilizing its kinematic model in an underwater environment under motion and sensor uncertainties. Our method uses ocean dynamics from a predictive ocean model to understand the water flow pattern and introduces a goal-constrained belief space to make the feedback plan synthesis computationally tractable. Energy-aware feedback plans for different water current layers are synthesized through sampling and ocean dynamics. The synthesized feedback plans provide strategies for the vehicle that drive it from an environment’s initial location toward the goal location. We validate our method through extensive simulations involving the Tethys vehicle’s kinematic model and incorporating actual ocean model prediction data.

Highlights

  • Ocean ecosystems are complex and have high variability in both time and space

  • It is not practical to generate a simple navigation trajectory from an initial location to a goal location in a dynamic ocean environment because the vehicle can deviate from its trajectory due to motion noise and cannot estimate its state accurately in underwater environments due to sensor noise

  • We develop our energy-aware feedback planning algorithm based on the Partially Observable Monte Carlo Planning (POMCP) algorithm (Silver and Veness, 2010)

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Summary

Introduction

Ocean ecosystems are complex and have high variability in both time and space. ocean scientists must collect data over long periods to obtain a synoptic view of ocean ecosystems and understand their spatiotemporal variability. Autonomous underwater vehicles (AUVs) are increasingly being used for studying different oceanic phenomena such as oil spill mapping (Kinsey et al, 2011), harmful algal blooms (Das et al, 2010), phytoplankton and zooplankton communities (Kalmbach et al, 2017), and coral bleaching (Manderson et al, 2017) These AUVs can be classified into two categories: 1) propeller-driven vehicles, such as the Dorado class, which can move fast and gather numerous sensor observations but are limited in deployment time to multiple hours; and 2) minimally-actuated vehicles such as drifters, profiling floats, and gliders that move slower, but can remain on deployment for tens of days to multiple weeks. A new generation of the long-range autonomous underwater vehicles (LRAUVs), i.e., Tethys, combines the advantages of both minimally-actuated and propeller-driven AUVs (Hobson et al, 2012) These LRAUVs can move quickly for hundreds of kilometers, float with water currents, and carry a broad range of data collection sensors. It is not practical to generate a simple navigation trajectory from an initial location to a goal location in a dynamic ocean environment because the vehicle can deviate from its trajectory due to motion noise and cannot estimate its state accurately in underwater environments due to sensor noise

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