Abstract
In recent years, there has been significant concern about the impacts of offshore oil spill plumes and harmful algal blooms on the coastal ocean environment and biology, as well as on the human populations adjacent to these coastal regions. Thus, it has become increasingly important to determine the 3D extent of these ocean features (“plumes”) and how they evolve over time. The ocean environment is largely inaccessible to sensing directly by humans, motivating the need for robots to intelligently sense the ocean for us. In this paper, we propose the use of an autonomous underwater vehicle (AUV) network to track and predict plume shape and motion, discussing solutions to the challenges of spatiotemporal data aliasing (coverage versus resolution), underwater communication, AUV autonomy, data fusion, and coordination of multiple AUVs. A plume simulation is also developed here as the first step toward implementing behaviors for autonomous, adaptive plume tracking with AUVs, modeling a plume as a sum of Fourier orders and examining the resulting errors. This is then extended to include plume forecasting based on time variations, and future improvements and implementation are discussed.
Highlights
The underwater environment itself is hazardous to humans, as we cannot survive without air to breathe and our bodies cannot withstand the ambient pressure deep underwater, yet we could not exist without the presence of large bodies of water on our planet
We present a simulated plume environment sampled by autonomous underwater vehicle (AUV), from which we attempt to reconstruct the plume as a sum of Fourier orders as an initial estimate of the plume shape
This paper provides a conceptual outline of the requirements for implementing adaptive, autonomous plume tracking using a network of AUVs, including a first-pass simulation of detecting and reconstructing plume shapes solely from AUV sample points, with the example of a plume of oil originating from the sea floor
Summary
The underwater environment itself is hazardous to humans, as we cannot survive without air to breathe and our bodies cannot withstand the ambient pressure deep underwater, yet we could not exist without the presence of large bodies of water on our planet. These vehicles demonstrate the best motion and stability control at speeds between 1 and 1.8 m/s, with navigational error of about 1%–5% of the distance traveled between surfacing to get a position fix via GPS. Acoustic communication structure (AUV-to-AUV and AUVto-ship/lab) that has been actively developed and refined in recent years to give virtually real-time updates (delays on the order of minutes) of scientific and navigational data (more details on this are found in the Goby project documentation [5, 6]) Linking all of these pieces together is the autonomy system on board each AUV. These behaviors autonomously and adaptively control the heading, speed, and depth of the vehicle, depending on the behavior the AUV operators have chosen to run (more on this in Section 4 and [7, 8])
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Distributed Sensor Networks
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
