Final Technical Report For The Enhancement Of Autonomous Marine Vehicle Testing In The South Florida Testing Facility Range

The purpose of this grant was to carry out the six scientific experiments on the South Florida Testing Facility (SFTF) Range. In addition to the enhancements to the range, work was performed on all six with some being successfully completed while research continues on the long term tasks

Design and Control of a Flight-Style AUV with Hovering Capability

The small flight-style Delphin AUV is designed to evaluate the performance of a long range survey AUV with the additional capability to hover and manoeuvre at slow speed. Delphin’s hull form is based on a scaled version of Autosub6000, and in addition to the main thruster and control surfaces at the rear of the vehicle, Delphin is equipped with four rim driven tunnel thrusters. In order to reduce the development cycle time, Delphin was designed to use commercial-off-the-shelf (COTS) sensors and thrusters interfaced to a standard PC motherboard running the control software within the MS Windows environment. To further simplify the development, the autonomy system uses the State-Flow Toolbox within the Matlab/Simulink environment. While the autonomy software is running, image processing routines are used for obstacle avoidance and target tracking, within the commercial Scorpion Vision software. This runs as a parallel thread and passes results to Matlab via the TCP/IP communication protocol. The COTS based development approach has proved effective. However, a powerful PC is required to effectively run Matlab and Simulink, and, due to the nature of the Windows environment, it is impossible to run the control in hard real-time. The autonomy system will be recoded to run under the Matlab Windows Real-Time Windows Target in the near future. Experimental results are used to demonstrating the performance and current capabilities of the vehicle are presented

An Underwater Sensor Network with DBMS Concept

In this paper is a concept of  a technique of sending and receiving message below water. There are several ways of employing such communication but the most common is using hydrophones. Under water communication is difficult due to factors like multi-path propagation, time variations of the channel, small available bandwidth and strong signal attenuation, especially over long ranges. In underwater communication there are low data rates compared to terrestrial communication, since underwater communication uses acoustic waves instead of electromagnetic waves.  we present a novel platform for underwater sensor  networks to  be used  for long-term monitoring of coral reefs  and  fisheries.    The  sensor  network consists  of static and  mobile  underwater sensor  nodes.   The  nodes  communicate  point-to-point using  a novel high-speed optical  communication system  integrated into  the  TinyOS stack,   and they  broadcast using an acoustic  protocol  integrated in the TinyOS stack.    The  nodes  have  a variety of sensing  capabilities,   including cameras,  water   temperature,  and  pres- sure.    The  mobile  nodes  can  locate  and  hover  above  the static nodes for data mining  and  they  can perform  network maintenance functions such  as deployment, relocation, and recovery.   In this  paper  we describe  the  hardware and  soft- ware  architecture of this  underwater sensor  network.   We then  describe  the  optical  and  acoustic  networking protocols and  present  experimental networking and  data collected  in a pool, in rivers,  and  in the  ocean.  Finally, we describe  our experiments with  mobility for data mining  in this  network. Keywords: Mobile sensor networks, underwater networks, data minin

Autonomous surveillance for biosecurity

The global movement of people and goods has increased the risk of biosecurity threats and their potential to incur large economic, social, and environmental costs. Conventional manual biosecurity surveillance methods are limited by their scalability in space and time. This article focuses on autonomous surveillance systems, comprising sensor networks, robots, and intelligent algorithms, and their applicability to biosecurity threats. We discuss the spatial and temporal attributes of autonomous surveillance technologies and map them to three broad categories of biosecurity threat: (i) vector-borne diseases; (ii) plant pests; and (iii) aquatic pests. Our discussion reveals a broad range of opportunities to serve biosecurity needs through autonomous surveillance.Comment: 26 pages, Trends in Biotechnology, 3 March 2015, ISSN 0167-7799, http://dx.doi.org/10.1016/j.tibtech.2015.01.003. (http://www.sciencedirect.com/science/article/pii/S0167779915000190

Design, construction, and operation of an unmanned underwater vehicle

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (leaf [62]).The practical usage of unmanned underwater vehicles (UUVs) is limited by vehicle and operation cost, difficulty in accurate navigation, and communication between the vehicle and operator. The "Rex 2" UUV employs a system design where a submersible is connected to a float at the water's surface by means of a tether. By maintaining a surface expression, high-bandwidth radio communication to the operator becomes possible, and GPS may be used for accurate navigation. This arrangement allows the freedom of movement characteristic of untethered autonomous underwater vehicles (AUVs), while maintaining the live operator control and communication found with tethered remotely operated vehicles (ROVs). Expanding on the design and field experiences with the MIT AUV Lab's first Reef Explorer UUV, Rex 2 was designed to be inexpensive, easy to deploy, adaptable to various payloads, and simple to use. Rex 2 was designed, built, and operated in a number of ocean field tests, validating the utility of the vehicle and system concept.by Dylan Owens.S.M

Lossy compression and real-time geovisualization for ultra-low bandwidth telemetry from untethered underwater vehicles

Submitted in partial fulfillment of the requirements for the degree of Master of Science at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution September 2008Oceanographic applications of robotics are as varied as the undersea environment itself. As underwater robotics moves toward the study of dynamic processes with multiple vehicles, there is an increasing need to distill large volumes of data from underwater vehicles and deliver it quickly to human operators. While tethered robots are able to communicate data to surface observers instantly, communicating discoveries is more difficult for untethered vehicles. The ocean imposes severe limitations on wireless communications; light is quickly absorbed by seawater, and tradeoffs between frequency, bitrate and environmental effects result in data rates for acoustic modems that are routinely as low as tens of bits per second. These data rates usually limit telemetry to state and health information, to the exclusion of mission-specific science data. In this thesis, I present a system designed for communicating and presenting science telemetry from untethered underwater vehicles to surface observers. The system's goals are threefold: to aid human operators in understanding oceanographic processes, to enable human operators to play a role in adaptively responding to mission-specific data, and to accelerate mission planning from one vehicle dive to the next. The system uses standard lossy compression techniques to lower required data rates to those supported by commercially available acoustic modems (O(10)-O(100) bits per second). As part of the system, a method for compressing time-series science data based upon the Discrete Wavelet Transform (DWT) is explained, a number of low-bitrate image compression techniques are compared, and a novel user interface for reviewing transmitted telemetry is presented. Each component is motivated by science data from a variety of actual Autonomous Underwater Vehicle (AUV) missions performed in the last year.National Science Foundation Center for Subsurface Sensing and Imaging (CenSSIS ERC