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
