Progressively communicating rich telemetry from autonomous underwater vehicles via relays

Abstract

As analysis of imagery and environmental data plays a greater role in mission construction and execution, there is an increasing need for autonomous marine vehicles to transmit this data to the surface. Without access to the data acquired by a vehicle, surface operators cannot fully understand the state of the mission. Communicating imagery and high-resolution sensor readings to surface observers remains a significant challenge -as a result, current telemetry from free-roaming autonomous marine vehicles remains limited to 'heartbeat' status messages, with minimal scientific data available until after recovery. Increasing the challenge, longdistance communication may require relaying data across multiple acoustic hops between vehicles, yet fixed infrastructure is not always appropriate or possible.In this thesis I present an analysis of the unique considerations facing telemetry systems for free-roaming Autonomous Underwater Vehicles (AUVs) used in exploration. These considerations include high-cost vehicle nodes with persistent storage and significant computation capabilities, combined with human surface operators monitoring each node. I then propose mechanisms for interactive, progressive communication of data across multiple acoustic hops. These mechanisms include wavelet-based embedded coding methods, and a novel image compression scheme based on texture classification and synthesis. The specific characteristics of underwater communication channels, including high latency, intermittent communication, the lack of instantaneous end-to-end connectivity, and a broadcast medium, inform these proposals. Human feedback is incorporated by allowing operators to identify segments of data that warrant higher quality refinement, ensuring efficient use of limited throughput. I then analyze the performance of these mechanisms relative to current practices.Finally, I present CAPTURE, a telemetry architecture that builds on this analysis. CAPTURE draws on advances in compression and delay tolerant networking to enable progressive transmission of scientific data, including imagery, across mul-3 tiple acoustic hops. In concert with a physical layer, CAPTURE provides an endto-end networking solution for communicating science data from autonomous marine vehicles. Automatically selected imagery, sonar, and time-series sensor data are progressively transmitted across multiple hops to surface operators. Human operators can request arbitrarily high-quality refinement of any resource, up to an error-free reconstruction. The components of this system are then demonstrated through three field trials in diverse environments on SeaBED, OceanServer and Bluefin AUVs, each in different software architectures.

Visual summaries for low-bandwidth semantic mapping with autonomous underwater vehicles

“…Considering that images are captured every 3 seconds, the total mission time to capture 2800 images is over 2 hours. With the current state of the art in acoustic image transmission being approximately one full-resolution 1-megapixel image every 15 minutes [24], we estimate that about 8 images could be transmitted back within the course of a mission. Therefore, we set the summary set size to 8.…”

Advances in integrating autonomy with acoustic communications for intelligent networks of marine robots

“…One exception is Murphy's work [50,56], in which he uses transform compression (e.g. the discrete wavelet transform) to source encode imagery and historical time series of scalar data.…”

A State Observation Technique for Highly Compressed Source Coding of Autonomous Underwater Vehicle Position

“…One exception is Murphy's work [8], [9], in which he uses transform compression (e.g. the discrete wavelet transform) to source encode imagery and historical time series of scalar data.…”