Thom Maughan

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In this paper we describe an integrated goal-oriented control architecture for onboard decision-making for AUVs. Onboard planning and execution is augmented by state estimation of perceived features of interest in the coastal ocean, to drive platform adaptation. The partitioned architecture is a collection of coordinated control loops, with a recurring(More)
With the advent of Autonomous Underwater Vehicles (AUVs) and other mobile platforms, marine robotics have had substantial impact on the oceanographic sciences. These systems have allowed scientists to collect data over temporal and spatial scales that would be logistically impossible or prohibitively expensive using traditional ship-based measurement(More)
We have designed, built, tested and fielded a decision support system which provides a platform for situational awareness, planning, observation, archiving and data analysis. While still in development, our inter-disciplinary team of computer scientists, engineers, biologists and oceanographers has made extensive use of our system in at-sea experiments(More)
We present AUV survey methodologies to track and sample an advecting patch of water. Current AUV-based sampling rely primarily on geographic waypoint track-line surveys that are suitable for static or slowly changing features. When studying dynamic, rapidly evolving oceanographic features, such methods at best introduce error through insufficient spatial(More)
Fronts have been recognized as hotspots of intense biological activity and are important targets for observation to understand coastal ecology and transport in a changing ocean. With high spatial and temporal variability, detection and event response for frontal zones is challenging for robotic platforms like autonomous underwater vehicles (AUVs). These(More)
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