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 vehicles have shown their versatility and cost-effectiveness in using automated approaches to detect a range of features. Targeting them for in-situ observation and sampling capabilities for frontal zones then provides an important tool for characterizing rapid and episodic changes. We introduce a novel momentum-based front detection (MBFD) algorithm which utilizes a Kalman filter and a momentum accumulator function to identify significant temperature gradients associated with upwelling fronts. MBFD is designed to work at a number of levels including onboard an AUV, on-shore with a sparse real-time data stream and post-experiment on a full resolution data set gathered by a vehicle. Such a multi-layered approach plays an important role in mixed human-robot decision making for oceanographers making coordinated sampling and asset allocation strategies in large multi-robot field experiments in the coastal ocean.

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 techniques. Increased dependence of scientists on such robots has permeated scientific data gathering with future field campaigns involving these platforms as well as on entire infrastructure of people, processes and software, on shore and at sea. Recent field experiments carried out with a number of surface and underwater platforms give clues to how these technologies are coalescing and need to work together. We highlight one such confluence and describe a future trajectory of needs and desires for field experiments with autonomous marine robotic platforms. Our 2010 inter-disciplinary experiment in the Monterey Bay involved multiple platforms and collaborators with diverse science goals. One important goal was to enable situational awareness, planning and collaboration before, during and after this large-scale collaborative exercise. We present the overall view of the experiment and describe an important shore-side component, the Oceanographic Decision Support System (ODSS), its impact and future directions leveraging such technologies for field experiments.