SBIR/STTR Award attributes
We propose to integrate the capabilities we have developed for real-time habitat processing on shipboard with the HabCam towed vehicle, into an autonomous vehicle with 3D reconstruction of seafloor topology, substrate classification, single target identification, hyperspectral imaging for physiological and chemical information, and plankton classification as an index of ecosystem health. Together, these data streams represent a full description of habitat characterizing a variety of organisms, communities, and biodiversity. In Phase I, we designed and built a prototype HARIM (Habitat Aware Reconnaissance and Imaging Module), which is a complete package of sensors and processing capability to survey habitat at depths from 0 to 600m. In this Phase II proposal, we will integrate information being collected by HARIM with a REMUS-600 navigational system to create a dynamic sampling capability. Deep Learning models of habitat will be built from stereo images in a variety of habitats, Habitat Suitability Modeling will be used to project habitats using statistical inference, and topic modeling will be used to label habitat components and specific targets to ascertain the degree of information content. A dynamic sampling scheme will understand when habitat information is changing or when it is stable and guide the vehicle to maximize information content. The market for habitat characterization is large- from wind farm siting and monitoring to oil and gas prospecting and environmental monitoring for exploration of novel environments and assessment of damage to both shallow and deep coral reef systems. This Phase II will complete the software workflow for HARIM and test it under rigorous field conditions. We will commercialize the product by rugedizing the hardware and hardening the software. Potential customers include the US Navy, offshore windfarm developers, oil and gas developers and prospectors, and the NOAA Office of Ocean Exploration and Research.
