Object Identification, Classification and Avoidance in 3-D Underwater Automated Surveillance

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9506771 Valavanis The central mbjective of the proposed work is: object identification, classification and avoidance in 3-D underwater automated surveillance. The motivation behind this objective is monitoring and safeguarding wetlands, coastal fisheries and deep water polluted environments, which are among the most valuable national resources. The specific application domain, directly related to the proposed work, is surveying and monitoring Louisiana's wetlands and shallow water fisheries in collaboration with the Southern Science Center (SSC) - previously known as the National Wetlands Research Center - and the recently established National Marine Fisheries Services (NMFS), both located at the University of Southwestern Louisiana's (USL's) research park. This proposal capitalizes on already available resources, technology and algorithms, developed for other applications, but adapts and further modifies and enhances them for the proposed automated surveillance problem. The end result will be: a detailed methodology/approach for the real-time sensor based autonomous control and navigation of an underwater vehicle, capable of automated surveillance in sensitive environments. There will be no field studies and/or vehicle related experiments at this stage. Three specific objectives will be accomplished: i)Development of a detailed architecture for underwater vehicle motion control. ii) Implementation and experimental verification of an already derived total color difference (TCD) measure and total color difference tolerance (TCDT) thresholds for underwater object identification. iii) Enhancement and verification of a recently derived potential based panel method for autonomous vehicle motion planning in a 3-D workshop environment. ***