Fish catching by visual servoing and observed intelligence of the fish

This paper presents a vision related technique for a manipulator real-time visual servoing to catch fish and intelligence observed in the behaviours of the fish to escape from the net attached at robot's hand. The visual recognition method utilizes the global search feature of a genetic algorithm (GA) and a local search technique of the GA and also the unprocessed gray-scale image called here as raw-image, in order to perform recognition of a known target object being imaged. Also in GA process, the computation of the fitness function is based on the configuration of an object model designated as surface-strips model. The raw-image is used since it is more tolerant of contrast variations from an input image to the next one, and moreover does not require any filtering processing time, which is useful for real-time recognition. The global GA is utilized together with the local GA in order to recognize the target shape and detect the position and orientation simultaneously, and to increase the GA's convergence speed so as to provide faster and better recognition results. In order to evaluate the effectiveness of the proposed scene recognition method, experiments to track a fish by hand-eye camera and catch the fish with a net attached at hand of the manipulator have been done. The success to catch has shown the effectiveness of the proposed technique for manipulator real-time visual servoing. In this presentation, we concentrate the stressing point on the observed intelligent behaviour of the fish.