A model-based real-world scene recognition using local/global search of a GA for manipulator real-time visual servoing

This paper presents a vision related technique for a manipulator real-time visual servoing. The 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 rawimage, 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. The global GA is utilized together with the local GA in order to increase the GA's convergence speed so as to provide fester and better recognition results. In order to evaluate the effectiveness of the proposed scene recognition method, experiments have been done using real images. The results have shown the effectiveness of the proposed technique for manipulator real-time visual servoing.