Manipulator visual servoing and tracking of fish using a genetic algorithm

This paper presents the real-time visual servoing of a manipulator and its tracking strategy of a fish, by employing a genetic algorithm (GA) and the unprocessed gray-scale image termed here as `raw-image'. The raw-image is employed to shorten the control period, since it has more tolerance of contrast variations occurring within an object, and between one input image and the next one. GA is employed in a method called 1-step-GA evolution. In this way, for every generational step of the GA process, the found results, which express the deviation of the target in the camera frame, are output for control purposes. These results are then used to determine the control inputs of the PD-type controller. Our proposed GA-based visual servoing has been implemented in a real system, and the results have shown its effectiveness by successfully tracking a moving target fish.