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A fast color information setup based on evolutionary programming (EP) like particles swarm optimization (EPSO) for the manipulator control system is examined in this paper. The first step for a manipulator to grasp and place color objects into the correct location is to correctly identify the RGB or the corresponding hue, saturation, value (HSV) color model. The commonly used method to determine the thresholds of HSV range is manual tuning, but it is time-consuming to find the best boundary to segment the color image. This paper proposes a new method to learn color information, which is executed by semiautomatic learning. At first, the watershed algorithm incorporates user interactions to segment the color image and obtain the target image. Then, the comparison between the target image and the original image is utilized to build a lookup table (LUT) of color information, where three HSV thresholds are learned by PSO methods. Because the convergence speed of well-known PSO algorithms is slow and may be stuck in the local minimum, we present the EPSO method realized by applying EP to the PSO method. Moreover, a novel approach is investigated to escape the local minimum supposing the particles are stuck in the local minimum. Finally, both the numerical and experimental results demonstrate that the developed approach can not only rapidly learn the thresholds to segment a color image but can also jump out the local minimum.
Li et al. (Thu,) studied this question.