Exploring the environment using multi-robot systems is a fundamental process that most automated applications depend on. This paper presents a hybrid decentralized task assignment approach based on Partially Observable Semi-Markov Decision Processes called HDec-POSMDPs, which are general models for multi-robot coordination and exploration problems in which robots can make their own decisions according to its local data with limited communication between the robot team. In this paper, a variety of multi-robot exploration algorithms and their comparison have been tackled. These algorithms, which have been taken into consideration, are dependent on different parameters. Collectively, there are five metrics maximize the total exploration percentage, minimize overall mission time, reduce the number of hops in the networked robots, reduce the energy consumed by each robot and minimize the number of turns in the path from the start pose cells to the target cells. Therefore, a team of identical mobile robots is used to perform coordination and exploration process in an unknown cell-based environment. The performance of the task depends on the strategy of coordination among the robots involved in the team. Therefore, the proposed approach is implemented, tested and evaluated in MRESim computer simulator, and its performance is compared with different coordinated exploration strategies for different environments and different team sizes. The experimental results demonstrate a good performance of the proposed approach compared to the four existing approaches.
Environment Exploration is the basic process that most of Multi Robot Systems applications depend on it. The exploration process performance depends on the coordination strategy between the robots participating in the team. In this paper the coordination of Multi Robot Systems in the exploration process is surveyed, and the performance of different Multi Robot Systems exploration strategies is contrasted and analyzed for different environments and different team sizes.
Abstract:In this paper a new binarization algorithm for ancient manuscripts and historical documents with bleeding noise has been proposed. This algorithm consists of three primary processes. In the first process, a given gray-scale image has been classified into three classes: black-foreground pixels class, white-background pixels class and confused pixels class. In the second process, the confused pixels class will be classified into either of the two black and white classes. The classified image was cut into rectangles using the confused-pixels vertical and horizontal histograms. Each rectangle is a sub-image containing a region of the image with pixels having similar properties. The third is a voting process where a threshold value is selected to binarize each sub-image separately. Seven thresholding values driven from six different global binarization techniques contribute to the voting process. The binarized image is the collection of the sub-images binarization results. Four different measuring metrics have been used to evaluate the results of the proposed algorithm. The performance of the algorithm has been compared with two widely used binarization algorithms which yield a significant improvement in the binarization process of ancient manuscripts and historical documents with bleeding noise.
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