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Previously, route planning in holes drilling process has been taken for granted due to its automated process, in nature. But as the interest to make Computer Numerical Control machines more efficient, there have been a steady increase in number of studies for the past decade. Many researchers proposed algorithms that belong into Computational Intelligence, due to their simplicity and ability to obtain optimal result. In this study, an optimization algorithm based on Gravitational Search Algorithm is proposed for solving route optimization in holes drilling process. The proposed approach involves modeling and simulation of Gravitational Search Algorithm. The performance of the algorithm is benchmark with one case study that had been frequently used by previous researchers. The result indicates that the proposed approach performs better than most of the literatures.

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In this paper, a robust surveillance system to enable robots to detect humans in indoor environments is proposed. The proposed method is based on fusing information from thermal and depth images which allows the detection of human even under occlusion. The proposed method consists of three stages; pre-processing, ROI generation and object classification. A new dataset was developed to evaluate the performance of the proposed method. The experimental results show that the proposed method is able to detect multiple humans under occlusions and illumination variations.

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