Abstract
Robotic and automation field is continuously in expansion. Robotic systems are now operating in unknown and dynamic environments. Therefore, they must not only classify sensory pattern but also determine the decision and action to be made. The well making decision of robot will depend on its efficiency when processing raw sensor data. In this work, we propose an innovative approach for robot intelligent perception and decision making process. We investigate the ability of deep learning methods to be brought to bear on robotic system decision making and control. Our challenging researches consist on providing robots the ability to autonomously recognize obstacle without a pre-programming need. For this purpose, we design a deep learning based framework to compute a high-quality convolutional Neural Network (CNN) model for image classification. The designed approach is labeled Enhanced Elite CNN Propagation Method. Simulations demonstrate the effectiveness of robot decision making when exploring its environment based on our approach.
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Loussaief, S., Abdelkrim, A. (2019). Robot Intelligent Perception Based on Deep Learning. In: Benavente-Peces, C., Slama, S., Zafar, B. (eds) Proceedings of the 1st International Conference on Smart Innovation, Ergonomics and Applied Human Factors (SEAHF). SEAHF 2019. Smart Innovation, Systems and Technologies, vol 150. Springer, Cham. https://doi.org/10.1007/978-3-030-22964-1_7
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DOI: https://doi.org/10.1007/978-3-030-22964-1_7
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