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Intelligent autonomous navigation of robot kuka youbot

Abstract:

We report the successful integration of deep learning approach for autonomous robot KUKA YouBot navigation. The incorporation of deep learning approach was carried out through the combination among Robot Operating System, Python software and Open Source Computer Vision Library working environments. The Robot Operating System with Hydro medusa distribution provides the managing of odometry, kinematics and path planning nodes. The combination of all nodes allows the simulation of the autonomous robot navigation by using Gazebo and provides the implementation of the algorithms in simulated and real platforms. Python software improves the communication tasks taking advantage of data processing tools in the deep learning process. Then, Open Source Computer Vision Library allows the integration of the deep learning approach by using the Single Shot Detector algorithm which provides robustness in velocity and precision which mainly allows the human detection by using a trained neuronal network. The integration of Robot Operating System, Python software and Open Source Computer Vision Library is a promising architecture for providing smart autonomous navigation capability for different applications like: social robots, human rescue, precision farming, industry and so on.