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Real-Time Mobile Robot Perception Based on Deep Learning Detection Model

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New Technologies, Development and Application V (NT 2022)


The recent advances in deep learning models have enabled the robotics community to utilize their potential. The mobile robot domain on which deep learning has the most influence is scene understanding. Scene understanding enables mobile robots to exist and execute their tasks through processes such as object detection, semantic segmentation, or instance segmentation. A perception system that can recognize and locate objects in the scene is of the highest importance for achieving autonomous behavior of robotic systems. Having that in mind, we develop the mobile robot perception system based on deep learning. More precisely, we utilize an accurate and fast Convolution Neural Network (CNN) model to enable a mobile robot to detect objects in its scene in a real-time manner. The integration of two CNN models (SSD and MobileNet) is performed and implemented on mobile robot RAICO (Robot with Artificial Intelligence based COgnition). The experimental results show that the proposed perception system enables a high degree of object recognition with satisfying inference speed, even with limited processing power provided by Nvidia Jetson Nano integrated within RACIO.

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This research has been financially supported by the Ministry of Education, Science and Technological Development of the Serbian Government, through the project “Integrated research in macro, micro, and nano mechanical engineering – Deep learning of intelligent manufacturing systems in production engineering” (contract No. 451-03-9/2021-14/200105), and by the Science Fund of the Republic of Serbia, Grant No. 6523109, AI – MISSION 4.0, 2020–2022.

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Correspondence to Aleksandar Jokić .

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Jokić, A., Petrović, M., Miljković, Z. (2022). Real-Time Mobile Robot Perception Based on Deep Learning Detection Model. In: Karabegović, I., Kovačević, A., Mandžuka, S. (eds) New Technologies, Development and Application V. NT 2022. Lecture Notes in Networks and Systems, vol 472. Springer, Cham.

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