Abstract
The process of recognizing manufacturing parts in real time requires fast, accurate, small, and low-power-consumption sensors. Here, we describe a method to extract descriptors from several objects observed from a wide range of angles in a three-dimensional space. These descriptors define the dataset, which allows for the training and further validation of a convolutional neural network. The classification is implemented in reconfigurable hardware in an embedded system with an RGB sensor and the processing unit. The system achieved an accuracy of 96.67% and a speed 2.25× faster than the results reported for state-of-the-art solutions. Our proposal is 655 times faster than implementation on a PC. The presented embedded system meets the criteria of real-time video processing and it is suitable as an enhancement for the hand of a robotic arm in an intelligent manufacturing cell.
Highlights
Deep Learning and ItsIn the field of manufacturing robotics, it is of the highest relevance to count with optical sensors able to provide the system with a sensory mechanism that feeds back the actions of one or more robotic arms that collaborate or interact with a human
This paper focuses on the second precept, that is, of vision systems attached to a robotic arm
We propose an object recognition method implemented in an field-programmable gate array (FPGA) in which tasks such as image capture and preprocessing are automated, and at a further stage, an object classification process is conducted
Summary
Deep Learning and ItsIn the field of manufacturing robotics, it is of the highest relevance to count with optical sensors able to provide the system with a sensory mechanism that feeds back the actions of one or more robotic arms that collaborate or interact with a human. Robotics vision allows for automatic learning to achieve fast and accurate object or pattern recognition, which is sometimes a complicated, dangerous, and strenuous task, and humans are usually not involved in the loop These automatic detection mechanisms must be precise when selecting the proper object, which is key to the success of the manufacturing process [1]. A robotic vision system must be able to recognize and discriminate parts or objects from any angle and indicate its position to the robot These vision systems are either fixed to the manufacturing cell [4] or the last end of the robotic arm [5]. In the former, systems have the advantage that, regardless of the overall weight of the system (camera, communications, and computer), vision
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