Abstract
With the rapid development of information technology and widespread use of the Internet of Things, machine intelligence will undoubtedly emerge as a leading research topic in the future. The main purpose of the present research is to incorporate an image recognition system into a robotic arm motion to achieve automatic classification. First, we upload captured images to a PC for classification process and use chess patterns to conduct a sampling test. Next, when the system identifies these patterns as proper chess patterns, the robotic arm grabs the objects and moves them to designated locations. The project is divided into two main sections: image recognition and robotic arm motion. In the image recognition section, we use Keras and the Tensorflow open source learning machine to build a convolutional neural network model. Then, we use a learning model network that is a considerably more compact variant of the VGGNet network in the image recognition system. With this model, we achieve a recognition accuracy of 95%. In the robotic arm section, we use a five-axis robotic arm and an Arduino Uno board as the controller. We design the Denavit–Hartenberg parameters of the arm and calculate the direct (inverse) kinematics parameters to plan its trajectory. Thereafter, we use MATLAB software to simulate prototype processes, such as grabbing, moving, and placing. Finally, we import the program into the controller so that the robotic arm can execute classification based on the chess pattern.
Highlights
MotivationWith the continuous vigorous development of information technology, the prevalence of the Internet of Things, and the intelligence of machines, it is the future trend
When the recognition rate is higher than 95%, the computer transfers the recognition result to the Arduino Uno board through a serial port
It controls the robotic arm move to a specific location
Summary
With the continuous vigorous development of information technology, the prevalence of the Internet of Things, and the intelligence of machines, it is the future trend. Many industries are constantly seeking new profit models and new technology upgrades, and the industry hopes to obtain higher prices at the least cost. The mains way to deal with the profits of the industry is to reduce labor costs. The aim of the present study is to combine the use of image recognition techniques with robotic arm operation by uploading captured images onto a PC for classification. We use a chess pattern to conduct the sampling test, and after the system recognizes it as a Department of Mechanical and Electro-mechanical Engineering, Tamkang University, Taipei. Proper chess pattern, the robotic arm grabs the objects and moves them to the designated location
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