The Application of Anomaly Detection Methods in the Robot Industry

DOI

10.2991/978-94-6463-512-6_25How to use a DOI?

Keywords

Machine Learning; Algorithm; Anomaly detection

Abstract

This paper reviews anomaly detection systems based on machine learning and algorithms for detecting and quantifying various types of defects in robotic assembly lines. Throughout history, the main method in anomaly detection has been human. By 2000, Artificial Intelligence (AI) is used more and more in anomaly detection. This paper summarizes the deep-learning model, reinforcement learning model, and You Only Look Once (YOLO) Algorithm. Convolutional Neural Networks (CNNs) is a network from deep-learning model which can detect in real-life environments the data given by the camera and sensor. CNNs can perform detection by extracting local features and the space between neighboring pixels. Reinforcement learning uses a technique called Q-learning and also deep Q-networks (DQNs). Q-learning uses the data from previous data set and learns to make choices that optimize long-term returns. This can significantly improve overall efficiency and productivity. Another algorithm mentioned in this paper is YOLO Algorithm also known as the You Only Look Once algorithm. The algorithm collects the data from the image and analyzes the pixels in each image. By using YOLO, the system can optimize the allocation of resources to ensure the correct identification and installation of each component. The research papers show the efficiency and work accuracy of the AI algorithm and machine learning model. The learning process facilitates autonomous improvement of the system.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jiacheng Wen
PY  - 2024
DA  - 2024/09/23
TI  - The Application of Anomaly Detection Methods in the Robot Industry
BT  - Proceedings of the 2024 International Conference on Artificial Intelligence and Communication (ICAIC 2024)
PB  - Atlantis Press
SP  - 218
EP  - 224
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-512-6_25
DO  - 10.2991/978-94-6463-512-6_25
ID  - Wen2024
ER  -