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A General Technique for Real-Time Robotic Simulation in Manufacturing System

Ting-Hsuan Chien1,*, Cheng-Yan Siao2, Rong-Guey Chang2

1 Department of Computer Science and Information Management, Providence University, Taichung, 433303, Taiwan
2 Department of Computer Science and Information Engineering, Advanced Institute of Manufacturing with High-Tech Innovations, National Chung Cheng University, Chiayi, 621005, Taiwan

* Corresponding Author: Ting-Hsuan Chien. Email: email

(This article belongs to this Special Issue: Machine Learning and Deep Learning for Transportation)

Intelligent Automation & Soft Computing 2021, 29(3), 827-838. https://doi.org/10.32604/iasc.2021.018256

Abstract

This paper describes a real-time simulator that allows the user in the factories to simulate arbitrary interaction between machinery and equipment. We discussed in details not only the general technique for developing such a real-time simulator but also the implementation of the simulator in its actual use. As such, people on the production line could benefit from observing and controlling robots in factories for preventing or reducing the severity of a collision, using the proposed simulator and its related technique. For that purpose, we divided the simulator into two main models: the real-time communication model and the simulation model. For the communication model, we present the distributed messaging server structure and the database semaphore technique for handling the real-time peer-to-peer (P2P) connections and control. For the simulation model, we used parallel programming and the general-purpose GPU (GPGPU) technique to speed up the processing time of the simulation. In the paper, we exhaustively explain how the simulator’s techniques are designed and implemented and verify two real world models with robots and robot-operating systems (ROS). Moreover, we compare the results between our technique and the traditional method.

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Cite This Article

T. Chien, C. Siao and R. Chang, "A general technique for real-time robotic simulation in manufacturing system," Intelligent Automation & Soft Computing, vol. 29, no.3, pp. 827–838, 2021. https://doi.org/10.32604/iasc.2021.018256



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