Deep-Learning-Based Accurate Identification of Warehouse Goods for Robot Picking Operations

Liu, Huwei; Li, Zhou; Zhao, Jianhua; Wang, Fan; Yang, Jianglong; Liang, Kaibo; Li, Zhaochan

doi:10.3390/su14137781

Cited by 9 publications

(4 citation statements)

References 31 publications

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“…To ensure competitiveness and successful development in the digital world, it delivers innovative solutions that improve the quality, efficiency, and flexibility of manufacturing processes. [16][17][18]…”

Section: Literature Reviewmentioning

confidence: 99%

Modular Intelligent Control System in the Pre-assembly Stage

Micieta,

Macek,

Binasova

et al. 2024

Preprint

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This paper addresses the approach of fully automated intelligent control systems in production organizations, focusing on intelligent production systems research. The overview highlights a gap in control operations ahead of assembly as single-purpose control machines are frequently employed, which is precisely their drawback. Conversely, there are accurate multipurpose measurement centers that do not provide total quality control over the manufactured parts because of the cost and length of the measuring procedure. The study's primary objective was to create an intelligent modular control system, whose concept enables it to control various components. The purpose of the modular intelligent control system is to keep an eye on each module's quality during the pre-assembly phase. This system ensures exact control over the production process of modules by integrating sophisticated sensors, diagnostic tools, and intelligent control mechanisms. The technology makes it possible to monitor several parameters and important quality features. Integrated sensors and diagnostic technologies identify possible inaccuracies and disparities, guaranteeing prompt diagnosis of issues in specific modules. The system's intelligent control algorithms streamline the production process and guarantee synchronization between individual modules to achieve consistent quality and performance. One advantage of implementing the solution is that, on average, inspection time is reduced by 40 to 60% as compared to the prior manual inspection approach. In addition, full measurement data archiving was made available, and finally, the human element—which introduced a substantial error rate into this process—was removed. This system also helps to increase the project's overall efficiency, predictability, and safety while allowing for quick adjustments by standards and requirements.

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Section: Literature Reviewmentioning

confidence: 99%

Modular Intelligent Control System in the Pre-assembly Stage

Micieta,

Macek,

Binasova

et al. 2024

Preprint

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show abstract

“…The design of modules for control operations [16][17][18] is based on a set of ideal components, which are subsequently used to modify real components available on the market. These can subsequently be suitably modified based on customer demand.…”

Section: General Model Of Control Modulesmentioning

confidence: 99%

Modular Intelligent Control System in the Pre-Assembly Stage

Micieta,

Macek,

Binasova

et al. 2024

Electronics

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This paper presents a novel approach to developing fully automated intelligent control systems for use within production-based organizations, with a specific focus on advancing research into intelligent production systems. This analysis underscores a prevailing deficiency in control operations preceding assembly, where single-purpose control machines are commonly utilized, thus presenting inherent limitations. Conversely, while accurate multipurpose measurement centers exist, they often fail to deliver comprehensive quality control for manufactured parts due to cost and time constraints associated with the measuring process. The primary aim in this study was to develop an intelligent modular control system capable of overseeing the production of diverse components effectively. The modular intelligent control system is designed to meticulously monitor the quality of each module during the pre-assembly phase. By integrating sophisticated sensors, diagnostic tools, and intelligent control mechanisms, this system ensures precise control over module production processes. It facilitates the monitoring of multiple parameters and critical quality features, while integrated sensors and diagnostic methods promptly identify discrepancies and inaccuracies, enabling the swift diagnosis of issues within specific modules. The system’s intelligent control algorithms optimize production processes and ensure synchronization among individual modules, thereby ensuring consistent quality and performance. Notably, the implementation of this solution reduces inspection time by an average of 40 to 60% compared to manual inspection methods. Moreover, the system enables the comprehensive archiving of measurement data, eliminating the substantial error rates introduced by human involvement in the inspection process. Furthermore, the system enhances overall project efficiency, predictability, and safety, while allowing for rapid adjustments in order to meet standards and requirements. This innovative approach represents a significant advancement in intelligent control systems for use in production organizations, offering substantial benefits in terms of efficiency, accuracy, and adaptability.

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“…Before deploying a cobot in an enterprise, it is important to perform an analysis of the processes to be automated. It is necessary to determine exactly which steps in the process can be performed by the cobot and what the requirements are for adapting the production process [16]. Deploying a cobot also usually involves training the personnel who will be working with the cobot and ensuring the necessary safety precautions are in place [17].…”

Section: Literature Reviewmentioning

confidence: 99%

Interfacing the Control Systems of Enterprise-Level Process Equipment with a Robot Operating System

Micieta,

Binasova,

Marcan

et al. 2023

Electronics

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The analytical section of this paper deals with theoretical knowledge, considering the latest trends in the subject area. To achieve the successful implementation of cobots based on operating systems in manufacturing systems, it is necessary to pay attention specifically to the interfacing of Robot Operating Systems with the control systems of manufacturing systems at the process level of an enterprise. In the practical section, an algorithm with well-defined steps towards the successful implementation of cobots in holonic manufacturing processes is proposed. By setting up an experimental workstation in a laboratory, the proposed procedures are verified at the end of the paper. The creation of a design for the implementation of a collaborative robot with a human represents the missing link in the whole chain of commercial applications of the latest trends from the field of robotic systems in the industrial sphere. The main contributions of this study include the establishment of a communication channel between cobots and programmable logic PLCs (Programmable logic controllers), and an experimental verification of the proposed solution for the implementation of a cooperating robot with a human in a manufacturing system in the laboratory. Another advantage of this paper is the creation of a new procedure for conducting a risk analysis of cooperating robots and multipurpose autonomous devices.

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Deep-Learning-Based Accurate Identification of Warehouse Goods for Robot Picking Operations

Cited by 9 publications

References 31 publications

Modular Intelligent Control System in the Pre-assembly Stage

Modular Intelligent Control System in the Pre-assembly Stage

Modular Intelligent Control System in the Pre-Assembly Stage

Interfacing the Control Systems of Enterprise-Level Process Equipment with a Robot Operating System

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