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A Distributed Computing Real-Time Safety System of Collaborative Robot
Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences. Intema Sp. z o.o., POL.
Intema Sp Zoo, POL.
Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
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2020 (English)In: Elektronika ir Elektrotechnika, ISSN 1392-1215, Vol. 26, no 2, p. 4-14Article in journal (Refereed) Published
Abstract [en]

Robotization has become common in modern factories due to its efficiency and cost-effectiveness. Lots of robots and manipulators share their workspaces with humans what could lead to hazardous situations causing health damage or even death. This article presents a real-time safety system applying the distributed computing paradigm for a collaborative robot. The system consists of detection/sensing modules connected with a server working as decision-making system. Each configurable sensing module pre-processes vision information and then sends to the server the images cropped to new objects extracted from a background. After identifying persons from the images, the decision-making system sends a request to the robot to perform pre-defined action. In the proposed solution, there are indicated three safety zones defined by three different actions on a robot motion. As identification method, state-of-the-art of Machine Learning algorithms, the Histogram of Oriented Gradients (HOG), Viola-Jones, and You Only Look Once (YOLO), have been examined and presented. The industrial environment tests indicated that YOLOv3 algorithm outperformed other solutions in terms of identification capabilities, false positive rate and maximum latency.

Place, publisher, year, edition, pages
Kauno Technologijos Universitetas , 2020. Vol. 26, no 2, p. 4-14
Keywords [en]
Artificial intelligence, Collaborative robots, Neural networks, Safety system
National Category
Signal Processing Robotics
Identifiers
URN: urn:nbn:se:bth-19446DOI: 10.5755/j01.eie.26.2.25757ISI: 000529319700001Scopus ID: 2-s2.0-85086876420OAI: oai:DiVA.org:bth-19446DiVA, id: diva2:1430321
Note

open access

Available from: 2020-05-14 Created: 2020-05-14 Last updated: 2023-06-07Bibliographically approved
In thesis
1. Sensors and Algorithms in Industry 4.0: Security and Health Preservation Applications
Open this publication in new window or tab >>Sensors and Algorithms in Industry 4.0: Security and Health Preservation Applications
2021 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Globalisation and technological digitisation have triggered an Industry 4.0. revolution.  The core of this revolution is autonomisation of complex processes, which require expert knowledge. The technical foundations of Industry 4.0 are IoT, Big Data and AI technologies. Nowadays, autonomous systems are widely used to increase human and environmental safety and to prevent health degradation.  Such non-industrial, life related applications demand high reliability as well as precision and accuracy, which challenge engineering science. 

The thesis objective is to provide suitable solutions for non-invasive, automated, and autonomous systems used for life protection and health maintenance. The proposed solutions enable non-invasive measurements by means of vision and acoustic sensors. The presented methods and systems are designed based on an analytical assessment of existing technologies and algorithms. New hardware solutions, signal and data processing methods, as well as classification and decision-making algorithms are proposed. Where required, additional customisations and modifications are applied. The systems and methods presented have been modelled and rigorously validated, and subsequently implemented and verified in a real environment.  

The scope of the thesis includes the assessment of functional requirements, precision, accuracy and reliability of life-related technological systems. It covers an analytical evaluation of proposed methods and algorithms of filtration, feature extraction, also detection, localization, identification, and classification of objects. The application fields are health monitoring, nature observation and facilitating collaborative frameworks in modern factories. 

The thesis specifically focuses on methods and algorithms of autonomous decision making concerning the risk of heart disease, the threat of fatal collision of rare birds with man-made structures and the prevention of accidents in modern robotised factories. It also deals with the implementation of the Industry 4.0 fundamentals, which are smart sensing, IoT and AI methods optimised to improve the system performance in a broad sense. The applied distributed computing method and machine-to-machine communication are aimed at limiting the data stream at an early stage of the decision-making process, and thus ensure the system’s cost-effectiveness. From the thesis, one can understand how the Industry 4.0 paradigm can contribute to autonomisation of compound processes and to increase system performance, without compromising its affordability.

The thesis is divided into two parts. The first, Prolegomena provides an overview of the sensors and algorithms applicable to industrial safety along with human health and nature preservation. This part also visualizes the relationships and interactions among the articles comprising the second part named Papers. In general, each of the enclosed six papers deals with the problem of autonomisation of complex processes in real-time and in a regular environment.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2021
Series
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 4
Keywords
Acoustic Sensor, Artificial Intelligence, Autonomisation, Classification, Detection, Feature Extraction, Health Preservation, Identification, Internet of Things, Machine Learning, Multi-Sensor System, Safety System, Vision System
National Category
Engineering and Technology Signal Processing Computer Systems
Research subject
Systems Engineering
Identifiers
urn:nbn:se:bth-21387 (URN)978-91-7295-424-3 (ISBN)
Public defence
2021-09-10, Zoom, Campus Gräsvik, Karlskrona, 13:15 (English)
Opponent
Supervisors
Available from: 2021-05-11 Created: 2021-05-10 Last updated: 2021-09-20Bibliographically approved

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Gradolewski, DawidDziak, DamianJachimczyk, BartoszKulesza, Wlodek

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Gradolewski, DawidDziak, DamianJachimczyk, BartoszMundlamuri, Siva TejaPrakash, Chandran G.Kulesza, Wlodek
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