Book Chapter: Smart manufacturing workplace safety with virtual training, AR and haptic technologies

Dwijayanti, Suci (2022) Book Chapter: Smart manufacturing workplace safety with virtual training, AR and haptic technologies. In: Accidents due to undetected fires have caused huge losses in various contexts in the world, such as in offices, manufacturing workplaces, residential areas, even in forest areas. The evacuation process in the firefighting system requires an effective and. The Institution of Engineering and Technology, pp. 375-399. ISBN 978-1-83953-415-7

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Abstract

Accidents due to undetected fires have caused huge losses in various contexts in the world, such as in offices, manufacturing workplaces, residential areas, even in forest areas. The evacuation process in the firefighting system requires an effective and fast response. The source of fire must be found quickly to prevent it from spreading. Image processing based on an object detection system, it is believed, can circumvent this problem. The method for detecting objects is performed by the You Only Look Once (YOLO) algorithm in real-time. The most suitable YOLO model for the system is the Tiny YOLO VOC model which was built with the Darkflow framework. Besides detecting the fires, the robot can be used to ease the search process and firefighting efforts. However, it needs planning to fit the robots with the system optimized to do the best in the minimum time possible. In robotics, finding the fastest path can be solved by embedding the A* algorithm in the robot. A* algorithm is one of the artificial intelligence methods for finding the fastest path. Not many studies have applied this algorithm to the wheel robots to find the shortest path. Therefore, this A* method will be used for finding the fastest path to find the fire with accuracy by finding the best distance and route with the coordinate system. In this study, the wheel robots had three missions, finding the fire, extinguishing the fire, and returning to the starting points. The system accuracy for those missions was 100%, 83.33%, and 50%, respectively.

Item Type:	Book Section
Subjects:	#3 Repository of Lecturer Academic Credit Systems (TPAK) > Articles Access for TPAK (Not Open Sources)
Divisions:	03-Faculty of Engineering > 20201-Electrical Engineering (S1)
Depositing User:	Ms Suci Dwijayanti
Date Deposited:	06 May 2023 05:20
Last Modified:	06 May 2023 05:20
URI:	http://repository.unsri.ac.id/id/eprint/99939

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