Smart Factory Navigation: Sensor-Driven Access Point Selection for Automated Guided Vehicles

Authors Gundreddi Deepika Reddy1, Nageswara Rao Medikondu1, , T. Vijaya Kumar1, Vinjamuri Venkata Kamesh2, Perumalla Vijaya Kumar3
Affiliations

1Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation Vaddeswaram, Guntur, 522302, India

2Department of Mechanical Engineering, Aditya Engineering College (A), Surampalem, East Godavari District, Andhra Pradesh-533437, India

3Department of Mechanical Engineering, Lakireddy Bali Reddy College of Engineering, Mylavaram, Krishna (Dt.), Andhra Pradesh, 521230, India

Е-mail medikondu1979@gmail.com
Issue Volume 16, Year 2024, Number 3
Dates Received 15 April 2024; revised manuscript received 17 June 2024; published online 28 June 2024
Citation Gundreddi Deepika Reddy, Nageswara Rao Medikondu, et al., J. Nano- Electron. Phys. 16 No 3, 03019 (2024)
DOI https://doi.org/10.21272/jnep.16(3).03019
PACS Number(s) 01.50.hv, 07.05.Hd
Keywords Sensor-based navigation, Access point selection, Industry 4.0, Sensor fusion Real-time decision making and Path optimization.
Annotation

In smart factory settings, ensuring the optimal navigation of Automated Guided Vehicles (AGVs) is essential for streamlining material handling operations and enhancing overall efficiency. This paper introduces a novel Sensor-Based Access Point Selection Strategy (SBAPSS) designed specifically to enhance the navigation capabilities of AGVs within smart manufacturing environments. The SBAPSS harnesses a comprehensive array of sensor data, including inputs from laser scanners, vision systems, ultrasonic sensors, and proximity sensors, to dynamically evaluate and select optimal access points for AGV navigation routes. Utilizing real-time sensor information, the SBAPSS algorithm employs sophisticated decision-making mechanisms to identify the most favorable access points based on multiple criteria. These criteria encompass factors such as obstacle detection, proximity to designated loading/unloading stations, traffic congestion, and path optimization. By integrating sensor-driven intelligence into the access point selection process, AGVs can adaptively adjust their navigation paths to circumvent obstacles, avoid collisions, and optimize travel routes in real-time. The effectiveness and reliability of the SBAPSS are demonstrated through extensive simulation studies and experimental validations conducted in representative smart factory environments. Results indicate significant improvements in AGV navigation efficiency, throughput, and safety, thereby validating the efficacy of the proposed strategy. Moreover, the SBAPSS's ability to seamlessly integrate with existing AGV control systems underscores its practical feasibility and scalability for deployment in industrial settings. This innovative sensor-driven approach represents a substantial advancement in AGV navigation methodologies, offering a robust solution tailored to the demands of modern smart manufacturing facilities. By empowering AGVs with intelligent decision-making capabilities, the SBAPSS contributes to the realization of agile, adaptive, and autonomous material handling systems in the industry 4.0 era.

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