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Journal : The Indonesian Journal of Computer Science

Building an Automated Guided Vehicle Based on UWB Technology Haryono; Santoso, Handri
The Indonesian Journal of Computer Science Vol. 13 No. 6 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i6.4487

Abstract

The development of automated guided vehicles (AGVs) for indoor environments necessitates precise positioning technology to enable accurate navigation within confined spaces. Ultra-Wideband (UWB) technology has proven to be a leading solution for this purpose, known for its high accuracy, low latency, and resilience to interference. This study presents a specialized approach to AGV localization within a room, utilizing UWB technology to achieve reliable movement and positioning. We conducted a comparative analysis of two UWB modules, DWM1000 and DWM1001, evaluating their performance and suitability for AGV applications. Although both modules provide high accuracy, the DWM1001 was chosen due to its integrated microcontroller, simplified setup, and enhanced compatibility with indoor navigation. The DWM1001’s efficient integration and power management make it ideal for environments requiring precise and dependable AGV operation. This paper details the methodology for selecting the DWM1001 and demonstrates how it enables robust AGV navigation with minimal drift, achieving a positioning accuracy of approximately 10 cm—an acceptable margin for indoor applications. Through rigorous testing and evaluation, we observed consistent performance, validating the DWM1001 as an effective solution for small-scale AGV systems. This approach not only provides a reliable foundation for deploying UWB technology in compact indoor settings but also addresses a gap in current research on high-precision, small-scale AGV localization.
Machine Health in a Click: A Website for Real-Time Machine Condition Monitoring Rochadiani, Theresia Herlina; Santoso, Handri; Aprilia, Novia Pramesti; Laurenso, Justin; Suhandi, Vartin
The Indonesian Journal of Computer Science Vol. 12 No. 6 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i6.3592

Abstract

Globalization in the current digital era has made it easier to use information technology to obtain fast and accurate information. One source of information is a website that can be used to monitor machine conditions in the industry. A good machine maintenance strategy is needed to maintain and increase machine productivity. Therefore, this research aims to build a website to monitor machine conditions in real-time. The machine condition is monitored using sushi sensors to track parameters such as temperature, acceleration, and velocity. Deep learning analysis is then used to identify anomalies in the machine. Using the SCRUM method, this website was successfully built. From the results of tests carried out using unit testing and integrated testing, every feature on this website can run well and according to user needs.