Proponent/Claimant
Hapsari Peni Agustin Tjahyaningtijas, Parama Diptya Widayaka, Raissa Alfatikarani, Sayyidul Aulia Alamsyah, Pradini Puspitaningayu, L Endah Cahya Ningrum, Jessie R Paragas
Abstract
The integration of deep learning technologies in object detection has significantly enhanced the capabilities of wheeled mobile robots, making them more efficient and intelligent in navigating complex environments. These technologies enable more accurate pattern recognition, adaptability to diverse environmental conditions, and improved autonomous decision-making capabilities. This study aims to explore the evolution and current trends in object detection for wheeled mobile robots, with a specific focus on the application of deep learning technologies as a foundational driver for system advancements. The objectives include analyzing the contribution of deep learning to improving object detection accuracy, system efficiency, and the robots' adaptability to dynamic environments. The methodology employed in this study is a Systematic Literature Review (SLR), comprising several key steps: formulating research questions, identifying relevant research sources, utilizing specific keywords for data collection, disseminating the gathered data, and analyzing the findings to address the research questions. Data is sourced exclusively from the Scopus digital database, focusing on publications from 2019 to 2024. The collected data, formatted in RIS, is subsequently analyzed and visualized using VOSviewer. The outcomes of this research include insights into the growth of research publications in recent years, the identification of key trends in object detection methodologies for wheeled mobile robots, the exploration of interconnections between critical concepts in the field, and the mapping of the knowledge network based on relevant keywords. Special emphasis is placed on the pivotal role of deep learning technologies in driving object detection advancements, including accuracy and system efficiency enhancements.
Name of Research Journal
Journal of Robotics and Control (JRC)
Volume and Issue No.
Volume 6, Issue No. 3
Date/Year of Publication
May 2025
Citation
H. P. A. Tjahyaningtijas, et al. (2025). Object Detection for Wheeled Mobile Robot Based Using Deep Learning: Systematic Review, J Robot Control (JRC), 6(3),1425–1433
