Design optimization and navigation for autonomous guided vehicle (AGV) in agriculture plantation

Tan, Kim Loong and Mohamed Kassim, Anuar and Yaacob, Mohd Rusdy and Azahar, Arman Hadi and Almashwali, Ayman Ahmed Hashem Salem and Awangku Jaya, Awangku Khairul Ridzwan and Ngadiron, Zuraidah and Yasuno, Takashi (2024) Design optimization and navigation for autonomous guided vehicle (AGV) in agriculture plantation. International Journal of Agriculture, Forestry and Plantation, 15. pp. 210-218. ISSN 2462-1757

Text 014582912202401130.pdf Download (525kB)

Abstract

The Industrial Revolution (IR) 4.0 is altering the way we communicate, work, and live. However, automation and artificial intelligence (AI) have increasingly the potential to be applied in agricultural plantations. Nowadays, most plantations need humans to collect soil data and require high labor and time consumption. To solve this problem, designing and implementing an automated guided vehicle (AGV) system in the agriculture plantation has high potential. The AGV is based on plantation monitoring, and sending the data readings to the cloud server without humans is desired. In this project, the LIDAR is used as the main sensor for navigational purposes for travel around the experimental area. For obstacle avoidance, the same LIDAR is applied with the obstacle detection algorithm to detect the human or object around the AGV to prevent any collision. The multi-sensing system, such as the rotary encoder, is the supported sensor to accurately measure the wheel rotation and position. All signal data from the sensor fusion will be processed by the Robot Operating System (ROS) to optimize robot navigation, such as robot movement and data analysis for obstacle avoidance. Some simulations and experiments have been successfully performed in a computer-based simulation and farm.

Item Type:	Article
Uncontrolled Keywords:	Autonomous guided vehicle, LIDAR, Sensor fusion, Robot operating system
Divisions:	Faculty Of Electrical Technology And Engineering
Depositing User:	Sabariah Ismail
Date Deposited:	23 May 2025 16:32
Last Modified:	23 May 2025 16:32
URI:	http://eprints.utem.edu.my/id/eprint/28701
Statistic Details:	View Download Statistic

Actions (login required)

View Item