Design optimization and navigation for autonomous guided vehicle (AGV) in agriculture plantation
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 a...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | en |
| Published: |
ZES Rokman Resources
2024
|
| Online Access: | http://eprints.utem.edu.my/id/eprint/28701/2/014582912202401130.pdf http://eprints.utem.edu.my/id/eprint/28701/ https://ijafp.org/wp-content/uploads/2024/12/IJAFP15_067.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|