AI-Based Mobile Robot for Agricultural Application using Sliding Mode Controller
| dc.contributor.advisor | Dereje Shiferaw (PhD) | |
| dc.contributor.author | Zewdu Jemema | |
| dc.date.accessioned | 2023-12-05T06:50:32Z | |
| dc.date.available | 2023-12-05T06:50:32Z | |
| dc.date.issued | 2023-05 | |
| dc.description.abstract | Recent advancements in agricultural robotic systems have greatly enhanced their functionality, usability, and integration into various tasks, particularly in the field of agriculture. The primary goal of designing agricultural robots is to enhance efficiency, save time, and decrease production costs by incorporating controllers, sensors, actuators, and communication systems. These robots have versatile applications and are widely embraced in the agricultural sector in industrialized countries. Extensive research has been dedicated to developing mobile robot platforms tailored for agricultural tasks, including plant health monitoring, pesticide spraying, fruit picking, and harvesting, with the aim of supporting farmers in developing nations like Ethiopia, where approximately 67% of the population is involved in agriculture. The thesis specifically targets fruit harvesting and focuses on the challenging task of modeling, designing, and simulating a mobile manipulator with advanced capabilities for agricultural businesses, making it one of the most difficult undertakings in this field. The study encompasses the presentation of the mobile manipulator’s 3D design, kinematics, and dynamics. In addition, AI techniques are employed to analyze fruit images, facilitating the accurate detection and determination of fruits. Based on the results, the effectiveness of the training technique has been assessed using an RMSE value of 0.19 and a loss value of 3.6e-02. An SMC utilizes the input generated from the image to govern the mobile manipulator’s position. The system’s stability and robustness have been assessed by considering uncertainties and variations in mass. When comparing the performance of a designed controller with a PID controller in the presence of uncertainty and parameter variation, it was found that SMC outperformed. According to the evaluation using the ITAE, SMC proves to be more effective, demonstrating a 75% improvement compared to the PID controller. Overall, this research contributes to the development of a robust and intelligent mobile manipulator for fruit harvesting in the agricultural sector, with potential applications to support farmers in countries like Ethiopia. | |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/232 | |
| dc.language.iso | en_US | |
| dc.publisher | Addis Ababa University | |
| dc.title | AI-Based Mobile Robot for Agricultural Application using Sliding Mode Controller | |
| dc.type | Thesis |