AI-Based Mobile Robot for Agricultural Application using Sliding Mode Controller
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Date
2023-05
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Addis Ababa University
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.