Enhancing Gripper Mechanisms with Sensors for Precise Rice Planting in Robotic Competition Applications

Abstract

The growing demand for precision in agricultural robotics, particularly in competitive environments, has driven the development of advanced gripper mechanisms capable of efficient and accurate rice planting. This study presents the optimization of sensor-enhanced gripper mechanisms designed specifically for precision rice planting in robotic contest applications. The gripper integrates force sensors, proximity sensors, and high-resolution visual sensors, allowing for real-time feedback and adaptive control during the planting process. The system was tested both in laboratory simulations and real-world field conditions, with a focus on achieving high planting accuracy, reducing seedling damage, and enhancing overall operational efficiency. The implementation of Proportional-Integral-Derivative (PID) control and advanced image processing algorithms, such as Convolutional Neural Networks (CNN), allowed the gripper to perform with superior precision in dynamic environments typical of robotic contests. Experimental results demonstrated significant improvements in planting depth accuracy, seedling survival rates, and operational speed compared to traditional methods, highlighting the system’s potential for both competitive and practical agricultural applications. This research contributes to the ongoing development of robotic systems in agriculture and provides a robust framework for the deployment of precision planting technologies in competitive settings.