Robot móvil autónomo basado en control óptimo para el seguimiento de barreras vegetales laterales

Abstract

This research focuses on a comprehensive study on the development and implementation of an autonomous mobile robot designed for precision agriculture, specifically for navigating crop rows using optimal control techniques. The research addresses the global importance of agriculture, emphasizing its role in achieving Sustainable Development Goals (SDGs) such as zero hunger (SDG 2), industry innovation (SDG 9), and responsible consumption (SDG 12). This research outlines the design and implementation of a differential-drive mobile robot capable of navigating crop rows using optimal control methods, specifically Linear Quadratic Regulator (LQR)and Linear Quadratic Gaussian (LQG) controllers. The robot is equipped with sensors such as a LiDAR for distance measurement, an inertial measurement unit (IMU) for orientation, and encoders for motor control. The Robot Operating System (ROS) is used as the middleware to integrate sensors, actuators, and control algorithms [1, 2]. The study includes a detailed methodology for sensor and actuator characterization, ROS insta-llation and configuration, and the implementation of optimal control algorithms. Several experiments are conducted to validate the robot’s performance in different scenarios, including following a wall, navigating around obstacles, and tracking crop rows. The results demonstrate the effectiveness of the LQR and LQG controllers in maintaining the robot’s trajectory and correcting deviations caused by disturbances. This document concludes with a discussion of the results, comparing the performance of the LQR and LQG controllers in terms of root mean square error (RMSE) for distance and angle. The LQR controller achieved an RMSE of 0.1206 for distance and 0.1884 for angle in the final experiment with a simulated crop row, while the LQG controller with adjusted noise covariance (𝑅𝑛 = 1 ∗ 10−6) achieved an RMSE of 0.0862 for distance and 0.1324 for angle, showing superior performance in scenarios with varying light conditions and obstacles [3, 4]. The study underscores the potential of autonomous robots in precision agriculture, offering a scalable solution to improve productivity and reduce labor costs in the agricultural sector [5, 6].