Extended Kalman Filter-Based Locating System for Autonomous Sprayer Robot in Precision Agriculture Environment

Automation in agriculture has become increasingly widespread in the context of the so-called Smart Farms. The integration of these technologies has brought significant advancements in recent years, allowing for more efficient and accurate farming operations. Closed environments with limited lighting, such as greenhouses, represent a suitable scenario for automation, as they offer controlled conditions that favor the implementation of autonomous systems. In this context, this paper proposes an autonomous navigation system designed for greenhouse environments, with the aim of guiding a mobile robot during spraying tasks. The proposed method integrates onboard odometry and IMU data to estimate the robot's position, as well as distance measurements from a UWB sensor to identify the checkpoints installed in the greenhouse. The architecture improves location accuracy and navigation stability while remaining less complex and more cost-effective than state-of-the-art approaches with 5.17 cm positioning errors.