Autonomous Field Navigation of Mobile Robots for Data Collection and Monitoring in Agricultural Crop Fields

Abstract

This paper introduces a mobile ground robot designed for autonomous navigation and data collection in agricultural fields, utilizing precise localization through an extended Kalman filter (EKF) that integrates data from GPS, an inertial measurement unit (IMU), and wheel encoders. We propose a novel method based on an artificial electric potential field (AEPF) for reliable and autonomous navigation in these robots. Implemented on a four-wheeled robot interfaced, our experiments showed that AEPF-based navigation processed data more quickly than the traditional Nav2 local path planner. Additionally, the robot reliably collected RGB and depth images while navigating crop rows, highlighting the method's effectiveness and its potential for extensive applications in autonomous crop monitoring. Additionally, a graphical user interface was developed to enable users to define target areas, assign tasks, and monitor the robot's performance in real time.