Simulation of Autonomous Tractor using ROS

Farmers encounter a multitude of obstacles in agriculture, such as operating in challenging weather conditions and unpredictable surroundings, which can have a significant impact on sustainability and productivity. To tackle these hurdles, autonomous agricultural vehicles are indispensable. This paper introduces the digital modelling and testing of a self-driving tractor system tailored for agricultural tasks. By utilizing the Robot Operating System (ROS) and Gazebo simulator, the study delves into the incorporation of cutting-edge navigation algorithms, including the GMapping algorithm for Simultaneous Localization and Mapping (SLAM), Dijkstra algorithm, and the Dynamic Window Approach (DWA) for efficient path planning. The tractor prototype, developed using Fusion 360, undergoes thorough assessments to evaluate its mapping accuracy, localization precision, path planning effectiveness, and obstacle avoidance capabilities. The outcomes showcase successful mapping and localization, precise trajectory planning, adept obstacle avoidance, and visualization through RViz and Gazebo co-simulation. The study aims to propel the realm of autonomous agriculture by offering insights into the design, enhancement, and virtual implementation of autonomous tractor systems, ultimately enhancing the efficiency and sustainability of agricultural practices. Keywords: Autonomous Vehicles, Autonomous Tractors, Path Planning, GMapping, AMCL, ROS, Gazebo, RViz, Dijkstra’s Algorithm, DWA Path Planning, PID Controller, Simulation