CottonSim: Development of an autonomous visual-guided robotic cotton-picking system in the Gazebo
Journal:
arXiv
Published Date:
May 8, 2025
Abstract
In this study, an autonomous visual-guided robotic cotton-picking system,
built on a Clearpath's Husky robot platform and the Cotton-Eye perception
system, was developed in the Gazebo robotic simulator. Furthermore, a virtual
cotton farm was designed and developed as a Robot Operating System (ROS 1)
package to deploy the robotic cotton picker in the Gazebo environment for
simulating autonomous field navigation. The navigation was assisted by the map
coordinates and an RGB-depth camera, while the ROS navigation algorithm
utilized a trained YOLOv8n-seg model for instance segmentation. The model
achieved a desired mean Average Precision (mAP) of 85.2%, a recall of 88.9%,
and a precision of 93.0% for scene segmentation. The developed ROS navigation
packages enabled our robotic cotton-picking system to autonomously navigate
through the cotton field using map-based and GPS-based approaches, visually
aided by a deep learning-based perception system. The GPS-based navigation
approach achieved a 100% completion rate (CR) with a threshold of 5 x 10^-6
degrees, while the map-based navigation approach attained a 96.7% CR with a
threshold of 0.25 m. This study establishes a fundamental baseline of
simulation for future agricultural robotics and autonomous vehicles in cotton
farming and beyond. CottonSim code and data are released to the research
community via GitHub: https://github.com/imtheva/CottonSim
