Visual Servoing-based Navigation for Monitoring Row-Crop Fields
This addresses the problem of cost-effective and efficient navigation for precision agriculture robots, though it is incremental as it builds on existing visual servoing methods tailored to row-crop structures.
The paper tackles autonomous navigation for field robots in row-crop agriculture by proposing a visual servoing framework that uses only on-board cameras, eliminating the need for RTK-GPS or explicit localization, and demonstrates accurate row-following and seamless row-switching in simulations and on an actual robot at frame-rate.
Autonomous navigation is a pre-requisite for field robots to carry out precision agriculture tasks. Typically, a robot has to navigate through a whole crop field several times during a season for monitoring the plants, for applying agrochemicals, or for performing targeted intervention actions. In this paper, we propose a framework tailored for navigation in row-crop fields by exploiting the regular crop-row structure present in the fields. Our approach uses only the images from on-board cameras without the need for performing explicit localization or maintaining a map of the field and thus can operate without expensive RTK-GPS solutions often used in agriculture automation systems. Our navigation approach allows the robot to follow the crop-rows accurately and handles the switch to the next row seamlessly within the same framework. We implemented our approach using C++ and ROS and thoroughly tested it in several simulated environments with different shapes and sizes of field. We also demonstrated the system running at frame-rate on an actual robot operating on a test row-crop field. The code and data have been published.