Robust Image-based Visual Servoing for Autonomous Row Crop Following with Wheeled Mobile Robots

In this work, we present a new robust vision-based controller for wheeled mobile robots, equipped with a fixed monocular camera, to perform autonomous navigation in agricultural fields accurately. Here, we consider the existence of uncertainties in the parameters of the robot-camera system and external disturbances caused by high driving velocities, sparse plants, and terrain unevenness. Then, we design a robust image-based visual servoing (rIBVS) approach based on the sliding mode control (SMC) method for robot motion stabilization, even under the presence of such inaccuracies and perturbations. The vision-based controller, based on column and row primitives, is slightly modified to include a robustness term into the original feedback control laws to ensure successful row crop reaching and following tasks. We employ the Lyapunov stability theory to verify the stability and robustness properties of the overall closed-loop system. 3D computer simulations are carried out in the ROS-Gazebo platform, an open-source robotics simulator, using a differential-drive mobile robot (DDMR) in an ad-hoc developed row crop environment to illustrate the effectiveness and feasibility of the proposed control methodology.