Adaptive Sliding Mode Control with Artificial Potential Field for Ground Robots in Precision Agriculture

摘要

This work considers autonomous Guidance, Navigation and Control (GNC) of Unmanned Ground Vehicles (UGVs) for Precision Agriculture (PA) applications. In the agricultural environment, the GPS signal can be weak for various reasons, which makes GPS-based navigation unreliable. Therefore, we consider a GPS-denied situation and localise the robot with several sensors and an Extended Kalman Filter (EKF). The path planner exploits a suitably designed Artificial Potential Field (APF), which ensures attraction to the target while avoiding obstacles. The motion controller, on the other hand, is based on Sliding Mode Control (SMC) and proposes a new direct adaptive law to adjust a control parameter in real time and achieve better tracking of the reference path.The navigation strategy was validated in Matlab/Simulink through numerical simulations, employing simulation scenarios which reflect typical applications in precision agriculture.