Custom UAV with model predictive control for autonomous static and dynamic trajectory tracking in agricultural fields

This study introduces a custom-built uncrewed aerial vehicle (UAV) designed for precision agriculture, emphasizing modularity, adaptability, and affordability. Unlike commercial UAVs restricted by proprietary systems, this platform offers full customization and advanced autonomy capabilities.

The UAV integrates a Cube Blue flight controller for low-level control with a Raspberry Pi 4 companion computer that runs a Model Predictive Control (MPC) algorithm for high-level trajectory optimization. Instead of conventional PID controllers, this work adopts an optimal control strategy using MPC. The system also incorporates Kalman filtering to enable adaptive mission planning and real-time coordination with a moving uncrewed ground vehicle (UGV). Testing was performed in both simulation and outdoor field environments, covering static and dynamic waypoint tracking as well as complex trajectories.

The UAV performed figure-eight, curved, and wind-disturbed trajectories with root mean square error values consistently between 8 and 20 cm during autonomous operations, with slightly higher errors in more complex trajectories. The system successfully followed a moving UGV along nonlinear, curved paths.

These results demonstrate that the proposed UAV platform is capable of precise autonomous navigation and real-time coordination, confirming its suitability for real-world agricultural applications and offering a flexible alternative to commercial UAV systems.