Biological Systems Engineering, Department of
Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research
First Advisor
Santosh Pitla
Committee Members
Marilyn Wolf, Yeyin Shi
Date of this Version
6-2025
Document Type
Thesis
Citation
A thesis presented to the faculty of the Graduate College at the University of Nebraska in partial fulfilment of requirements for the degree of Master of Science
Major: Agriculture and Biological Systems Engineering
Under the supervision of Professor Santosh Pitla
Lincoln, Nebraska June 2025
Abstract
This study presents the design and development of a custom-built uncrewed aerial vehicle (UAV) tailored for precision agriculture. Unlike commercial UAVs, which are often constrained by proprietary systems and limited hardware customization, the proposed platform emphasizes modularity, upgradeability, and cost-effectiveness. The UAV is equipped with a Cube Blue flight controller for reliable low-level actuation and a Raspberry Pi 4 companion computer that executes a Model Predictive Control (MPC) algorithm for high-level trajectory optimization and stability enhancement.
Most conventional UAV autopilot systems rely on non-optimal control strategies, such as proportional–integral–derivative (PID) controllers, which can be inadequate for dynamic or resource-constrained environments. In contrast, this work implements an optimal control strategy using MPC, resulting in smoother trajectory tracking and optimized computational efficiency. Additionally, traditional mission planning approaches based on static waypoints lack the flexibility required for dynamic field conditions. To overcome this, the proposed architecture integrates MPC with Kalman filtering, enabling adaptive flight planning and allowing the UAV to continuously track and follow a moving uncrewed ground vehicle (UGV) in real time.
The system was first validated through simulations in the AirSim environment and later tested in real-world field trials. Experimental evaluations encompassed both static and dynamic waypoint tracking, as well as complex figure-eight trajectory navigation under windy conditions. The UAV demonstrated high precision and responsiveness across all test scenarios. During autonomous takeoff and landing operations, the root mean square error (RMSE) ranged between 8 cm and 20 cm. For complete mission cycles, including takeoff, navigation, and return-to-launch the RMSE remained within this same range. During figure-eight trajectory execution, RMSE values increased slightly, ranging from 20 cm to 35 cm. Notably, the UAV was able to track the UGV successfully, even along curved, row-crop style paths, demonstrating adaptability to non-linear trajectories. These results confirm the system’s effectiveness and reliability, highlighting its potential for deployment in precision agriculture and other dynamic, real-world robotic applications.
Advisor: Santosh Pitla
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Copyright 2025, Veera Venkata Ram Murali Krishna Rao Muvva. Used by permission