Unmanned Aerial Vehicles (UAVs) are versatile, adapting hardware and software for research. They are vital for remote monitoring, especially in challenging settings such as volcano observation with limited access. In response, economical computer vision systems provide a remedy by processing data, boosting UAV autonomy, and assisting in maneuvering. Through the application of these technologies, researchers can effectively monitor remote areas, thus improving surveillance capabilities. Moreover, flight controllers employ onboard tools to gather data, further enhancing UAV navigation during surveillance tasks. For energy efficiency and comprehensive coverage, this paper introduces a budget-friendly prototype aiding UAV navigation, minimizing effects on endurance. The prototype prioritizes improved maneuvering via the integrated landing and obstacle avoidance system (LOAS). Employing open-source software and MAVLink communication, these systems underwent testing on a Pixhawk-equipped quadcopter. Programmed on a Raspberry Pi onboard computer, the prototype includes a distance sensor and basic camera to meet low computational and weight demands.Tests occurred in controlled environments, with systems performing well in 90% of cases. The Pixhawk and Raspberry Pi documented quad actions during evasive and landing maneuvers. Results prove the prototype’s efficacy in refining UAV navigation. Integrating this cost-effective, energy-efficient model holds promise for long-term mission enhancement—cutting costs, expanding terrain coverage, and boosting surveillance capabilities.

This paper introduces a structured methodology for bridging the gap between theoretical modeling and high-fidelity simulation of multirotor Unmanned Aerial Systems (UAS) through the construction of digital twins in PX4 v1.12 Software-in-the-Loop (SITL) environments. A key challenge addressed is the absence of standardized procedures for translating physical UAV characteristics into simulation-ready parameters, which often results in inconsistencies between virtual and real-world behavior. To overcome this, we propose a hybrid parametric identification pipeline that combines analytical modeling with experimental characterization. Critical parameters—such as inertial properties, thrust and torque coefficients, drag factors, and motor response profiles—are obtained through a combination of physical measurements and theoretical derivation. The proposed methodology is demonstrated on a custom-built heavy-lift quadrotor, and the resulting digital twin is validated by executing autonomous missions and comparing simulated outputs against flight logs from real-world tests.