UAV Takeoff, Hover and Landing Test in Real-World Flight Conditions Within a Laboratory Setting

Shipboard landing of Vertical Take-Off and Landing (VTOL) aircraft is an important consideration to operate aircraft in the ocean. The oceanic operation of aircraft has more difficult environment than the one on the ground due to salty, fogy, and windy condition. Especially the gust and the vortex around the structures on the ship make the approach of aircraft difficult. The movement of ship board in roll, pitch, and heave by wind and sea wave also makes the landing of aircraft severe. Tilt rotor UAV developed by Korea Aerospace Research Institute (KARI) basically aimed to be operated on the ground and to be expanded to an oceanic operation. The simulation environment of UAV to evaluate a various flight condition also considers to be applied to the oceanic operation. In this paper, the sea state conditions according to amplitude of sea wave and wind velocity was investigated and the mathematical description of sea state through sea wave modeling was conducted. In the condition of each sea state modeled with sea wave and wind, the shipboard landing of tilt rotor UAV was evaluated in simulation environment.

Artificial intelligence (AI) is used in tasks that usually require human intelligence. The motivation behind this study is the growing interest in deploying AI in public spaces, particularly in autonomous vehicles such as flying drones, to address challenges in navigation and control. The primary challenge lies in developing a robust, cost-effective system capable of autonomous navigation in real-world environments, handling obstacles, and adapting to dynamic conditions. To tackle this, we propose a novel approach integrating machine learning (ML) algorithms, specifically, reinforcement learning (RL), with a comprehensive simulation and testing framework. Reinforcement learning machine algorithms designed to solve problems requiring optimization of the solution for the highest possible reward were used. It was assumed that the algorithms do not have to be created from scratch, but they need a well-defined training environment that will appropriately reward or punish the actions taken. This study aims to develop and implement a novel approach to autonomous drone navigation using machine learning (ML) algorithms. The primary innovation lies in the comprehensive integration of ML algorithms with a real-world drone control system, encompassing both simulations and real-world testing. A vital component of this approach is creating a multi-stage training environment that accurately replicates actual flight conditions and progressively increases the complexity of scenarios, ensuring a robust evaluation of algorithm performance. This research also introduces a new approach to optimizing system cost and accessibility. It involves using commercially available, cost-effective drones and open-source or free simulation tools, significantly reducing entry barriers for potential users. A critical aspect of this study is to assess whether affordable components can provide sufficient accuracy and stability without compromising system quality. The authors developed a system capable of autonomously determining optimal flight paths and controlling the drone, allowing it to avoid obstacles and respond to dynamic conditions in real time. The performance of the trained algorithms was confirmed through simulations and real-world flights, which allowed for assessing their usefulness in practical drone navigation scenarios.

This study designs a 3-axis UAV gimbal rig for testing stability and performance before deployment in real-world flight conditions. The gimbal rig simulates the vertical, lateral, and longitudinal axes to ensure reliable operation in various scenarios. Made from lightweight aluminum alloy, the structure minimizes vibrations and maintains rigidity during testing. For precise motion tracking, each axis is equipped with an LPD3806- 600BM-G5 rotary encoder, offering accurate feedback on movement. The Arduino Nano processes the encoder data, displaying real-time results on a 16x2 LCD with an I2C interface for easy monitoring. Additionally, a push-button system enables users to switch between different readings for each axis. This setup aids researchers in analyzing UAV dynamics and refining both firmware and hardware. Future enhancements may include wireless data logging and integration of machine learning techniques to predict maintenance needs, further supporting UAV stability testing in various applications, including aerospace, defense, and commercial use.

The study focuses on the analysis and optimization of energy supply systems for unmanned aerial vehicles (UAVs), specifically FPV drones equipped with 10-inch propellers, to increase flight duration and operational efficiency under various civilian application scenarios. With the rapid expansion of drone usage in fields such as agriculture, environmental monitoring, logistics, and technical inspection, the issue of selecting the most efficient power source has become particularly relevant. The research aims to determine the optimal type of battery in terms of weight, energy density, endurance, and cost-effectiveness, ensuring reliable operation and sufficient energy reserve for different mission profiles. The article presents a comparative analysis between a lithium-polymer (LiPo) battery pack (6S, 10Ah) and three lithium-ion (Li-Ion) battery assemblies based on Molicel P42A cells with different configurations: 6S2P, 6S3P, and 6S4P. The analysis includes both theoretical calculations and practical considerations derived from known empirical data. Each configuration was evaluated based on three main criteria – flight time, maximum range, and overall economic viability. Additionally, the influence of payload mass (0–3 kg) on flight duration and efficiency was modeled for six key mission scenarios: observation and video surveillance, light and medium cargo delivery, heavy cargo delivery, one-way missions, and high-speed flight operations. Methodologically, the work takes into account motor efficiency (0.8–0.85), aerodynamic drag, and the voltage behavior of both LiPo and Li-Ion systems under load. The results demonstrate that Li-Ion batteries offer a higher specific energy density (up to 180 Wh/kg) and lower cost per watt-hour compared to LiPo alternatives, though their discharge characteristics limit maximum current output. Among the tested configurations, the Li-Ion 6S3P assembly showed the most balanced performance, providing a flight time of up to 45 minutes without payload and maintaining stable voltage throughout the discharge cycle. The LiPo 6S 10Ah pack remains advantageous for high-current, short-duration missions such as racing or rapid-response operations due to its superior instantaneous power output. The study concludes that no single battery configuration is universally optimal for all use cases. However, the Li-Ion 6S3P pack can be considered the most versatile and practical solution for multi-purpose civilian FPV platforms, combining reasonable cost, low weight, and extended endurance. The article also provides recommendations for selecting power systems depending on the mission profile and discusses the practical aspects of battery assembly, including the use of bi-metal copper and nickel-plated steel strips (0.15×10 mm) to ensure low resistance and reliable current transfer during high-load operation. The findings highlight the need for further experimental validation under real-world flight conditions, as well as future research focused on optimizing propeller geometry, assessing temperature impacts, and integrating hybrid power systems for small UAVs. The proposed analytical framework and results can serve as a methodological foundation for engineers, drone developers, and energy system designers seeking to enhance the performance and energy efficiency of modern FPV drones