The need for civilian use of Unmanned Aerial Vehicles (UAVs) has drastically increased in recent years. Their potential applications for civilian use include door-to-door package delivery, law enforcement, first aid, and emergency services in urban areas, which put the UAVs into obstacle collision risk. Therefore, UAVs are required to be equipped with sensors so as to acquire Artificial Intelligence (AI) to avoid potential risks during mission execution. The AI comes with intensive training of an on-board machine that is responsible to autonomously navigate the UAV. The training enables the UAV to develop humanoid perception of the environment it is to be navigating in. During the mission, this perception detects and localizes objects in the environment. It is based on this AI that this work proposes a real-time three-dimensional (3D) path planner that maneuvers the UAV towards destination through obstacle-free path. The proposed path planner has a heuristic sense of $A^{\star }$ algorithm, but requires no frontier nodes to be stored in a memory unlike $A^{\star }$. The planner relies on relative locations of detected objects (obstacles) and determines collision-free paths. This path planner is light-weight and hence a fast guidance method for real-time purposes. Its performance efficiency is proved through rigorous Software-In-The-Loop (SITL) simulations in constrained-environment and preliminary real flight tests.

In this study, we formulate and implement 14 widely used battery-equivalent circuit models (ECMs) in a MATLAB/Simulink environment and extract the parameters using the hybrid pulse power characterization (HPPC) test results based on the parameter estimator app working on the curve-fitting method. Using the extracted parameter values, which are either state of charge-dependent sixth-degree polynomials or constants, we perform an error fluctuation deviation analysis on two different real flight test results by loading 0 kg and 10 kg payloads on a hexacopter unmanned aerial vehicle for the ECM characteristic evaluation. According to the error characteristic analysis, the Liu ECM resulted in the lowest root mean square (RMS) (0.0376 V), standard deviation (0.0343 V), and maximum errors (0.6159 V) for the HPPC test, the Rint ECM resulted in the lowest RMS (0.0702 V) and maximum errors (0.6844 V) for the real flight test carrying a 0 kg payload, and the Shepherd ECM resulted in the lowest RMS (0.0875 V) and standard deviation errors (0.0872 V) for the real flight test carrying a 10 kg payload. In conclusion, we recommend the Shepherd ECM with constant parameters owing to its fast parameter optimization convergence time and flexibility to the dynamic discharging load with a smaller error.

Small-scale unmanned aerial vehicles are being deployed in urban areas for missions such as ground target tracking, crime scene monitoring, and traffic management. Aerial vehicles deployed in such cluttered environments are required to have robust autonomous navigation with both target tracking and obstacle avoidance capabilities. To this end, this work presents a simple-to-design but effective steerable sensor platform and its implementation techniques for both obstacle avoidance and target tracking. The proposed platform is a 2-axis gimbal system capable of roll and pitch/yaw. The mathematical model that governs the dynamics of this platform is developed. The performance of the platform is validated through a software-in-the-loop simulation. The simulation results show that the platform can be effectively steered to all regions of interest except backward. With its design layout and mount location, the platform can engage sensors for obstacle avoidance and target tracking as per requirements. Moreover, steering the platform in any direction does not induce aerodynamic instability on the unmanned aerial vehicle in mission.

An optimization study of an electric vertical takeoff and landing personal air vehicle (eVTOL PAV) was performed during the conceptual design stage using the design of experiments method. In defining the initial problem, a design target parameter was set. The PAV subsystem was based on a configuration tradeoff study matrix, which was used to effectively conduct configuration selection. Initial sizing was performed using the PAV sizing program developed by this research team using Microsoft Excel and Visual Basic for Application (VBA). A screening test was performed to find parameters with high sensitivity among independent design parameters. The response surface method was used to model design target parameters, and a regression equation was estimated using the experimental design method. A Monte Carlo simulation was performed to confirm the feasibility of the generated model. To optimize the design independent parameter, a satisfaction function was selected, and the appropriateness of the data was determined using a Pareto plot and p-value.

The Electrical Power System (EPS) is a vital subsystem in CubeSats, responsible for powering all onboard components. Due to the standardized size and weight constraints, the limited surface area of solar panels restricts power generation. To address this, Maximum Power Point Tracking (MPPT) is crucial. However, traditional MPPT techniques struggle in CubeSats’ dynamic orbital environments, where solar irradiance varies across different facets. This paper presents a novel MPPT method combining a Deep Feed Forward Neural Network DFFNN with a Proportional-Integral (PI) controller to adapt to these rapidly changing conditions. A year-long simulation of a 3U CubeSat in Systems Tool Kit (STK) generated real-time orbital temperature and irradiance data to train the DFFNN using 70,000 data points. The trained DFFNN was integrated into a Simulink model for testing under variable illumination. Results showed the DFFNN-based MPPT achieved 97.4% efficiency, outperforming Perturb and Observe (P&O) (88.9%), Incremental conductance (InC) (89.7%), and Particle Swarm Optimization (PSO) (94.08%). It also achieved 88.3% overall system efficiency, reduced power ripple to under 2.5%, and improved transient performance. These findings confirm the DFFNN-PI approach’s effectiveness in optimizing power extraction for CubeSat missions under dynamic conditions.

The anisotropic nature of fiber reinforced composite materials causes great challenges in predicting the inter-ply shear stress during forming. The complexity of understanding the functional dependency of inter-ply shear stress on multiple forming parameters such as blank temperature, pressure load, inter-ply slippage, and the relative fiber orientation angle of adjacent plies further limits the effort to produce a defect-free composite structure. Performing real experiments for various combinations of the mentioned parameters is both time consuming and economically costly. To overcome these difficulties, a surrogate-based analysis of inter-ply shear stress is proposed in this study. Based on the ranges of the forming parameters, computer experiments were performed. Using these experimental data, a radial basis function (RBF) based surrogate model that mimics inter-ply shear stress during composite press forming was developed. The fidelity of this model was checked with test data and found to be over 98% efficient.