Unmanned aerial vehicles are being deployed in complex, safety-critical, missions that require a high autonomy level, especially in planning and control in hazardous or inaccessible environments. This review provides a comprehensive analysis of two fundamental pillars of dual-layer autonomy, that is, path planning and control techniques applied in autonomous vehicles. First, this review systematically classifies and evaluates planning techniques into global and local methods, including graph-based, sampling-based, potential field, metaheuristic, and artificial intelligence approaches. The second part analyzes control methods, covering classical linear, modern nonlinear, intelligent, and hybrid control strategies. For each technique, we discuss the basic concept, advantages, disadvantages, and challenges. Quantitative comparisons reveal trade-offs between computation time, path optimality, and environmental adaptability, and robustness to disturbances. This analysis demonstrates how hybrid methods mitigate individual limitations to reveal trade-offs between these factors. We introduce unifying frameworks, including a dual-layer autonomy model and a novel taxonomy of hybridization patterns to contextualize methods. Moreover, the paper discusses advancements, challenges, and future trends in aircraft autonomy. Finally, we synthesize a research roadmap focused on the convergence of model-based and data-driven approaches, such as learning-augmented optimal controller and certifiable neuro-symbolic autonomy, addressing persistent challenges in uncertainty, safety, autonomous, and real-world applications.
Additive manufacturing, particularly Material Extrusion (MEX), has become increasingly important for producing polymer components in applications where mechanical performance, surface quality, and cost efficiency are simultaneously critical. Polylactic acid (PLA) material is widely used in functional, biomedical, and consumer products; however, inconsistent and sometimes contradictory findings in the literature regarding the effects of MEX process parameters on strength, surface roughness, and cost-related metrics limit reliable process optimization. This study addresses the question of how key MEX parameters can be systematically optimized to balance mechanical performance and economic efficiency while accounting for complex parameter interactions and part anisotropy. A structured design-of-experiments framework was employed, beginning with a fractional factorial screening design to identify the most influential parameters, followed by a central composite design (CCD) integrated with response surface methodology (RSM). Ultimate tensile strength (UTS) and surface roughness were selected as performance indicators, while material consumption and printing time were considered as cost-related objectives. Build orientation and raster angle were parameterized using a continuous-variable approach, moving beyond the conventional use of discrete orientations. Statistical analysis was performed using analysis of variance (ANOVA), and multi-objective optimization (MOO) was applied to determine optimal parameter sets for different application scenarios. The results demonstrate that build orientation, layer thickness, infill percentage, and raster orientation significantly influence both UTS and surface roughness, with pronounced nonlinear effects and interactions, while their relative contributions differ in magnitude and importance. The proposed continuous orientation framework reveals critical transition regions in mechanical behavior that are not captured by traditional discrete orientation studies. The multi-objective optimization model effectively identifies trends of printing parameter combinations that balance conflicting practical printing requirements. This work provides a comprehensive and systematic optimization framework for MEX of PLA, offering deeper insights into orientation-dependent anisotropy and parameter interactions. The findings contribute to more reliable decision-making for cost-effective and sustainable production of high-performance polymer components using MEX.
Sea salt increasingly harbors organic contaminants from personal care products, yet current monitoring methods lack spatial resolution and require destructive sampling. This study introduces an innovative analytical framework integrating Laser-Induced Fluorescence (LIF) Hyperspectral Imaging (HSI) with machine learning for the rapid, non-destructive detection of sunscreen residues on salt crystals. To simulate contamination, seawater from the Mediterranean coast (Alexandria, Egypt) was spiked to achieve a 10 mg/L sunscreen concentration within the seawater matrix prior to crystallization; this formulation contained Ethylhexyl Methoxycinnamate, Homosalate, and Ethylhexyl Salicylate. A SOC710 HS camera (128 bands) acquired fluorescence data under 450 nm laser excitation. Raw data underwent preprocessing and dimensionality reduction via Sparse Principal Component Analysis (Sparse PCA, λ = 0.5, k = 4 components, 73.4
Electro-hydraulic servo systems utilizing multi-stage hydraulic cylinders (MSHCs) in erection mechanisms face strong nonlinearities, time-varying loads, and model uncertainties that challenge conventional controllers. This paper develops and compares different control strategies for such systems: the classical proportional–integral–derivative controller (PID), a PID controller whose gains are tuned via particle swarm optimization (PSO), fractional-order PID tuned by PSO (FOPID–PSO), and a robust sliding mode control (SMC) scheme with Lyapunov-based design and a boundary layer implementation to mitigate chattering. A nonlinear model of the erection mechanism and MSHC, including inter-stage impacts captured by a Kelvin–-Voigt impact model and controlled by a proportional directional control valve, is formulated and simulated using MATLAB/Simulink. Results show that PSO-optimized PID significantly improves tracking and settling behavior over baseline PID, FOPID–PSO adds flexibility to the PID, while robust SMC maintains accuracy and disturbance rejection under large load variations, but with a pronounced peak control signal during the stage transition interval. The study quantifies performance, demonstrating that the control approaches enhance stability and robustness, with SMC offering strong resilience to uncertainties and PSO providing a simple and high-performance tuning route for implementation. The results are based on numerical simulation and serve as a baseline for future experimental validation and hardware realization.
Ship airwakes, or turbulent airflows produced by the ship’s superstructure, introduce significant challenges to helicopter operations on naval ships. The landing deck may experience unstable aerodynamic conditions because of these airwakes, reducing operational safety. In this study, passive flow control methods were developed to reduce the impact of airwakes on the landing deck. Their effectiveness was evaluated using numerical simulations. Turbulence energy, recirculation zones, and reattachment lengths were the main subjects of the study. The aerodynamic performance of two distinct modifications at the hangar’s rear edges was evaluated using a 1/50 scale model of the NATO-GD. It was found that these modifications improved flow stability, with reattachment length and recirculation zones reduced by up to 79
