Search Results (1,003)

This paper provides a systematic synthesis of recent developments in physics-informed neural networks (PINNs) applied to aerospace engineering, with an emphasis on their role in physically consistent surrogate modeling, forward simulation, and inverse parameter estimation. Using a PRISMA-based methodology, the study surveys peer-reviewed works published between 2017 and 2025 across aviation- and space-related domains, including aerodynamics, structural mechanics, aeroelasticity, propulsion, control, structural health monitoring, satellite-orbit prediction, space-debris collision avoidance, and spacecraft radiation-impact modeling. The analysis shows that embedding governing equations, boundary conditions, and observational data into composite loss functions enables PINNs to improve predictive consistency, reduce dependence on dense simulation or experimental datasets, and support parameter identification under sparse or noisy measurements. Attention is given to architectural variants such as XPINNs, cPINNs, gPINNs, operator-learning approaches, and hybrid PINN-CFD/FEM formulations, as well as to training strategies based on adaptive sampling, domain decomposition, transfer learning, and dynamic loss weighting. Reported benefits include reduced approximation error, improved convergence in selected high-gradient or multiphysics problems, and enhanced interpretability compared with purely data-driven models. At the same time, the review identifies persistent open challenges, including scalability to large aerospace domains, sensitivity to loss-weighting and collocation strategies, limited robustness under noise and uncertainty, high computational cost, and the lack of standardized aerospace benchmarks. Overall, the review highlights PINNs as a promising but still developing framework for fast, interpretable, and physically consistent modeling of aircraft and spacecraft systems. Full article

Coordinating robot swarms requires jointly solving the interdependent Multi-Robot Task Allocation (MRTA) and Multi-Agent Path Finding (MAPF) problems under strict time and communication constraints, yet most existing methods rely on centralized planning or expose agents’ exact positions. In this study, a fully decentralized algorithm is proposed in which each agent estimates the positions and intended plans of others from broadcast bid values rather than shared coordinates, anticipating conflicts at intersections before moving and dynamically altering its movement or task assignment when it predicts it cannot reach its task in time. The method combines the Priority Inheritance with Backtracking (PIBT) algorithm for collision-free navigation with a novel Interval Type-3 Fuzzy Logic (IT3FL) mechanism for conflict resolution and congestion-aware rerouting. The approach was evaluated across seven benchmark environments against the centralized methods Enhanced Conflict-Based Search (ECBS) and ECBS with Task Allocation (ECBS-TA) and the Consensus-Based Auction Algorithm (CBAA). It reduced path cost by up to 7.10% relative to ECBS in open environments, while centralized methods remained superior in complex corridor-based maps. In the most demanding constrained scenario, it reduced solution cost by up to 47.03% and improved task completion by 35% over CBAA, demonstrating a robust, scalable decentralized alternative. Full article

To address the critical safety challenges of flooding induced by ship–ice collisions in Arctic shipping routes, this study proposes an Observe–Orient–Predict–Decide–Act (OODA-P)-enhanced closed-loop intelligent damage control decision-support framework integrated with predictive simulation. To address the limitations of existing systems—namely, weak polar adaptability and the absence of a decision feedback loop—this research presents three core findings: (1) A fast time-domain floating condition model was developed by coupling topside icing with progressive flooding. Numerical simulations indicate that neglecting ice accretion leads to an underestimation of the long-term heel angle and transverse stability by 4.4% and 4.5%, respectively, validating the necessity of incorporating coupled ice loads. (2) A serial dual-channel prediction and evaluation mechanism, integrating “situation evolution prediction” and “decision efficacy evaluation,” was designed. This mechanism can proactively forecast long-term deterioration trends in the floating condition within 0.3147 s of acquiring damage information, capable of identifying and flagging potentially high-risk emergency plans before their execution, thus preventing adverse outcomes. (3) The proposed framework was validated through typical polar scenarios and 111 damage control training sessions across three batches, with the full-loop logic flow completing in under 3 s. Compared with the traditional OODA loop, the average emergency response time was reduced from 26.9 to 22.7 min (a 15.5% reduction), while the initial response success rate improved from 74.7% to 97.3% in a simulated training environment. By enabling “virtual trial-and-error” prior to execution, this framework demonstrates the potential to augment traditional experience-based damage control with proactive, simulation-driven decision support, marking a step towards more intelligent interventions. Through the explicit coupling of topside icing and progressive flooding into real-time predictions, this work provides a foundation for further development of polar-adaptable intelligent damage control systems. Full article

Achieving safe and efficient navigation in cluttered and dynamic environments remains an open challenge for mobile robots, especially when perception and actuation are uncertain. Standard navigation stacks typically handle obstacle avoidance through fixed safety margins or costmap inflation layers. While effective in simple settings, these approaches are difficult to tune in practice: conservative inflation can prevent traversal through narrow passages, whereas less conservative settings may lead to unsafe behavior. Moreover, they usually encode risk only as a function of obstacle proximity. We propose a unified probability-inspired risk-cost map that integrates perception uncertainty, actuation uncertainty, dynamic obstacle prediction, and occlusion-aware memory into a single spatial representation. The resulting risk map is used by a local path-modification module that adapts a reference global path using the proposed risk map and interfaces with a standard Model Predictive Path Integral (MPPI) controller. The proposed method is compatible with standard navigation pipelines. We validate the resulting framework in Gazebo simulations under different sensing and actuation uncertainty conditions and in environments containing unknown static and dynamic obstacles. The results show that the proposed method is more robust than conventional costmap-based baselines, resulting in fewer aborted goals in cluttered environments and substantially fewer collision events when dynamic obstacles are present. Full article

In the LoRa (long-range) wide area network (LoRaWAN), Class A devices employ a pure ALOHA random access mechanism. Under large-scale access and bursty traffic conditions, severe packet collisions are likely, which reduces throughput and increases the packet loss rate. To address these issues, herein, we propose a collision mitigation scheme integrating the extended Kalman filter (EKF) with nonorthogonal multiple access (NOMA). First, a nonlinear state-space model is constructed to capture the dynamic evolution of backlog nodes and the uncertainty of traffic arrivals. The backlog node number is modeled as the hidden state, while newly arrived and successfully decoded packets are incorporated into the state-transition equation. At the gateway, decoded packet counts and channel occupancy are treated as observations based on which a nonlinear mapping between system state and observable features is established. The EKF is then applied to recursively predict and correct, enabling real-time estimation of the backlog state. Accordingly, an adaptive backoff strategy is designed to adjust transmission probability based on the estimated optimal load. Furthermore, to mitigate packet loss caused by collisions, a power-domain NOMA scheme with successive interference cancelation (SIC) is introduced. Signals transmitted with different spreading factors (SFs) are decoupled into approximately independent processing branches by exploiting inter-SF quasi-orthogonality. To account for imperfect inter-SF orthogonality, cross-SF residual coupling coefficients are introduced to characterize leakage interference. For transmissions sharing the same SF, overlapping packets are successively decoded and recovered through a NOMA-SIC mechanism jointly constrained by the SINR-based decoding threshold, the power-domain separation requirement, the maximum number of resolvable SIC layers, and residual SIC interference. Accordingly, the proposed receiver architecture enhances the decoding and recovery capability for collided LoRa packets. Simulation results demonstrate that, under medium-to-high traffic loads, the proposed scheme significantly improves throughput and access success rate while effectively reducing collision probability and packet loss, thereby enhancing the overall robustness and efficiency of the LoRa network. Full article

The discovery of the Higgs boson in 2012 at the Large Hadron Collider marked a major milestone in our understanding of electroweak symmetry breaking. Since then, increasingly precise measurements by the ATLAS and CMS Collaborations, based primarily on proton–proton collision data at \(\sqrt{s}\) = 13 TeV corresponding to about 140 fb−1 per experiment, have confirmed its compatibility with Standard Model predictions within current uncertainties. The Higgs boson mass is now measured with a precision of about 0.08%, while its couplings to fermions and bosons are determined at the 7–20% level. The completion of the LHC programme and the High-Luminosity LHC, will probe Higgs boson couplings at the fewpercent level. However, sub-percent precision is required for stringent tests of the Standard Model, as any deviation would signal new physics beyond it. This strongly motivates future collider facilities, designed both as high-precision Higgs factories and, in many cases, as energy-frontier machines. Within the framework of the update of the European Strategy for Particle Physics, we discuss the physics case and main characteristics of the proposed particle collider options, highlighting their complementarity, technological challenges, and expected performance. The 2026 Strategy Update identifies the FCC-ee collider as the preferred next flagship project at CERN. Operating at the Z pole and at centre-of-mass energies between 240 and 365 GeV, it would enable model-independent, per-mille-level precision on Higgs boson couplings, while providing a pathway to a future high-energy hadron collider. The Higgs sector thus constitutes a central laboratory for precision tests of the Standard Model and for exploring the fundamental structure of our universe. Full article

Contrary to the common assumption of Maxwell–Boltzmann (MB) distributions, space plasmas are characterized by kappa distributions and reside in thermodynamic stationary states out of classical thermal equilibrium, owing to the correlations between the charged plasma particles. In this study, we extend prior work to include realistic 3D collisions and Debye-like shielding of the correlations to show how these two processes compete in the development of realistic plasma particle velocity distributions. We modify our prior numerical experiment to incorporate both 3D collisions and correlations that include realistic Debye-like shielding of plasma particles and run it over many collisions until it becomes stationary. While 3D collisions alone produce Maxwell–Boltzmann (MB) distributions of the particles (κ → ∞), introducing correlations drives the distributions to stationary states with finite thermodynamic kappa (κ), where stronger correlations produce lower values of κ, as observed in space plasmas. Further, development of correlation clusters around each collision rapidly produces thermodynamic systems where the Debye length is proportional to $\sqrt{1+1/{{\kappa }_0}_{\mathrm{th}}}$, for invariant thermal kappa ${{\kappa }_0}_{\mathrm{th}}$, just as predicted by theory. This simple numerical experiment explores much more realistic particle interactions to show how 3D collisions and properly shielded correlations compete to produce stationary states of plasma particle kappa distributions and illuminates how long-range interactions correlate particles over the scale of the Debye lengths. Full article

Road traffic accidents represent a critical road safety issue, the severity of which depends on the complex interplay of multiple factors. This issue directly impacts Target 3.6 of Sustainable Development Goal (SDG) 3, which aims to halve global deaths and injuries by 2030, and SDG 11, which focuses on safe and sustainable transport systems. The study of these factors and their interrelationships is important in the scientific literature. The objective of this study is to analyze the factors that determine the severity of road traffic accidents, identifying the most important ones and their correlations. A dataset containing variables such as infrastructure, location, time, and vehicle type, among others, was used to predict severity, applying Association Rules to identify latent correlations and the Classification and Regression Tree for hierarchical risk classification. The results reveal that the type of collision is the primary predictor of severity; the highest severity is associated with heavy traffic and head-on or side-impact collisions, involving critical scenarios, in the early morning hours and in rural areas, linked to trucks. The combined use of both tools provides a scientific basis for designing interventions on highly vulnerable road segments, contributing to the fulfillment of the 2030 Agenda for safe mobility. Full article

In the context of aircraft engine technologies, sprays are used to inject water into the engine cycle to enhance efficiency and reduce emissions. Accurate specification of droplet injection boundary conditions is therefore essential for reliable numerical predictions. This study presents a numerical validation of a water spray configuration previously characterized using phase Doppler anemometry. An Euler/Lagrange approach is applied to simulate the spray using two distinct injection strategies: an array of injector points (Case 1) and a solid-cone injector (Case 2). Numerical results are compared with experimental data to assess droplet size and velocity distributions. Both approaches capture the main spray characteristics, while Case 1 provides improved agreement due to a more accurate representation of the injection conditions. In addition, the influence of droplet–droplet collisions is investigated using different collision-regime maps. While the collision models lead to significantly different collision outcomes, only minor differences are observed in spray characteristics, with noticeable deviations occurring in the downstream region. Overall, the results demonstrate the importance of accurate injection modeling for reliable spray predictions, while simpler injection approaches remain viable with reduced accuracy. The influence of collision modeling is limited under the present conditions and for the investigated spray metrics, providing insight into its role and limitations in polydisperse sprays. Full article

The ski-jump spillway is an energy-dissipating structure that discharges extra water beyond the dam’s capacity. The scour process occurs below spillways due to the collision of the water jet with high energy. It is critical to acquire information on scour holes to improve the dam’s safety and related components. Machine learning (ML) techniques have successfully demonstrated their effectiveness for modeling scour in hydraulic engineering. The present research considers novel approaches of ML models for estimating the scour hole geometries below ski-jump bucket spillways. This study investigates the capability of two novel feature-engineering approaches, namely Stronger Variable Creator Machine (SVCM) and High Correlated Variables Creator Machine (HCVCM), along with Gene Expression Programming (GEP) and their hybrid forms (SVCM+GEP and HCVCM+GEP), which were employed to predict normalized scour depth, scour length, and scour width below ski-jump spillways. Statistical metrics, graphical analyses, the Rank Mean (RM) method, the cross-validation approach, and $U_{95}$ index were used for the evaluation and reliability assessment of the proposed ML models. The results showed that hybrid ML models consistently outperformed individual algorithms. The results indicated that the SVCM+GEP method with $\mathrm{R}\mathrm{M}=1.83$ and $1.50$ had the highest performance compared to other methods for the prediction of ${\displaystyle \frac{Ds}{Dw}}$ and ${\displaystyle \frac{L_s}{D_w}}$, respectively. In addition, the HCVCM+GEP method with $\mathrm{R}\mathrm{M}=1.33$ was the best model for the prediction of ${\displaystyle \frac{Ws}{Dw}}$. In comparison with the conventional regression-based equations and previously reported ML methods, the proposed hybrid approaches improved the prediction results. In addition, the cross-validation method confirmed the robustness and generalization capability of the suggested hybrid ML models. The superior performance of the hybrid models is attributed to their ability to capture complex nonlinear interactions among hydraulic and geometric variables. The developed SVCM/HCVCM+GEP models provide accurate approaches for predicting scour parameters in hydraulic structures. Full article

This study presents an integrated framework for enhancing the safety and operational efficiency of robotic arms in laparoscopic surgery by addressing minimum distance estimation between multi-arm manipulators and the associated collision-aware warning. By combining analytical modeling, real time simulation, and machine learning, the framework offers a robust solution for ensuring safe robotic operations. An analytical model was developed to estimate the minimum distances between robotic arms based on their joint configurations, offering theoretical calculations that serve as both a validation tool and a benchmark. To complement this, a 3D simulation environment was created to model two 7 DOF Kinova robotic arms (Kinova Inc., Boisbriand, QC, Canada), generating a diverse dataset of configurations for distance estimation and collision warning. Using these insights, a deep residual neural network model was trained with joint configurations as inputs. On the held out validation set, the model achieves $R^2=0.940$, RMSE $=42.0$ mm, MAE $=28.7$ mm, and a near zero mean bias, demonstrating strong predictive accuracy and consistent generalization across the workspace. The framework is intended as an early collision warning layer, where a warning is triggered when the predicted inter-arm distance falls below a 0.2 m threshold, which corresponds to a surface to surface clearance of approximately 50 mm given the Kinova Gen3 (Kinova Inc., Boisbriand, QC, Canada) cross sectional radius. This work demonstrates the effectiveness of combining analytical modeling with machine learning to enhance the precision and reliability of multi-arm robotic systems. Full article

To address the problems of state estimation bias, dynamic threat response lag, and insufficient safety margin in formation coordination caused by the mismatch between the three-dimensional continuous motion model and the discrete sampling characteristics of sensors in UAV multi-aircraft collaborative inspection missions under complex dynamic environments, this paper studies a trajectory planning method that integrates model predictive control and multi-constraint optimization. By constructing a three-dimensional continuous motion model of the UAV and discretizing it using the Euler integral method, the mapping deviation between the continuous motion characteristics and the discrete working mechanism of the airborne system is solved. Based on the model predictive control method, a patrol trajectory tracking planning model is designed, and state increment and integral augmentation strategies are introduced to transform global reference trajectory tracking into a constrained quadratic programming problem in the rolling time domain, achieving high-precision closed-loop tracking. Furthermore, a dynamic environment model coupling static terrain height field and sudden spherical threat is constructed to systematically characterize the static obstacles and random dynamic threats faced by the UAV in complex scenarios such as mountains and hills. On this basis, multiple constraints such as flight altitude, pitch angle, horizontal turning angle, terrain safety margin, and multi-aircraft collision avoidance are integrated to establish a comprehensive objective function that includes range cost, attitude penalty, and safety cost. Through a collaborative mechanism of global optimization and local online correction, a reference trajectory that meets the requirements of formation safety and flight efficiency is generated and used as the input command for the tracking planning model, forming a closed-loop architecture of global optimization generation, local closed-loop tracking, and dynamic real-time correction for trajectory planning. Experimental results show that the success rate of dynamic obstacle avoidance in complex dynamic environments is always higher than 99.9%, and the mean square error of trajectory tracking is stable in the range of 0.02–0.04 km, which verifies its significant advantages in dynamic adaptability, tracking accuracy and formation safety. Full article

Underground mine emergencies compromise fixed communication infrastructure exactly when situational awareness is most critical for effective rescue operations. Existing LoRa mesh protocols fail in underground mines because they ignore the structured topology of tunnel networks, specifically the waveguide effect along straight galleries, severe signal discontinuity at junctions, and the dead-end geometry of working faces. This paper presents the Topology-Aware Concurrent LoRa (TACL) mesh protocol, in which each node autonomously infers its structural role from local RF observations and packet header information, without GPS, pre-loaded mine maps, or central coordination. Role classification resolves the contender estimation problem $\left(N_h\right)$ left open in the prior concurrent transmission literature, enabling provably bounded timing offsets before transmission. TACL assigns a spreading factor (SF)12 to dead-end source nodes for maximum link robustness and SF7–SF10 to relay nodes to create the inter-SF orthogonality margin required for concurrent decoding at junction nodes. Monte Carlo simulation of over 2000 trials yields TACL a PDR of 80.5% versus near-zero for all three baselines, confirming that topology-aware SF diversity is the necessary and sufficient mechanism to prevent junction collision collapse. Hardware deployment at the Missouri S&T Experimental Mine yields a 4.0× PDR improvement over the topology-agnostic concurrent transmission (CT)-fixed baseline, a median end-to-end latency of 1815 ms with 84× tighter latency spread than ALOHA-based protocols and 2.5× lower energy per delivered packet. These results establish that explicit exploitation of underground mine topology is essential for reliable, predictable, and energy-efficient emergency mesh communications in post-disaster underground mine scenarios. Full article

Collision avoidance in maritime unmanned aerial vehicle (UAV) operations must satisfy two competing objectives: ensuring reliable safety separation and minimizing unnecessary maneuver commands that increase operator burden and communication overhead. While deep reinforcement learning (DRL) has shown promise in handling high-dimensional encounter states, standard DRL approaches often prioritize safety at the cost of operational suitability, leading to frequent, oscillatory, or unnecessary avoidance commands that erode remote operator trust and consume limited communication bandwidth. To address this challenge, this paper proposes Resource-Aware Intrinsic Surprise Exploration (RAISE), a unified framework that balances collision avoidance performance with command economy. We conceptualize the issuance of avoidance maneuvers as a consumable “virtual resource”, compelling the agent to optimize its intervention budget. RAISE integrates this mechanism into the Soft Actor–Critic (SAC) architecture, augmented by a surprise-based intrinsic reward derived from the ensemble forward dynamics prediction error. This allows the agent to efficiently explore complex encounter scenarios driven by curiosity, while a resource-aware coefficient adaptively suppresses redundant actions when the communication or operational budget is constrained. Furthermore, an adaptive exponential moving average (EMA) scaling mechanism is introduced to stabilize the interplay between intrinsic and extrinsic rewards. Extensive simulations under diverse resource constraints and encounter geometries demonstrate that RAISE outperforms state-of-the-art baselines. It significantly reduces maneuver reversal rates and strengthens command stability without compromising safety margins. Specifically, under resource-constrained settings, RAISE suppresses excessive and unstable advisory behavior by reducing strengthening and reversal commands while maintaining effective collision avoidance; under resource-rich settings, it flexibly enhances safety buffers, demonstrating superior adaptability and operational realism for autonomous maritime UAV systems. Robustness evaluation confirms that RAISE maintains stable performance under sensor noise and wind disturbances. Full article

Ensuring safety in autonomous vehicles (AVs) requires predictive control methods that can handle dynamic constraints, uncertain interactions, and real-time decision making. This review examines safety-oriented model predictive control (MPC) for AVs using a PRISMA-guided screening process. From 363 records published between January 2015 and March 2026, 101 peer-reviewed studies were selected for qualitative synthesis. The literature is organized into three domains: collision avoidance and risk mitigation, trajectory tracking and path following, and intersection and coordination tasks. Across these domains, MPC has evolved from nominal tracking and geometric avoidance toward risk-aware, robust, hierarchical, and learning-enhanced formulations. Unlike broader reviews on autonomous driving control, this review focuses specifically on safety-oriented MPC and compares the reviewed literature in terms of safety mechanisms, uncertainty treatment, validation practice, computational feasibility, and deployment limitations. The review shows that MPC remains one of the most versatile frameworks for AV safety, but the evidence base is weakened by heavy reliance on simulation, inconsistent safety metrics, limited validation under uncertainty, and uneven treatment of computational feasibility. The most promising directions are hybrid architectures that combine model-based safety guarantees with uncertainty-aware prediction, learning-assisted adaptation, and scalable coordination mechanisms. Full article