Search Results (4,569)

Autonomous Mobile Robots (AMRs) are increasingly important in Industry 4.0 intralogistics but creating path planning systems that adapt to dynamic and uncertain Flexible Manufacturing Systems (FMS), especially managing conflicts among multiple AMRs with a need for scalable decentralised solutions, remains a significant challenge. This research introduces a dynamic path planning system for AMRs designed for reactive adaptation to FMS disturbances and generalisation across factory layouts, incorporating support for multiple AMRs with integrated conflict avoidance. The system is built on a Multi-Agent Systems (MAS) architecture, where software AMR agents independently calculate their paths using a hybrid Genetic Algorithm (GA) that employs Cell-Based Decomposition (CBD) and optimises path length, smoothness, and overlap via a multi-objective fitness function. Multi-AMR conflict avoidance is implemented using the Iterative Exclusion Principle (IEP), which facilitates priority-based planning, knowledge sharing through Predictive Collision Avoidance (PCA), and iterative replanning among agents communicating via a blackboard agent. Verification demonstrated the system’s ability to successfully avoid deadlocks for up to nine AMRs and exhibit good scalability. Validation in a simulated FMS environment confirmed robust adaptation to various disturbances, including static and dynamic obstacles, while maintaining stable run times and consistent path quality. These results affirm the practical feasibility of this hybrid GA and MAS-based approach for dynamic AMR control in complex industrial settings. Full article

As an important traffic and transportation roadway, tunnel engineering is widely used in important fields such as highways, railways, water conservancy, subways and mining. It is limited by complex geological conditions, harsh construction environments and poor robustness of the monitoring system. If the construction process and monitoring method are not properly designed, it will often directly induce disasters such as tunnel deformation, collapse, leakage and rockburst. This seriously threatens the safety of tunnel construction and operation and the protection of the regional ecological environment. Therefore, based on distributed fiber optic sensing technology, the full–cycle spatiotemporally continuous sensing information of the tunnel structure is obtained in real time. Accordingly, the health status of the tunnel is dynamically grasped, which is of great significance to ensure the intrinsic safety of the whole life cycle for the tunnel project. Firstly, this manuscript systematically sorts out the development and evolution process of the theory and technology of structural health monitoring in tunnel engineering. The scope of application, advantages and disadvantages of mainstream tunnel engineering monitoring equipment and main optical fiber technology are compared and analyzed from the two dimensions of equipment and technology. This provides a new path for clarifying the key points and difficulties of tunnel engineering monitoring. Secondly, the mechanism of action of four typical optical fiber sensing technologies and their application in tunnel engineering are introduced in detail. On this basis, a spatiotemporal continuous perception method for tunnel engineering based on DFOS is proposed. It provides new ideas for safety monitoring and early warning of tunnel engineering structures throughout the life cycle. Finally, a high–speed rail tunnel in northern China is used as the research object to carry out tunnel structure health monitoring. The dynamic changes in the average strain of the tunnel section measurement points during the pouring and curing period and the backfilling period are compared. The force deformation characteristics of different positions of tunnels in different periods have been mastered. Accordingly, scientific guidance is provided for the dynamic adjustment of tunnel engineering construction plans and disaster emergency prevention and control. At the same time, in view of the development and upgrading of new sensors, large models and support processes, an innovative tunnel engineering monitoring method integrating “acoustic, optical and electromagnetic” model is proposed, combining with various machine learning algorithms to train the long–term monitoring data of tunnel engineering. Based on this, a risk assessment model for potential hazards in tunnel engineering is developed. Thus, the potential and disaster effects of future disasters in tunnel engineering are predicted, and the level of disaster prevention, mitigation and relief of tunnel engineering is continuously improved. Full article

As the core component of high-performance mechanical transmission devices, semi-open impellers are widely used in aerospace, automobiles, and high-end industrial manufacturing. Their complex structures and variable blade shapes have extremely high requirements for machining accuracy and surface quality. However, at present, traditional manual programming for machining generally has problems of relatively low machining efficiency and machining accuracy. Therefore, we explored the application of the Siemens NX (UG NX12.0) software platform in modeling and process programming for machining semi-open impellers and designed an efficient machining strategy by combining rough machining, semi-finishing machining, and finishing machining in an optimized manner. By using the advanced functions of UG NX, the whole process from three-dimensional modeling and process planning to tool path generation was formulated, and machining verification was carried out on the Mikron Mill E500U five-axis machining center. The process plan significantly improved machining efficiency and optimized surface quality. After verification, the machining efficiency increased by more than 30%, and the machining accuracy was significantly improved, fully verifying the superiority and practicability of the UG NX platform in the machining of complex curved surface parts. Full article

This paper presents a novel approach to address the challenges of complex terrain, dynamic wind fields, and multi-objective constraints in multi-UAV collaborative path planning for forest firefighting missions. An extensible algorithm, termed Parallel Vectorized Differential Evolution-based Multi-Objective Jellyfish Search (PVDE-MOJS), is proposed to enhance path planning performance. A comprehensive multi-objective cost function is formulated, incorporating path length, threat avoidance, altitude constraints, path smoothness, and wind effects. Forest-specific constraints are modeled using cylindrical threat zones and segmented wind fields. The conventional jellyfish search algorithm is then enhanced through multi-core parallel fitness evaluation, vectorized non-dominated sorting, and differential evolution-based mutation. These improvements substantially boost convergence efficiency and solution quality in high-dimensional optimization scenarios. Simulation results on the Phillip Archipelago Forest Farm digital elevation model (DEM) in Australia demonstrate that PVDE-MOJS outperforms the original MOJS algorithm in terms of inverted generational distance (IGD) across benchmark functions UF1–UF10. The proposed method achieves effective obstacle avoidance, altitude optimization, and wind adaptation, producing uniformly distributed Pareto fronts. This work offers a viable solution for emergency UAV path planning in forest fire rescue scenarios, with future extensions aimed at dynamic environments and large-scale UAV swarms. Full article

Chart reasoning, a critical task for automating data interpretation in domains such as aiding scientific data analysis and medical diagnostics, leverages large-scale vision language models (VLMs) to interpret chart images and answer natural language questions, enabling semantic understanding that enhances knowledge accessibility and supports data-driven decision making across diverse domains. In this work, we formalize chart reasoning as a sequential decision-making problem governed by a Markov Decision Process (MDP), thereby providing a mathematically grounded framework for analyzing visual question answering tasks. While recent advances such as multi-step reasoning with Monte Carlo tree search (MCTS) offer interpretable and stochastic planning capabilities, these methods often suffer from redundant path exploration and inefficient reward propagation. To address these challenges, we propose a novel algorithmic framework that integrates a pheromone-guided search strategy inspired by Ant Colony Optimization (ACO). In our approach, chart reasoning is cast as a combinatorial optimization problem over a dynamically evolving search tree, where path desirability is governed by pheromone concentration functions that capture global phenomena across search episodes and are reinforced through trajectory-level rewards. Transition probabilities are further modulated by local signals, which are evaluations derived from the immediate linguistic feedback of large language models. This enables fine grained decision making at each step while preserving long-term planning efficacy. Extensive experiments across four benchmark datasets, ChartQA, MathVista, GRAB, and ChartX, demonstrate the effectiveness of our approach, with multi-agent reasoning and pheromone guidance yielding success rate improvements of +18.4% and +7.6%, respectively. Full article

In underground coal mines, narrow and irregular tunnels, dust, and gas hazards pose significant challenges to robotic path planning, particularly for shotcrete operations. The traditional A* algorithm has the limitations of limited safety, excessive node expansion, and insufficient dynamic obstacle avoidance capabilities. To address these, a hybrid algorithm integrating adaptive A*, Q-learning, and the Dynamic Window Approach (DWA) is proposed. The A* component is enhanced through improvements to its evaluation function and node selection strategy, incorporating dynamically adjustable neighborhood sampling to improve search efficiency. Q-learning re-plans unsafe trajectories in complex environments using a redesigned reward mechanism and an adaptive exploration strategy. The DWA module facilitates real-time obstacle avoidance in dynamic scenarios by optimizing both the velocity space and the trajectory evaluation process. The simulation results indicate that the proposed algorithm reduces the number of path nodes by approximately 30%, reduces the computational time by approximately 20% on 200 × 200 grids, and increases the path length by only 10%. These results demonstrate that the proposed approach effectively balances global path optimality with local adaptability, significantly improving the safety and real-time performance in complex underground environments. Full article

Rapid socio-economic development has made energy application and environmental issues increasingly prominent. Hydrogen energy, clean, eco-friendly, and highly synergistic with renewable energy, has become a global research focus. This study, using the EnergyPLAN model that includes the electricity, transportation, and industrial sectors, takes Jinan City as the research object and explores how hydrogen penetration changes affect the decarbonization path of the urban integrated energy system under four scenarios. It evaluates the four hydrogen scenarios with the entropy weight method and technique, placing them in an order of preference according to their similarity to the ideal solution, considering comprehensive indicators like cost, carbon emissions, and sustainability. Results show the China Hydrogen Alliance potential scenario has better CO₂ emission reduction potential and unit emission reduction cost, reducing them by 7.98% and 29.39%, respectively. In a comprehensive evaluation, it ranks first with a score of 0.5961, meaning it is closest to the ideal scenario when cost, environmental, and sustainability indicators are comprehensively considered. The Climate Response Pioneer scenario follows with 0.4039, indicating that higher hydrogen penetration in terminal energy is not necessarily the most ideal solution. Instead, appropriate hydrogen penetration scenarios should be selected based on the actual situation of different energy systems. Full article

This study aims to assess the role of headwater systems (HS) in enhancing ecological connectivity and supporting Green Infrastructure in the Centre Region of Portugal. Specifically, it identifies restoration opportunity areas within HS by analysing land-use changes over the past 70 years, modelling land-use scenarios to promote ecological resilience, and evaluating connectivity between HS and Natura 2000 sites. The methodology integrates spatial analysis of historical land-use data with connectivity modelling using least-cost path approaches. Results show substantial transformation in HS areas, notably the expansion of eucalyptus plantations and a decline in agricultural land. Approximately 58% of the HS are identified as requiring restoration, including areas within the Natura 2000 network. The connectivity assessment reveals that HS can function as effective ecological corridors, contributing to improved water regulation, soil conservation, gene flow, and wildfire mitigation. A total of 61 potential ecological linkages between Natura 2000 sites were identified. These findings highlight the strategic importance of integrating HS into regional and national Green Infrastructure planning and supporting the implementation of the EU Biodiversity Strategy for 2030. The study recommends prioritising headwater restoration through multi-scale planning approaches and active involvement of local stakeholders to ensure sustainable land-use management. Full article

Accurate path-loss characterization in the upper mid-band is critical for 5G/6G outdoor planning, yet classical deterministic expressions lose fidelity at 18 GHz, and purely data-driven regressors offer limited physical insight. We present a physics-guided residual learner that couples a calibrated two-ray model with an XGBoost regressor trained on the deterministic residuals. To enlarge the feature space without promoting overfitting, synthetic samples obtained by perturbing antenna height and ground permittivity within realistic bounds are introduced with a weight of $w=0.3$. The methodology is validated with narrowband measurements collected along two straight 25 m corridors. Under cross-corridor transfer, the hybrid predictor attains $0.59$–$0.62$ dB RMSE and $R^2\ge 0.996$, reducing the error of a pure-ML baseline by half and surpassing deterministic formulas by a factor of four. Small-scale analysis yields decorrelation lengths of 0.23 m and 0.41 m; a cross-correlation peak of unity at $\Delta =0.10$ m confirms the physical coherence of both corridors. We achieve <1 dB error using a small set of field measurements plus simple synthetic data. The method keeps a clear mathematical core and can be extended to other priors, NLOS cases, and semi-open hotspots. Full article

This study investigates the spatiotemporal optimization of small-scale emergency infrastructure in high-density urban environments, using nucleic acid testing sites in Macao as a case study. The objective is to enhance emergency responsiveness during future public health crises by aligning infrastructure deployment with dynamic patterns of population influx. A behaviorally informed spatial decision-making framework is developed through the integration of kernel density estimation, point-of-interest (POI) distribution, and origin–destination (OD) path simulation based on an Ant Colony Optimization (ACO) algorithm. The results reveal pronounced temporal fluctuations in testing demand—most notably with crowd peaks occurring around 12:00 and 18:00—and highlight spatial mismatches between existing facility locations and key residential or functional clusters. The proposed approach illustrates the feasibility of coupling infrastructure layout with real-time mobility behavior and offers transferable insights for emergency planning in compact urban settings. Full article

With the rapid development of smart manufacturing, the optimization of real-time monitoring in operating procedures has become a crucial issue in modern industry. Traditional disassembly and assembly (D/A) work, relying on human experience and visual inspection, lacks immediacy and a quantitative basis, further affecting operating quality and efficiency. This study aims to develop a thin-film force sensor and an inertial measurement unit (IMU)-integrated wearable device for monitoring and analyzing operators’ behavioral characteristics during D/A tasks. First, by having operators wear self-made smart gloves and 17 IMU sensors, the work tables with three different heights are equipped with a mechatronics machine for the D/A experiment. Common D/A motions are designed into the experiment. Several subjects are invited to execute the standardized operating procedure, with upper limbs used to collect data on operators’ hand gestures and movements. Then, the measured data are applied to verify the performance measure functional best path of machine D/A. The results reveal that the system could effectively identify various D/A motions as well as observe operators’ force difference and motion mode, which, through the theory of performance indicator optimization and the verification of data analysis, could provide a reference for the best path planning, D/A sequence, and work table height design in the machine D/A process. The optimal workbench height for a standing operator is 5 to 10 cm above their elbow height. Performing assembly and disassembly tasks at this optimal height can help the operator save between 14.3933% and 35.2579% of physical effort. Such outcomes could aid in D/A behavior monitoring in industry, worker training, and operational optimization, as well as expand the application to instant feedback design for automation and smartization in a smart factory. Full article

To address the problems of low coverage rate and low detection accuracy in UAV-based aircraft skin defect detection under complex real-world conditions, this paper proposes a method combining a Greedy-based Breadth-First Search Coverage Path Planning (GB-CPP) approach with an improved YOLOv11 architecture (INN-YOLO). GB-CPP generates collision-free, near-optimal flight paths on the 3D aircraft surface using a discrete grid map. INN-YOLO enhances detection capability by reconstructing the neck with the BiFPN (Bidirectional Feature Pyramid Network) for better feature fusion, integrating the SimAM (Simple Attention Mechanism) with convolution for efficient small-target extraction, as well as employing RepVGG within the C3k2 layer to improve feature learning and speed. The model is deployed on a Jetson Nano for real-time edge inference. Results show that GB-CPP achieves 100% surface coverage with a redundancy rate not exceeding 6.74%. INN-YOLO was experimentally validated on three public datasets (10,937 images) and a self-collected dataset (1559 images), achieving mAP@0.5 scores of 42.30%, 84.10%, 56.40%, and 80.30%, representing improvements of 10.70%, 2.50%, 3.20%, and 6.70% over the baseline models, respectively. The proposed GB-CPP and INN-YOLO framework enables efficient, high-precision, and real-time UAV-based aircraft skin defect detection. Full article

Achieving energy-efficient aerial coverage remains a significant challenge for UAV-based missions, especially over hilly terrain where consistent ground resolution is needed. Traditional solutions use changes in altitude to compensate for elevation changes, which requires a significant amount of energy. This paper presents a new way to plan coverage paths (CPP) that uses real-time zoom control of a pan–tilt–zoom (PTZ) camera to keep the ground sampling distance (GSD)—the distance between two consecutive pixel centers projected onto the ground—constant without changing the UAV’s altitude. The proposed algorithm changes the camera’s focal length based on the height of the terrain. It only changes the altitude when the zoom limits are reached. Simulation results on a variety of terrain profiles show that the zoom-based CPP substantially reduces flight duration and path length compared to traditional altitude-based strategies. The framework can also be used with low-cost camera systems with limited zoom capability, thereby improving operational feasibility. These findings establish a basis for further development and field validation in upcoming research phases. Full article

This paper addresses the issue of the poor collision avoidance effect of underactuated surface vehicles (USVs) during local path tracking. A virtual ship group control method is suggested by using Freiner coordinates and a model predictive control (MPC) algorithm. We track the planned path using the MPC algorithm according to the known vessel state and build a hierarchical weighted cost function to handle the state of the virtual vessel, to ensure that the vessel avoids obstacles while tracking the path. In addition, the control system incorporates an Extended Kalman Filter (EKF) algorithm to minimize the state estimation error by continuously updating the ship state and providing more accurate state estimation for the system in a timely manner. In order to validate the anti-interference and robustness of the control system, the simulation experiment is carried out with the “Yukun” as the research object by adding the interference of wind and wave of level 6. The outcome shows that the algorithm suggested in this paper can accurately perform the trajectory-tracking task and make collision avoidance decisions under six levels of external interference. Compared with the original MPC algorithm, the improved MPC algorithm reduces the maximum rudder angle output value by 58%, the integral absolute error by 46%, and the root mean square error value by 46%. The improved control algorithm reduces the maximum rudder angle output value by 42% and the maximum rudder angle output value by 10%. The control method provides a new technical choice for trajectory tracking and collision avoidance of USVs in complex marine environments, with a reliable theoretical basis and practical application value. Full article

As a national treasure, architectural heritage carries multiple value dimensions such as history, technology, art, and culture. With the increasing demand for architectural heritage protection and utilization, the traditional static digital model of architectural heritage based on geometric expression can no longer meet the practical application of multi-stage and multi-level scenarios. To this end, this paper proposes a value-chain-driven multi-level digital twin model of architectural heritage. Based on the three-stage logic of protection, management, and dissemination of value-chain classification, it integrates four types of models: geometry, physics, rules, and behavior. Combined with different hierarchical application levels, the digital model of architectural heritage is refined into a VCLOD (Value-Chain-Driven Level of Detail) detail hierarchy system to achieve a unified expression from spatial form restoration to intelligent response. Through the empirical application of three typical scenarios: the full-area guided tour of the Forbidden City, the exhibition curation of the central axis and the preventive protection of the Meridian Gate, the model shows the following specific results: (1) the efficiency of tourist guidance is improved through real-time personalized path planning; (2) the exhibition planning and visitor experience are improved through dynamic monitoring and interactive management of the exhibition environment; (3) the predictive analysis and preventive protection measures of structural safety are realized, effectively ensuring the structural safety of the Meridian Gate. The research results provide a theoretical basis and practical support for the systematic expression and intelligent evolution of digital twins of architectural heritage. Full article