The dexterous multi-fingered robotic hand， serving as a key end-effector， is pivotal for enabling robots to perform fine-grained grasping and compliant manipulation. Its advancement holds significant importance for promoting automation in manufacturing， enhancing the intelligence of service robots， and expanding applications in specialized environments. Focusing on humanoid multi-fingered dexterous hand technologies， this paper systematically reviews the current state-of-the-art and future trends. It begins by elucidating the fundamental concepts， system architecture， and typical characteristics of dexterous hands. This is followed by a comprehensive of research achievements from domestic and international teams and commercially available mainstream multi-fingered dexterous hand products， covering various degrees-of-freedom designs and their respective hardware and software implementations. Key technologies， including core hardware components， multi-modal sensory fusion， and control strategies， are critically analyzed. The paper subsequently summarizes practical applications across domains such as industrial assembly， daily life assistance， and operations in extreme environments. Current challenges， particularly in reliability， multi-modal coordination， generalization capability， human-robot safety， and integration and application， are identified. Finally， future research directions are prospected from multiple perspectives， including standard establishment， novel mechanical structures， advanced multi-modal perception and fusion， bionic evolution， and embodied intelligence， aiming to provide valuable insights for in-depth research and groundbreaking applications of dexterous hands.

Cable-driven robots have attracted significant attention from researchers due to their advantages of low inertia, light weight, and extensive operational range. However, the inherent flexibility of cables and their unidirectional force transmission characteristics pose challenges for precise control. Achieving efficient and accurate motion control requires in-depth research on cable tension distribution, robot dynamics, and control strategies. This research reviews the research progress in the field of cable-driven robots. Firstly, it focuses on tension computation and optimization methods, including null-space method, geometric method, and least-squares method, comparing their advantages, disadvantages, and applicable scenarios. Secondly, it summarizes advancements in dynamic modeling approaches, such as the Lagrange method, Newton-Euler method, and the principle of virtual work, evaluating their strengths and weaknesses in modeling the dynamics of cable-driven continuum robots. Thirdly, it reviews the progress in control strategies for cable-driven robots, comparing model-based and model-free control approaches. Finally, the current state of research is summarized, and future development trends in cable-driven robots are discussed.

To address the challenges of frequent bearing failures under complex working conditions， as well as the low real-time performance and strong dependence on manual feature extraction in traditional diagnostic methods， this paper proposes a bearing fault diagnosis method based on a deep learning model combining a Multi-Scale Convolutional Neural Network （MSCNN） and Long Short-Term Memory （LSTM）， and develops an intelligent bearing health management system. The system adopts an end-to-end diagnostic workflow， directly taking raw time-domain vibration signals as input. It extracts hierarchical local features across different frequency domains through MSCNN， and captures the temporal evolution of fault characteristics using LSTM， thereby achieving high-accuracy automated fault classification. To enhance the interpretability of diagnostic results and support intelligent maintenance decisions， the system integrates the Chinese large language model iFLYTEK Spark， which generates natural language diagnostic reports and maintenance suggestions through standardized prompts. The system is deployed on a domestically developed Phytium quad-core processor platform， ensuring full autonomy and reliability of both hardware and software components for industrial applications. Experimental results show that the proposed system achieves an average classification accuracy of 98.46% on the CWRU bearing dataset， and 96.73% on the AITHE bearing fault dataset， demonstrating strong robustness and cross-dataset generalization under complex and noisy conditions. With real-time visualization of diagnostic results and maintenance recommendations through a human-machine interface （HMI）， this system provides a reliable and intelligent solution for equipment health management and predictive maintenance.

To ensure the safety and stability of the power transmission systems and achieve comprehensive inspection and maintenance of newly constructed power transmission towers， this paper proposes a centrally symmetric quadrupedal humanoid climbing robot designed for existing foot pegs used by maintenance workers to climb towers. Each limb is configured with 3-1-2 arrangement. At the end of each limb， a large-tolerance， semi-enclosed hook-type gripping tool is designed specifically for the foot pegs. This tool features high tolerance， eliminating the need for precise end-effector positioning and enabling rapid engagement with the foot pegs. Humanoid climbing gait planning method is developed， facilitating the robot's full-range climbing of the power transmission tower by quickly hooking and gripping the foot pegs using the hook-type tool. Targeting a 40-meter-high self-supporting transmission tower， the robot's full-range climbing dynamics model and simulations were completed. Simulation results demonstrate that the proposed robot configuration can achieve humanoid full-range climbing of the tower， with a climbing time from the base to the top of less than 30 minutes， matching the efficiency of maintenance personnel. This provides a feasible solution for robotic maintenance applications in power transmission towers.

With the rapid development of generative artificial intelligence, the field of mechanical design has ushered in new changes. The design concept is gradually developed from the traditional “computer-aided + artificial experience” to “historical design data and knowledge + generative modeling” with advanced intelligence, and specific design behavior is developed from “manual modeling” to “generative modeling”, and the mechanical product design driver is developed from manual experience to data knowledge. In response to this development trend, a new mechanical design concept is proposed: Intelligent generative design (IGD). The content composition, core operation mechanism, design features, and key technologies of IGD are described in this article. On this basis, this study explores the application value of IGD in mechanical product design, and points out the new trend and development direction for the design of mechanical products.

To address the problems of scattered knowledge systems in ship assembly and ineffective mining and utilization of massive process data， this paper proposes an automatic knowledge graph construction technology for the shipbuilding domain based on large language models. This method uses large language models to convert unstructured and semi-structured ship data into structured data to build a ship process corpus. It models ship ontology knowledge structure with the assistance of large language models， designs an instruction prompting framework for ship assembly domain， and achieves efficient entity-relationship extraction， to complete the automatic construction of knowledge graphs. Additionally， the method uses triple sets constructed by general large language model instruction prompts as fine-tuning training sets to further fine-tune specialized small language models， ensuring the security of specific private ship data while reducing computational resources. Experimental results show that this method outperforms traditional baseline models in key metrics such as accuracy， providing a new technical approach for knowledge management and intelligent upgrading in the shipbuilding domain.

As an important component of intelligent manufacturing and intelligent operation and maintenance systems， industrial inspection robots are playing a key role in various complex industrial scenarios. With the continuous progress of deep learning， multi-sensor fusion， and autonomous navigation technologies， industrial inspection robots have significantly been improved in terms of accuracy， efficiency， and adaptability. This article systematically reviews the concept， key technologies， and typical applications of industrial inspection robots， and focuses on analyzing the research status of core technologies such as perception and recognition， autonomous positioning and navigation， advanced control， and intelligent decision-making. It also assesses the maturity and industrialization progress of current technologies by combining practical applications in fields such as power， workshops， and special environments. Despite significant achievements in this field， challenges still exist in perception accuracy， dynamic environment adaptability， and task execution intelligence. The development of key technologies is expected to continue in the directions of multi-source data fusion， autonomous learning， and collaborative operation. The article aims to provide a systematic reference and guidance for future research and industrial development of industrial inspection robot technology.

To address issues such as low detection accuracy， slow speed， missed and false detections， and large model parameter sizes in complex scenarios from a UAV perspective， this paper proposes an improved RBGE-YOLO algorithm model. Firstly， RFAConv is introduced in the backbone network to replace the original Conv， enhancing the model's ability to extract and fuse image features. Secondly， the neck network is reconstructed using BiFPN-GLSA to improve feature fusion and spatial feature utilization efficiency. Thirdly， a dual-layer small target detection structure is designed to strengthen the feature information of small targets. Finally， the Inner-EIoU loss function is utilized to address the limitations of IoU. Experiments on the VisDrone2019 dataset show that RBGE-YOLO improves Precision， Recall， mAP@0.5， and mAP@0.5：0.95 by 4.7%， 2%， 3.6%， and 2.5%， respectively， compared to the original YOLOv8s， while reducing the number of parameters by 16.4%. This achieves model lightweighting while significantly enhancing detection performance.

In view of the design goals of lightweight, compact and high torque density of new motors, the printed circuit board (PCB) technology was introduced into the stator winding manufacturing process, and an axial flux double PCB stator motor is designed. Taking a single effective conductor bar as the research object, an analytical model of the motor's induced electromotive force, electromagnetic torque and output power was established. A finite element simulation model of the PCB motor was constructed, and key parameter optimization research was carried out. The electromagnetic field and temperature field simulation analysis of the series and parallel double PCB stator motors were carried out. Based on the constructed test platform, the output performance and temperature rise characteristics of the PCB motor under different configurations were studied. Under the rated operating conditions of 1 500 r/min and 3 A, the output torque of the series-connected double PCB stator motor is 130 mN·m, the torque density can reach 2 000 N·m/m³, and the measured maximum temperature is 180.6 ℃; the output torque of the parallel-connected double PCB stator motor is 103 mN·m, the torque density can reach 1 584.62 N·m/m³, and the maximum measured temperature is 110.5 ℃. Compared with the series configuration under the same working conditions, the temperature rise is smaller, which can effectively improve the PCB motor heating problem.

To address the inefficiency of traditional manual visual inventory counting and the high deployment costs of existing automated solutions in Automated Storage and Retrieval Systems （AS/RS）， this paper proposes an intra-warehouse visual inventory system based on modular visual devices. A retrofit-free stacker-accessible modular visual inventory device is designed. On the basis of a YOLOv8-powered visual inventory algorithm for multi-surface information fusion from a single view， the accurate counting of complex stack patterns （e.g.， non-full stacks and staggered stacks） is effectively solved by combining front pallet layer identification with top pallet layer counting. The system also features a non-intrusive integration architecture between the Warehouse Visual Stock System （WVSS） and the existing Warehouse Control System （WCS） via a database， enabling dynamic task scheduling and data closed-loop. Experimental results on four palletized cargo datasets demonstrate a stack quantity recognition accuracy of 96.3% with a processing time of 0.11 seconds per storage location. This solution provides a new engineering path for automated warehousing， characterized by high precision， low deployment cost， and minimal operational disruption.

In recent years， indigenously developed， aerospace-specific 3D structural design systems in China have undergone robust development， with notable achievements in the R&D of core components. However， with the widespread adoption of Large Language Models （LLMs）， establishing an effective interface between 3D structural design and AI-driven methodologies remains a central challenge. Furthermore， existing LLMs lack the capacity for precise reasoning over 3D geometry and complex physical fields， such as aerodynamics， which precludes their direct application in the intelligent design of aircraft structures. Among aerospace structural components， the aircraft wing is critical for generating lift. Its design process is highly complex， heavily reliant on expert experience， and tightly coupled with aerodynamic performance. Consequently， traditional design paradigms are characterized by lengthy iteration cycles and substantial costs. To address this challenge， this paper presents Airfoil-LLM， an intelligent design interface for the 3D modeling of aircraft wings， using the wing as a representative case study. Based on the Transformer architecture， this interface integrates natural language encoding with the decoding of CAD modeling sequences to enable intelligent and automated 3D wing generation. To support model training and validation， we have constructed a large-scale 3D wing design dataset. This comprehensive dataset comprises parameterized 3D CAD models， a wide spectrum of flight conditions from subsonic to supersonic regimes， key aerodynamic performance metrics， and multi-level textual descriptions. Experimental results demonstrate that Airfoil-LLM is capable of deeply comprehending textual descriptions ranging from simple geometric attributes to complex， coupled "geometry-performance" requirements. The system generates 3D models that align closely with the design targets in both geometric shape， achieving a maximum Intersection over Union （IoU） of 0.831， and aerodynamic performance.

The advancement of Industry 4.0 necessitates the deployment of intelligent， low-cost robotic systems on edge devices. However， the high computational complexity of Deep Reinforcement Learning （RL） algorithms presents a major obstacle to their implementation on resource-constrained platforms such as the Raspberry Pi. To overcome this challenge， this paper introduces a lightweight RL framework tailored for industrial robot sorting tasks. The core contributions are threefold： First， we propose a joint compression method combining Gradient Sensitivity-guided structured Pruning （GS-Pruning） with hierarchical quantization， which reduces model size by over 90% and achieves real-time inference below 35 ms on a Raspberry Pi while preserving policy accuracy. Second， we design a Dynamic Weight Adaptive Reward function （DWAR） that balances sorting efficiency， motion stability， and energy consumption， successfully suppressing robotic arm jitter and cutting average energy use by 18.1%. Third， we construct an end-to-end deployment system， RPi-EdgeRL， featuring a multi-threaded pipeline and a safety watchdog to guarantee stable and efficient autonomous operation. Experiments conducted on a FR3 collaborative robot validate our framework， achieving a 93.5% success rate in complex sorting tasks and confirming the feasibility and superiority of this low-cost， high-efficiency solution for real-world industrial applications.

The harmonic reducer, a crucial component of industrial robots, works in complex and variable environments, leading to significant losses when failures occur. Due to the challenges in acquiring actual vibration data of harmonic reducers, the limited number of fault sample, missing data labels, and differences in data distribution under varying working conditions, a fault diagnosis method for harmonic reducer under different working conditions based on digital twin is proposed. Firstly, a digital twin model of the faulty harmonic reducer is constructed using dynamic modeling to generate twin data. Secondly, a virtual-real mapping method based on a cyclic generative adversarial network is proposed to achieve the mapping between twin data and real measured data. To enhance feature extraction and suppress noise interference, an improved semi-soft threshold function is integrated into a deep residual shrinkage network. Meanwhile, the extracted features are subjected to domain adaptation in unsupervised scenarios, using the maximum mean discrepancy to reduce distribution differences between domains, thereby achieving fault diagnosis under different working conditions. Finally, a fault simulation test bench for the harmonic reducer is established, and experimental verification shows that the proposed method achieves an average accuracy of 99.2% in all transfer tasks. It effectively addresses the fault diagnosis challenges of harmonic reducers in unsupervised scenarios under different working conditions.

The modeling analysis and identification of the spatial errors of CNC machine tools have always been important steps in error compensation. Firstly, the research history and technological development of the modeling theories and identification methods for machine tool spatial errors are discussed. Secondly, the modeling and analysis of machine tool spatial errors is an important prerequisite for error compensation. The modeling theory of machine tool spatial errors has been comprehensively reviewed and analyzed, including methods such as rigid body kinematics theory, homogeneous coordinate change theory, D-H transformation theory, multi-body theory, and screw theory. Thirdly, the accurate measurement and precise identification of spatial error elements in machine tools are key to achieving effective control. The current status and development trends of key measurement and identification methods for machine tool spatial errors are specifically introduced and comprehensively evaluated, including laser interferometer multi line method, body diagonal method, as well as ball bar method, laser tracker method, and other methods. Finally, the modeling, detection, and identification of spatial errors in integrated machine tools are systematically analyzed to identify the problems that still need to be solved in improving the spatial accuracy of existing CNC machine tools. The importance of technological innovation in improving measurement efficiency and accuracy is emphasized; And prospects for future development directions have certain guiding significance for improving the accuracy of CNC machine tools.

Based on human-machine coupling modeling and biomechanical analysis， a shoulder-elbow rehabilitation assistive device featuring 5 degrees-of-freedom （DoF） rotational joints and 3-DoF sliding adjustments was developed. Motion capture experiments were conducted to obtain personalized scaled musculoskeletal models and reproduce upper limb rehabilitation movements through inverse kinematics. Utilizing Hill-type muscle models and the Computed Muscle Control （CMC） algorithm， the study analyzed muscle forces and energy consumption during rehabilitation training. Results demonstrated significant reductions in muscle forces for primary movers under assistive support： the long head of biceps brachii showed a 51.34% average force reduction， while the lateral head of triceps brachii exhibited 49.05% decrease. Energy consumption decreased by 30.74% and 36.56% in the long and short heads of biceps brachii respectively， with peak reductions exceeding 40%， indicating sustained unloading effects during elbow motion. Secondary muscles including the posterior deltoid and medial head of triceps brachii maintained moderate 10% reductions， balancing unloading requirements with joint stability to prevent over-intervention. The analysis confirms that the rehabilitation assistive device effectively reduces muscular burden and energy expenditure during training， mitigates muscle overload risks， and provides efficient assistance for patient rehabilitation.

To address the issues of suboptimal performance， and high rates of missed detection and false detection in steel surface defect detection technology in industrial production environments， an improved YOLO11 algorithm called GCI-YOLO11 has been proposed. Firstly， in the feature extraction part， the GC-C3k2 module based on the GCNet attention mechanism was designed to enhance the algorithm’s capability to extract contextual feature information from images. Secondly， the CARAFE upsampling algorithm was introduced in the neck part to enable the algorithm to aggregate contextual information within a large receptive field， reducing the loss of feature information during the upsampling process. Finally， Inner-CIoU was used to replace CIoU for loss function optimization， and auxiliary regression box was introduced to improve detection accuracy and model generalization capability. Experimental results show that， GCI-YOLO11 achieved improvements of 2.9% and 2.3% in mAP₅₀ and mAP_50-95 on the NEU-DET dataset， and 1.6% and 0.3% in mAP₅₀ and mAP_50-95 on the GC10-DET dataset， showing better detection performance.

Vehicle dynamics theory is fundamental to automotive design and control. With the rapid development of automotive electrification and intelligence, novel chassis configurations characterized by distribution, modularity, and redundancy have disrupted traditional boundaries of vehicle motion functions. The integration of onboard, roadside, and connected intelligent sensing information has transformed vehicle systems into cyber-physical systems. Existing vehicle dynamics theories, however, struggle to uniformly characterize the dynamics of multi-mode chassis structures and fail to elucidate the mechanical interactions between vehicles and multi-source external environmental information, highlighting critical limitations in model generality and environmental information integration. To address these issues, a generalized vehicle system dynamics framework is proposed. This framework abstracts chassis constraints, inter-vehicle interactions, and information exchange as generalized internal forces within the vehicle system, thereby constructing a coupled dynamics system encompassing mechanical, electronic, and informational multiphysics interactions. Furthermore, it enriches the traditional “modeling-estimation-control” theoretical paradigm, forming a unified theoretical framework to guide the chassis design and coordinated dynamic control of high-performance vehicles.

Segment assembly is an essential process in shield construction. The artificial manipulation is ineffective, high-risk and irregular in its quality. It is of great significance to automate the segment assembly process for improving the construction quality of shield tunnel, increasing the operating efficiency and promoting the intelligent construction of underground engineering. Based on the research work carried out by domestic and foreign scholars in the automatic assembling of segments，the article summarizes the research work from four aspects：including auto-selection, automatic perception, automatic movement of assembly machines, and automatic servo system of assembly machines. The article analyzes the research progress and shortcomings of the key technologies in various aspects of automatic segment assembly. The purpose of segment selection is divided into design stage typesetting and assembly point selection in construction period. Assembly point selection mainly uses the segment axis to fit shield machine attitude. The parameters include gap of shield tail and stroke difference of propulsion cylinder, but their weight coefficient determination is strongly dependent on construction experience. Pose perception of segment method is divided into contact measurement and non-contact measurement. The image-based target detection technology in non-contact measurement is better, but its algorithm accuracy and efficiency still need to be improved. The D-H method is mainly used to describe the pose and motion of the mechanical arm. The trajectory planning focuses on using polynomial curves to smooth the motion path to reduce the abrasion of the machine joints. The segment assembly machine is developing towards the direction of parallel mechanism with redundant degrees of freedom, and its assembly efficiency and accuracy are better. The servo system of the assembly machine controlled by the proportional valve has high accuracy, and multi-axis motion can improve the efficiency of segment assembly. Finally, the deficiencies of research are discussed, and new insights and directions are proposed. The research can provide reference for further improvement of the automatic assembly technology of segments and promotion of the intellectualization of underground engineering equipment.

In order to reduce the monitoring and operating pressure of operators during deep peak shaving， an intelligent desulfurization control system is proposed to address the problems of poor measurement accuracy and large inertia and delay in the controlled objects that prevented long-term stable automatic operations. By using BP neural network， a mapping relationship is constructed between signals such as flue gas flow rate， SO₂ concentration in the raw flue gas and slurry pH to achieve soft measurement of slurry pH value； Replacing conventional PID with variable structure predictive control and combining it with more accurate and reasonable feedforward signals ensures the control effect of the desulfurization system under rapidly changing load and coal quality conditions. Later， utilizing the unit ICS system， the desulfurization intelligent control system is successfully applied to a 650 MW unit. The operation results show that after the system is put into operation， the SO₂ concentration at the outlet is stably controlled within 25 mg/m³， and the deviation between the pH value of the slurry and the set value is kept within 0.2， and there are no significant fluctuations during the variable load and pH meter flushing process. The desulfurization is automatically put into operation for a long time， effectively reducing the operating pressure of the operators.

Industrial robots， with their high flexibility and large working range， have gradually become another important processing equipment besides CNC machine tools in national strategic fields such as aerospace and maritime industry in China. The dynamic characteristics of the robot end are dominated by its joints. To improve the dynamic performance of the robot， it is necessary to start with its weak joints， make improvements and innovations on the basis of the existing series form， and explore new high-stiffness driving methods and robot configurations. A 2-RPR robot for milling large propellers is proposed， which includes a six-axis robot main body and a double electric cylinder branch chain. The translation of the electric cylinder drives the rotation of the robot joint， thereby improving the stiffness of the whole robot. In order to meet the processing space requirements of large propellers， the length parameters of each link of the robot are optimized based on the genetic algorithm to realize the optimization of the working space of the whole robot， so that the working space meets the processing range of a single blade and has the maximum utilization rate.

The hypoid gear, as a complex spatial transmission widely utilized in aviation, specialized vehicles, and precision drive systems, has its meshing quality directly impacting the service performance of the entire machine. Although significant progress has been made in the design theory, generation mechanism, surface optimization, and manufacturing of this type of gear transmission, the increasingly performance requirements of high-level equipment present greater challenges for the active design of such transmissions. A detailed exposition of the research progress and development trends in the forward design methodologies of this type of transmission considering literature review, market research, and project studies has been provided. It focused on the configuration design, geometric parameter design, manufacturing parameter design, contact analysis, and tooth surface geometrical optimization of hypoid gears. Moreover, it systematically outlines the development trends of this transmission type to meet the service demands of high-level equipment and artificial intelligence. The aim is to provide theoretical and technical support for researchers and engineers in this field and to promote the advancement of hypoid gear forward design technology in China.

With the rapid development of digital manufacturing technology, a large number of machining process instances have accumulated in enterprise databases. Based on the basic principle that “geometric similarity likely leads to process similarity”, effective reuse of process knowledge can be achieved through identifying and extracting similar three-dimensional geometric process information, thereby enhancing the intelligence level of process decision-making systems and significantly shortening product development cycles. Against the background of rapid development in NC machining process reuse technology, systematically grasping its current status and future trends and providing comprehensive literature reviews for process designers has important theoretical and practical significance. The research systematically analyzes and summarizes the latest research progress of NC machining process reuse technology from three dimensions: first, at the macro process reuse level, methods for reusing the overall processing route of products are discussed; second, at the micro process reuse level, focus is placed on the precise extraction and application technology of process knowledge in specific processing links; finally, process reuse technology based on machine learning concentrates on the processing of unstructured CAD model data and the complex mapping relationship between them and process information. These research results not only have important theoretical guiding value for improving process design efficiency, but also show significant application prospects in promoting the improvement and optimization of enterprise process knowledge management systems.

It is of great significance to efficiently scheduling various resources to complete tasks for intelligent workshops with multiple production factors. An efficient hybrid evolutionary algorithm is proposed to solve a flexible job shop scheduling problem with multiple production factors. Firstly, a MFFJSP-WA model incorporating four production factors—jobs, machines, AGVs, and workers—is constructed with the objective of minimizing the maximum completion time based on the analysis of the problem background and the operation conditions of multiple factors. Since the model includes four kinds of decision variables, the hybrid initialization strategy combining heuristic and random methods is proposed to generate a high-quality initial population. A global search method based on classical genetic operators is designed according to the four-layer encoding structure of individuals. To address the issue of easily falling into local optimum, a multi-neighborhood local search method guided by a memory mechanism is proposed to enhance the algorithm's local search capability. Finally, the proposed algorithm is tested on sets of instances expanded from benchmarks. The experimental results show that the hybrid initialization strategy and local search operation can effectively improve the algorithm’s performance. Compared with various advanced algorithms in the field, the proposed algorithm is superior in solution quality performance.

Electric vehicle charging and battery swapping stations face multiple operational challenges including low service efficiency， poor economic benefits， and weak grid interaction capability. A V2G coordinated scheduling model based on hierarchical deep reinforcement learning is proposed which effectively reduces decision complexity through a collaborative architecture of strategic and tactical layers， and significantly enhances system responsiveness. Empirical research demonstrates that the model exhibits substantial practical value in actual charging station environments， primarily reflected in reasonable growth of operational revenue， optimized energy utilization efficiency， significant improvement in service quality， and effective reduction in user waiting times. Compared with traditional scheduling methods， the SAC algorithm adopted in this study shows stronger adaptability and stability when facing complex decision environments， effectively responding to uncertainties such as traffic flow fluctuations and electricity price changes. The research results provide an implementable intelligent scheduling solution for electric vehicle charging and battery swapping stations， offering valuable technical reference for addressing actual operational issues in the industry， and contributing positively to the sustainable development of the electric vehicle industry.

The aircraft skin is the primary component forming the aerodynamic shape of an aircraft, characterized by large size, thin wall (thickness 2～6 mm) and complex structure. Currently, manufacturers generally adopt a manual comparison-marking-trimming method to remove the edge allowance of the skin parts, leading to large cumulative human errors and difficulties in controlling assembly quality. Vision/force-guided industrial robot milling with high-flexibility and large operation range provides a novel approach to solving these problems. However, difficulties in simultaneous calibration of dual-robot systems, smooth path generation for machining and accurate control of the robot’s trajectory have become the bottlenecks restricting the application of robot milling for the aircraft skin. The above challenges can be summarized as the simultaneous decoupling of spatial transformation and the quantitative control of pose errors. To address these issues, this paper conducts in-depth research on dual-robot system calibration, smoothing machining path generation and closed-loop feedback control of the robot’s end-effector. The Part I proposes simultaneous calibration method of dual-robot system for robotic tracking/measuring-machining, establishes kinematics model of robot-tracking system and studies method to generate a smooth machining path for aircraft skin. The Part II studies closed-loop feedback control model for robot’s end-effector under external tracking system, develops closed-loop feedback control system for robot. The simultaneous calibration accuracy test of dual-robot, the trajectory accuracy test of end pose with closed-loop control, and the robotics milling accuracy test of typical skin samples are carried out to validate the effectiveness of the proposed methods.

As one of the core executive unit of the multifunctional system of launch vehicles, the action reliability and separation accuracy of separation and thrust mechanisms directly affect the success or failure of space launch missions. With the increase of the complexity of space missions and the carrying capacity of rockets in various countries, separation and thrust mechanisms face more stringent technical requirements in terms of bearing capacity, response speed, and environmental adaptability. A review systematically combs through the application and development status of separation and thrust mechanisms for launch vehicles at home and abroad, introduces in detail the working principles and characteristics of various separation and thrust mechanisms from four aspects: pyrotechnic, spring, pneumatic, and other energy sources, reviews the development in the field of dynamic characteristics, impact response, and reliability of separation and thrust mechanisms for launch vehicles, and introduces the simulation analysis techniques of some typical mechanisms. Finally, it looks forward to the development trend of separation and thrust mechanism products for launch vehicles, aiming to provide references for the innovative design and systematic development of separation and thrust systems for new-generation launch vehicles.

Facing the critical strategic demand for enhancing the resilience and security of industrial and supply chains at the national level， the paper focuses on digital twin technology as a key enabler for driving the digital and intelligent transformation of warehousing and logistics systems. A three-stage evolutionary trajectory is systematically outlined， progressing from static modeling to dynamic synchronization and ultimately to intelligent decision-making. In view of the structural challenges in traditional warehousing and logistics systems such as data silos， lagging equipment maintenance， rigid processes， insufficient flexibility， and lack of holistic optimization， this study constructs a systematic empowerment pathway encompassing five dimensions： “omni-domain data integration—intelligent operations and maintenance reconstruction—process simulation optimization—flexible collaborative scheduling—global decision simulation.” The construction of a unified data foundation enables standardized access and high-quality governance of multi-source heterogeneous data； Deployment of a predictive maintenance platform significantly enhances equipment reliability and system continuity； Application of virtual simulation and dynamic optimization technologies achieves intelligent restructuring of warehouse operations and efficiency multiplication； The construction of an elastic resource scheduling mechanism enhances the adaptability of the system to dynamic demands； By building a full-chain simulation decision-making sandbox， the system is boosted from empirically driven local decisions to data- and model-driven global autonomous optimization. In the practical application within the chemical fiber industry， this technology system has increased production efficiency by 5%， reduced operational costs by 15%， improved equipment utilization by 10%， and shortened fault-handling time by 30%. Looking ahead， digital twin technology will evolve toward “holistic coordination and intelligent symbiosis，” providing critical support for constructing autonomous as well as controllable modern warehousing and logistics systems and cultivating new productive forces.

In order to adapt to the characteristics of complex electronic equipment， such as multi-variety， variable batch， multi-level blind matching and vertical interconnection， a six-degree of freedom heterogeneous assembly robot arm has been independently developed and applied to the assembly line of complex electronic equipment. Firstly， the structure composition， configuration advantages and problems in practical application of the heterogeneous six-axis manipulator are introduced. Secondly， the forward and inverse kinematics algorithm of the heterogeneous six-axis manipulator is established by using D-H parameter method， and the kinematics model of the heterogeneous six-axis manipulator is constructed. Then the calibration algorithm， trajectory planning algorithm， collision control algorithm and other methods of the heterogeneous six-axis manipulator are studied. Finally， the field calibration experiment and MATLAB simulation analysis are used to verify the motion planning method， which proves the rationality and practicability of the relevant methods.

To address the challenges faced by mobile robots in overcoming obstacles in unstructured environments such as agricultural inspections and disaster rescue， this study proposes a design scheme for a four-wheeled mobile chassis that integrates a rocker-steering suspension with a crank-slider mechanism. First， kinematic and dynamic models of the walking mechanism were established to analyze the influence of key configuration parameters （e.g.， support wheel center distance， hinge distance） on terrain adaptability and load platform posture. A multi-objective optimization method was employed to determine the optimal parameter combination （L_F =200 mm，k₁=0.9）. Second， a three-dimensional virtual prototype was developed by integrating a crank-slider mechanism and symmetric frame design. Dynamic simulations conducted on the RecurDyn platform validated the chassis performance in traversing 18 mm speed bumps and 20 mm semi-cylindrical obstacles， showing pitch angle fluctuations within ±3°and peak torque demand ≤15 N·m. Finally， prototype tests demonstrated that the chassis can stably cross 90 mm speed bump-type obstacles under a 75 kg load， with a linear motion speed of 1.8 m/s and a path deviation of less than 20 mm/5 m. The results indicate that this design significantly enhances the terrain adaptability of mobile robots in unstructured environments， providing a reliable mobile platform for agricultural inspection， logistics， disaster rescue， and similar scenarios.

Structural reliability analysis is crucial for ensuring the safe and reliable operation of engineering equipment. The accuracy of traditional first-order reliability methods and their improved algorithms is limited by the type of limit state functions and gradient information，which may not be applicable to certain complex engineering cases. In the face of increasingly complex engineering scenarios，although the establishment strategies for multi-fidelity models have been widely studied，their application potential in reliability analysis remains to be further developed. Therefore，an adaptive Multi-Fidelity Kriging (MF-Kriging) model-assisted first-order reliability analysis method is proposed in this study. Firstly，the first-order reliability analysis problem is transformed into an unconstrained optimization problem through the augmented Lagrange function，enabling the use of heuristic optimization algorithms for its solution. Secondly，adaptive MF-Kriging modeling is employed by integrating multiple data sources to further reduce the computational cost of reliability analysis. Based on this reliability analysis method，any adaptive MF-Kriging modeling strategy，whether global or local search，can be nested. Additionally，considering that one of the important steps of the first-order reliability method is to accurately locate most probable point(MPP)，this study proposes a hybrid MF-Kriging modeling strategy. By balancing a global search strategy with a local search strategy based on iterative MPP neighborhood，it achieves a trade-off between local accuracy and global convergence in reliability analysis. Finally，the proposed method was applicated through four mathematical examples and three engineering cases. The results illustrate that the proposed method offers significant advantages in terms of accuracy，efficiency，and robustness.

In response to the challenges posed by complex microhole backgrounds in frames， a multitude of small and medium-sized defective targets， and the high degree of shape randomness encountered in mobile phone visual inspections， we have developed an enhanced YOLOv8-burr model based on YOLOv8 model. This model incorporates a lightweight global attention transformation module， which leverages packet convolution， within the network neck region. It also integrates a multi-scale feature extraction module into the backbone and employs a polarization self-attention mechanism along with a CARAFE operator in the network sampling stage. These innovations enable the model to harness global feature information and multi-layer channel details for more precise detection of small target defects. The experimental results show that the improved model has a size of 14.4M and can achieve 92.1% accuracy of microhole defect recognition， and in the category of "burr" defects that are difficult to identify， the accuracy has been improved by 10.4% compared with the original model before improved， which meets the identification accuracy requirements of the robot for the identification of microhole machining defects in the mobile phone frame.

Lithium plating on the negative electrode is one of the critical issues restricting the safety and lifespan of lithium-ion batteries. To enhance the safety and extend the lifespan of lithium-ion batteries, a lithium plating diagnosis method is proposed which is based on multidimensional feature mining and cluster analysis. Low-temperature lithium plating experiments are designed, and experimental data of batteries are collected. A high-precision equivalent circuit model of the battery is established, and a lithium plating feature extraction method based on model parameter identification and capacity increment analysis, as well as a feature space dimension reduction method based on principal component analysis, are proposed. Based on this, an adaptive grading diagnosis method for lithium-ion battery lithium plating faults is proposed using a density-based clustering algorithm optimized by particle swarm optimization, and the accuracy of the proposed method is verified based on the difference in capacity before and after lithium plating and scanning physical detection methods. The diagnostic results show that the lithium plating diagnosis results based on multi-dimensional features are optimal. Compared with single-dimensional lithium plating diagnosis methods based on battery model features, the missed diagnosis rate decreases by 8.00%, and compared with single-dimensional lithium plating diagnosis methods based on capacity increment curve features, the missed diagnosis rate decreases by 8.00% and the misdiagnosis rate decreases by 3.63%. At the same time, scanning electron microscope and inductively coupled plasma inspection results are consistent with diagnostic results, and can accurately diagnose mild and severe lithium plating, realizing graded diagnosis of lithium plating in lithium-ion batteries.

Aiming at the critical problem that real-time industrial defect detection systems are difficult to balance detection speed， accuracy and computational resource constraints in edge computing environments， a fast lightweight industrial defect detection architecture based on an efficient hybrid state space model is proposed. The architecture designs a C2f_EfficientViM_CGLU fast feature extraction module that deeply integrates the global sequence modelling capability of the visual state space model with the efficient local feature enhancement mechanism of convolutional gated linear units， achieving fast and efficient extraction of complex defect features. The HSM-SSD （Hidden State Mixer based State Space Duality） efficient state space modeling mechanism is introduced to process long sequence dependencies with O（n） linear complexity， significantly improving the fast recognition capability for irregularly shaped and sparsely distributed defects. A Slimneck fast lightweight feature fusion network is constructed through GSConv （Ghost Shuffle Convolution） sparse convolution and VoV-GSCSP （Variance of Variance Ghost Shuffle Cross Stage Partial） efficient feature fusion strategies， achieving significant improvements in inference speed and extreme model compression while ensuring detection accuracy. Comparative experimental results on NEU-DET and APDDD standard datasets show that the proposed network architecture achieves mAP50 of 92.13% on NEU-DET dataset， improving 9.77 percentage points compared to the baseline model YOLOv8n， with only 2.9 M parameters and 7.7 GFLOPs computational complexity， reducing parameters by more than 93% compared to the traditional Faster-RCNN method. The mAP50 on APDDD dataset reaches 89.68%， validating the good generalization performance and fast detection capability of the method. This study provides a theoretical foundation and an efficient and feasible fast detection technical solution for real-time quality control in Industry 4.0 intelligent manufacturing environments.

In grain storage management, the curved surface structure of silo walls imposes stringent demands on the adhesion performance of wall-climbing robots. These robots must possess sufficient flexibility to adapt to curved surfaces while maintaining adequate rigidity to ensure stable support. We find that when a rigid suction cup is employed on walls with varying curvature, the limited deformation capacity of the suction cup body causes the sponge to adopt a “saddle-shaped” deformation during adhesion, which significantly diminishes the adhesion performance. To address this issue, we optimize the rigid suction cup structure, leading to the design of a semi-rigid suction cup with distributed rigid elements. The adhesion force experiments on various simulated substrates reveal that the rigid suction cup exhibit forces of 205.49 N, 307.56 N and 360.25 N on simulated substrates with curvature radii of 100 mm, 200 mm and 400 mm, respectively. In contrast, the semi-rigid suction cup exhibit slight fluctuations in adhesion force across different curved surfaces, yet maintain a stable overall value around 420 N. This solution achieves a significant enhancement in adhesion performance on complex curved surfaces, effectively reducing the risk of detachment during robot operation, which establishes a reliable foundation for expanding the application of wall-climbing robots in areas such as silos.

With the rapid development of computer vision and artificial intelligence， object detection technology has been widely applied in the field of fire protection. A smart fire extinguisher dial recognition device based on the improved YOLOv5 algorithm has been designed and implemented to address the low efficiency and misjudgment of manual recognition of fire extinguisher dials in traditional fire equipment maintenance and repair processes. This device integrates the Raspberry Pi 4B computing core and a high-resolution camera， combined with a closed enclosure， a light shielding side panel， and an adaptive fill light system， effectively suppressing external interferences and adapting to different fire extinguishers of varying capacities. At the algorithm level， the CBAM attention module and BiFPN feature fusion network are introduced to optimize the feature extraction ability and multi-scale object detection performance of the YOLOv5m model. At the same time， an image segmentation strategy is proposed to enhance the recognition accuracy of the model for small targets in complex backgrounds. The improved model has higher accuracy and precision. Onsite tests have shown that the device has a 100% accuracy rate in recognizing the pressure state of 3 kg， 4 kg， 5 kg， and 8 kg fire extinguishers in complex industrial environments. After integration with the intelligent IoT system for vehicle-mounted fire-fighting equipment diagnosis， the detection data of the device can be uploaded in real time to the cloud platform for further data analysis and status evaluation of the system. In the maintenance and repair process of fire extinguishers， it demonstrates high work efficiency and automation level， providing strong technical support for the maintenance and repair of fire-fighting equipment.

Insufficient control precision is caused by strong nonlinearity in vacuum butterfly valve pressure control systems. A dual-mode switching strategy fusing Active Disturbance Rejection Control (ADRC) and PID control is proposed. The controller utilizes an extended state observer to uniformly estimate and compensate for aggregated disturbances including gas temperature drift, sealing friction, and gas source fluctuations. A pressure error threshold triggering mechanism is designed to activate ADRC exclusively during dynamic processes for rapid overshoot suppression, while automatically switching to lightweight PID control during steady-state operation to maintain precision. Compared with conventional PID control, settling time of proposed method is significantly shortened and overshoot substantially reduced. Compared with single ADRC control, steady-state error is effectively minimized. Under flow disturbances, pressure recovery time is 67% faster than that of PID control, with a steady-state error below 25 Pa. This approach significantly enhances system response speed, precision, and robustness, fully leveraging the cost advantage of butterfly valves. And it provides a high-performance, low-cost vacuum pressure control solution for semiconductor, aerospace, and related fields.

Spiral baffle is an important element in shell and tube heat exchangers. Effectively obtaining the spatial position information of the curved spiral baffle surface is the key to solving the tube hole machining of spiral baffle. Aiming at the problems of weak texture， multiple occlusions， reflective surface， and difficulty in meeting industrial requirements on accuracy and time for 3D reconstruction， this paper proposes a matching algorithm that combines feature detectors with dense vision and a variable field-of-view attention module， to obtain more feature points in weekly textured and reflective areas； The cascaded CasMVSNet architecture for dense point cloud reconstruction for spiral baffles is used to reduce surface voids and shorten reconstruction time. The experimental results on the self collected dataset show that the proposed 3D reconstruction technique yields a perfect spiral baffle surface with almost no voids or noise in weak texture areas. Compared to other algorithms， the reconstruction accuracy has improved by 3.96%， the cumulative error curve area of pose estimation has increased by 6.48%， and the reconstruction time has been reduced to within 180 seconds. This proves the reliability and effectiveness of the reconstruction technique.

In-space additive manufacturing (ISAM) is considered a key technology for achieving deep space exploration, efficient utilization of space resources and long-term habitation on extraterrestrial bodies. When compared to conventional terrestrial additive manufacturing, the development of ISAM technology is uniquely challenged by the space environment, which is characterized by high vacuum, intense radiation, microgravity and extreme temperature variations. Breakthroughs are required in equipment compatibility, process control, raw material selection and other aspects. ISAM research is currently focused on two cutting-edge fields: On-orbit manufacturing and surface construction on extraterrestrial bodies. Various types of forming processes, including materialextrusion, directed energy deposition, powder bed fusion, stereolithography and computational axial lithography, are being developed. The current research status and development trends of ISAM technology are reviewed, with comparisons made between the process principles and characteristics of various ISAM technologies. Typical microgravity verification platforms and their testing methods are summarized. Cases of ISAM technology research and application in actual space environments are outlined. The significant challenges and potential opportunities faced in the development of ISAM technology are analyzed and key directions for future research are anticipated.

Aiming at the challenges such as insufficient feature extraction capacity， and low accuracy in the fault diagnosis for asynchronous motors， an asynchronous motor fault diagnosis model based on convolutional neural network （CNN）-Transformer-BiLSTM is proposed. By linking large-scale convolutional kernels with the BiLSTM module to extract the temporal features of fault vibration signals， the model uses a gate mechanism and bidirectional temporal learning mechanism to effectively learn the feature relationships between fault signals at multiple moments. By connecting small-sized convolutional kernels with the Transformer module， the model further increases its ability to extract temporal features. The model uses a multi-head attention mechanism to achieve parallel and efficient processing of feature sequences and outputs a diagnosis result through a Softmax classification. Under 10 dB noise interference， the proposed model was compared with the MSCNN-LSTM-Attention， MSCNN， BiLSTM， and 1DCNN models. The comparison test results show that this model can effectively extract fault features， and the fault diagnosis accuracy rates were increased by 4.3%， 9.7%， 17.2%， and 18.5% respectively， demonstrating that this model has a higher fault accuracy under noise interference.

To solve the problem of accidents caused by damaged crane hooks in current industrial environment， and the low efficiency of crane loading and unloading， an improved YOLOv8n crane hook identification algorithm is proposed. Firstly， AKConv module is introduced to replace the Conv module in the backbone network. This module gives arbitrary parameters and shapes to the convolution kernel， providing rich choices between the convolution cores. Secondly， the ADown downsampling module is embedded in the backbone network， reducing the loss of feature information during the downsampling process. Finally， a CAFMAttention convolution attention fusion module is introduced to enhance the global and local feature extraction of hook recognition. Based on the experimental results， the improved YOLOv8n algorithm increases the precision， recall and mAP50 indicators by 4.6 %、4.2 % and 3.3 % respectively. The improved algorithm enables real time detection of hook positions， assisting operators in timely adjustment and decision-making， avoiding collisions or accidents， thereby improving safety in industrial environments. In addition， automatic hook recognition facilitates faster hook location identification while enabling precise cargo loading and unloading operations， consequently boosting work efficiency.