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.

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.

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.

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 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.

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 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.

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.

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.

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.

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.

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.

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.

The processing channels of integral components such as blisk, integral impeller, and closed impeller of aero-engine are twisted, narrow, and deep, and are prone to various machining interference, which are typical difficult-to-machine components. To improve their machined surface quality, the robotic belt grinding process that is widely used as the last finishing process of the integral component is taken as the research object, and a multi-objective planning method of interference-free machining postures for robotic belt grinding of integral components is presented. With this method, a definition of interference-free robot configuration space (IFRC-Space) is first proposed. Next, an exploration experiment on the evolution law of IFRC-Space along the toolpath when grinding integral components is carried out, which concluded that IFRC-Space varies continuously along the toolpath. Based on the conclusion, a rapid computation method of IFRC-Space is proposed by using the edge detection operator of images. Then, a multi-objective optimization algorithm of grinding postures is advanced based on IFRC-Space. With the algorithm, the indicators including interference avoidance, singularity avoidance, smoothness of grinding postures, and the kinematic performance of the robot can be comprehensively considered, which enables the automatic generation and multi-objective optimization of the interference-free machining postures for robotic belt grinding of integral components. The effectiveness and practicability of the proposed method are verified by toolpath planning experiments for robotic belt grinding of aero-engine blisk and closed impeller.

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.

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.

To overcome the limitations of traditional 2D planar grasping and address the challenge of inaccurate position estimation in existing 6D pose estimation algorithms such as Gen6D， this paper proposes an optimized algorithm， Gen6D-Op. For typical robotic grasping scenarios， the algorithm formulates the position estimation error as a constrained optimization problem based on a collinearity assumption， enabling the precise acquisition of object poses. Building on this high-precision pose， we further design two efficient grasping strategies—Vertical Pose and Planar Projection—to enhance grasping efficiency and stability. Experiments demonstrate that Gen6D-Op significantly improves pose estimation accuracy， reducing the total error by 72.3% to 9.48 mm and achieving a multi-object grasping success rate of 94%. Furthermore， applying the designed grasping strategies effectively reduces the robotic arm's joint angle variation and shortens the grasping time.

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.

Blade vibration and tip clearance are critical parameters reflecting the operational status of aero-engines, containing abundant fault and health information. Real-time monitoring and deep analysis enable fault diagnosis and early warning of engines. This article reviews contact-based measurement methods for blade vibration as well as non-contact measurement techniques such as blade tip timing and tip clearance monitoring. It summarizes important research achievements in related technologies domestically and internationally in recent years, focusing on three main aspects: Types of blade vibration and typical faults, monitoring and identification methods, and fault diagnosis and warning methodes. In particular, the application of these techniques in typical faults such as flutter, surge, and rubbing is emphasized. Finally, the future development trends of aero-engine fault diagnosis and warning technology based on tip monitoring are prospected from five perspectives: high-precision high-speed acquisition, mechanism and evolution path analysis, multi-source fusion testing, fault database optimization, and machine learning-enabled intelligent diagnosis.

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.

Aiming at the problems of insufficient coordinated control and poor adaptability due to the single-arm design of the traditional pork cutting robot， a dual-arm segmentation supple control method based on six-dimensional force sensors is proposed， in which the right arm decomposes the cutting task into the orthogonal subspace of force and position control by constructing a master-slave force-position hybrid control framework， adjusting the cutting force and motion trajectory in real time to ensure the smooth cutting， while the left arm tracks the change of the master arm position and attitude in real time based on the Jacobi matrix mapping and acceleration constraint transfer， to ensure the balance and stability of the two arms when they move together. The simulation results show that the control method has good robustness and flexibility， and is able to accomplish the high-precision pork cutting task.

High-precision localization of unmanned vehicles in complex environments still faces challenges such as insufficient hardware-software coordination and the lack of standardized integration platforms， which restrict the construction of end-to-end data links and cyber-physical closed-loop interactions. To address these issues， this paper proposes a digital-twin-based localization platform architecture for unmanned vehicles. On the hardware side， a multi-source sensing and communication system is developed on the STM32 platform by integrating Global Navigation Satellite System （GNSS）， Ultra-Wideband （UWB）， Inertial Measurement Unit （IMU）， Bluetooth/Wi-Fi， and Narrowband Internet of Things （NB-IoT） modules. These modules are interconnected via high-speed buses and dedicated interfaces， forming the foundation for heterogeneous data acquisition and transmission. On the software side， the platform leverages the industrial Internet identification and resolution system together with digital twin technology to enable bidirectional real-time interaction and closed-loop control between the physical unmanned vehicle and its virtual counterpart. Core functionalities include data management， spatiotemporal localization， path planning， and geofencing. Experimental results demonstrate that the proposed platform ensures ranging accuracy and stability while enhancing system interactivity and integration， thereby providing a comprehensive hardware-software solution for high-precision unmanned vehicle localization.

Currently， wall plastering operations primarily rely on manual labor. Although single-degree-of-freedom rail-guided smoothing plastering equipment has emerged， it still requires human assistance， resulting in low efficiency and high labor costs. To enhance the automation level of wall plastering， a wall-mounted dual-arm plastering robot is designed. The robot adopts a design scheme where dual robotic arms handle spraying and smoothing tasks separately， effectively addressing the issues of uneven spraying thickness and low smoothing efficiency in traditional equipment. The system consists of an autonomous navigation chassis， a concrete mixing tow truck， a dual-arm lifting mechanism， and an adaptive force-controlled smoothing tool. A force-position hybrid control algorithm is proposed， which decouples the force control and position control subspaces through a selection matrix， achieving stable force control and precise position tracking in complex environments. In experimental validation， the robot demonstrates superior flatness and high consistency under various wall conditions， significantly improving construction efficiency and surface quality. Compared to traditional manual methods and single-arm equipment， it respectively enhances operational accuracy and production efficiency by notable margins.

In order to solve the problem that multiple surface processing quality evaluation indexes need to be monitored online at the same time in the actual manufacturing process，a surface roughness recognition and dimensional accuracy prediction method based on RPM and MTL-GAMDenseNet-CA network is proposed. Firstly，the two-dimensional RPM diagram is obtained by two-dimensional conversion of the multi-channel vibration signal by RPM，and then the multi-channel RPM diagram is decomposed and reconstructed by using the two-dimensional discrete wavelet transform. Secondly，the GAM attention mechanism is introduced into the DenseNet model to form a new GAMDenseNet network as the encoder of the model，and then the CA attention mechanism is integrated into the decoder structure，and the MTL-GAMDenseNet-CA multi-task learning network model is constructed by using the hard-parameter sharing network architecture. The gradient normalization algorithm （GradNorm） adaptively adjusts the weight ratio of the two task loss functions of surface roughness recognition and dimensional accuracy prediction，and optimizes the model. Finally，the proposed method is verified by comparative experiments and ablation experiments. The experimental results show that the accuracy of the proposed method reaches 99.88% in the surface roughness recognition task. The mean absolute error （MAE） and root mean square error （RMSE） of the dimensional accuracy prediction task reaches 0.0177 and 0.0221， respectively. This shows that the proposed method can effectively realize the online monitoring of multiple evaluation indexes of surface processing quality.

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.

The new energy vehicles, exemplified by electric vehicles（EVs）, have embraced unprecedented opportunities for development. Lithium-ion batteries（LIBs）, leveraging their advantages such as high energy density, low self-discharge rate, and long lifespan, have emerged as the mainstream choice for EV power systems. However, the significant degradation of LIBs performance at low temperatures directly leads to reduced driving range, prolonged charging time, and potential safety hazards for EVs, posing a core challenge to their widespread adoption. Low-temperature heating, as one of the key methods to overcome the performance bottleneck of batteries at low temperatures, is currently the focus of industry research. This study comprehensively summarizes and discusses the latest advancements in low-temperature heating technologies for LIBs and their current application status in real vehicles, encompassing external heating, internal heating, and hybrid heating. It thoroughly elaborates on the principles, latest progress, strengths, weaknesses, and potential optimization opportunities of each technology. Additionally, it conducts a qualitative comparison of each technology and analyzes the current application status of heating technologies in real vehicles. Finally, the paper explores the future development prospects of low-temperature heating technologies, with an emphasis on key technological breakthroughs and opportunities, providing a comprehensive perspective for the next steps in research and real-vehicle applications of low-temperature heating technologies.

Difficult-to-cut materials are widely used in the aerospace and aviation industries. These materials have characteristics such as high cutting difficulty， high material cost， and difficult calibration experiments. In the finite element simulation modeling process of difficult-to-cut materials， the setting of material mechanical properties and tool chip friction performance， will significantly affect the prediction accuracy of the simulation model. How to achieve efficient acquisition of mechanical property parameters of difficult-to-cut materials， is of great significance to study the rapid and accurate uncertainty calibration strategy of simulation models. Taking the milling process of Ti₂AlNb intermetallic compound as an example， a finite element simulation model uncertainty calibration method for difficult-to-cut materials under the Bayesian framework is proposed. Firstly， an uncertainty analysis of the model is conducted， and a Bayesian based model uncertainties quantification method is proposed. The uncertainty coefficients are solved using the Markov chain Monte Carlo method. Secondly， finite element modeling， simulation experiment design， and simulation dataset construction for milling process are carried out based on finite element simulation software. A surrogate modeling method based on Gaussian process regression and supporting vector regression is proposed. Finally， Ti₂AlNb milling experimental design is carried out， and the working condition dataset is constructed to quantify the JC constitutive parameters and tool chip friction coefficient within the finite element model. The experimental results show that the uncertainty-calibrated finite element simulation model has significantly improved the prediction accuracy， and the relative error in predicting the cutting force of three-dimensional has decreased from 21.47% before calibration to 12.17%.

To address the challenges in object-oriented grasping by robotic arms in unstructured scenarios—such as unknown objects， complex environmental interference， and stringent real-time requirements— this paper proposes a Heatmap-Guided 6-DOF Grasp Detection （HGGD） method that integrates Coordinate Attention （CA） with a Memory-Augmented Neural Network （MANN）， establishing a target-guided grasping model. This approach incorporates a CA module within the pixel encoder. After high-resolution feature map output but before dimensionality reduction， key information is weighted by the CA channel branches， and target regions are located by spatial branches， thereby target perception and location accuracy are enhanced. During feature fusion， MANN is embedded to initialize a memory bank with multi-scale features from the training set. During fusion， read/write heads access historical features and fuse them with current features， updating the memory bank based on confidence thresholds to mitigate information decay and redundancy inherent in traditional fusion. Finally， using the object anchor boxes output by the object detection model， point cloud data undergoes sparsification to reduce computational overhead. The evaluation results based on the GrASPNET-1 billion dataset show that， compared with the benchmark model， the proposed method not only achieves a significant improvement in the crawling success rate index， but also effectively shortens the reasoning time consumption of the target crawling detection task. This solution achieves a coordinated improvement in grasping accuracy and efficiency， providing technical support for the precise grasping of robotic arms in complex real-world scenarios.

The realization of industrialized intelligent construction for steel structures is contingent upon two prerequisites： first， the development of a fully assembled steel structure system that is inherently efficient for repeated disassembly； second， the development of automated assembly robots to address the issues of low efficiency， low precision， and poor quality associated with on-site manual installation. This paper proposes a solution involving an automated assembly robot for the installation of torsion-shear high-strength bolts in Core-tube type steel column joint. Focusing on the assembly process of M16 torsion-shear high-strength bolts， this study emphasizes the mechanism design and structural reliability analysis of the robot end-effector. A hierarchical control system for bolt-hole assembly， based on machine vision， is designed. Simulation experiments demonstrate that the proposed robotic mechanism satisfies the assembly process requirements and significantly enhances the efficiency， precision， quality， and safety of installing torsion-shear high-strength bolts in core-tube column connections.

Aiming at the problems existing in space debris capture under microgravity environment， such as easy fragmentation caused by rigid collisions， low mechanism folding ratio， and poor drive adaptability， this paper proposes a fluid-driven capture mechanism that integrates origami structure and soft robotics technology. This mechanism is designed based on the analysis and optimization of the Youshimura origami structure. It realizes trajectory-controllable folding storage and unfolding wrapping through inner cavity pressure adjustment， and has both high folding ratio （≥1：4） and compliant contact characteristics. To verify the feasibility of the scheme， two types of simulation systems were built： 1） A microgravity contact capture multi-body dynamics simulation based on Abaqus， which analyzes the contact force， wrapping success rate and capture efficiency under different debris shapes； 2） A vision-control integrated system based on CoppeliaSim platform co-simulation， which uses YOLOv5 to realize debris pose recognition and PID control algorithm to generate drive commands， so as to verify the automatic capture accuracy under microgravity disturbance. The simulation results show that the wrapping success rates of the mechanism for cubic debris （55 mm×55 mm×55 mm）， cylindrical debris （Φ50 mm×80 mm） and plate-like debris （400 mm×200 mm×2 mm） are 93%， 80% and 33% respectively， which meets the requirements for compliance and autonomy in microgravity space debris capture.

Aiming at the problems of cumbersome deployment and poor adjustment flexibility associated with QR code navigation， which is widely used in current warehouse robotics， this paper designs a global path planning method based on predefined paths and a secondary positioning and pose adjustment method based on the PL-ICP （Point-to-Line Iterative Closest Point） algorithm. The system utilizes ROS2 （Robot Operating System 2） with 2D LiDAR， IMU， and wheel odometry sensors. The methodology employs the Cartographer laser SLAM algorithm to construct a 2D grid obstacle map and robot localization data files. A self-developed RViz2 plugin is utilized to create navigation map files， upon which the A* algorithm generates global paths. The Navigation2 framework is then implemented for local path planning and motion control. Upon arrival at a designated target point， the robot calculates its pose deviation using laser feature data and corrects its position via a PID control algorithm. Simulation tests conducted on the Gazebo platform demonstrate that， after secondary positioning and pose adjustment， the robot achieves a positional accuracy of ±3 mm in the x and y directions， and a heading angle （θ） accuracy of ±0.1°.

In order to solve the problems of low powder utilization rate caused by poor powder feeding convergence and clogging of powder outlet caused by high-intensity laser cladding in current laser coaxial powder feeding nozzle， the finite element analysis method and orthogonal experimental design method based on the theory of gas-solid two-phase flow movement are adopted in this project. Under the conditions of the determined nozzle structure and environmental parameters， the orthogonal experiments are designed according to the parameters such as the powder concentration in the central axis， the distance between the convergence points， the powder flow concentration distribution per unit distance， and the powder spot diameter， which measure the powder flow convergence ability. The influence laws of the angle between the upper wall of the channel and the axis， the angle between the channel walls， and the outlet width of the particle path， on the gas powder flow distribution are obtained.

Multistable characteristics represent a unique nonlinear mechanical phenomenon. When a system exhibits multistable characteristic, it means that there are multiple stable equilibrium states (states where the energy reaches a local minimum). In these states, even if the system is subjected to small external disturbances, it can maintain its state by itself without energy input. Multistability can exhibit a variety of mechanical characteristics/behaviors, such as negative stiffness, self-balancing, and disturbance resistance, etc. It has gradually shown great application value in fields such as aerospace, smart robot, and biomedicine, and has attracted a large number of researchers in recent years. This research reviews the typical research achievements on multistable mechanisms and structures in recent years. First, the principles, configurations, and design methods of different multistable mechanisms/structures are summarized from the aspects of compliant mechanisms, origami structures, kirigami structures, soft materials, and tensegrity structures; Then, the typical applications of multistable mechanisms/structures in various application scenarios are introduced; Finally, it offers insights into future development trends, aiming to provide new perspectives for enhancing the design and application of multistable mechanisms and structures.

To address multi-source interference issues， including partial occlusion， perspective distortion， large dimensions， and spatial curvature， in machine vision measurement of multi-specification rebar profiles， a machine vision measurement system tailored for the geometric parameters of rebar is designed and constructed. Firstly， a dual-camera near-far vertical layout resolves the conflict between wide field-of-view and high precision. Secondly， an object plane elevation method combined with dual-camera coupling calibration corrects perspective distortions induced by varying rebar specifications. Thirdly， an image preprocessing framework is developed to identify contour regions and locate sub-pixel coordinates， incorporating Bézier interpolation-based occlusion repair. Fourthly， Euclidean clustering-based feature extraction is adopted for deriving geometric parameters. Finally， back-projection registration and geometric scaling converts pixel dimensions to real-world measurements via Zhang’s calibration. Experimental results demonstrated 0.1 mm accuracy validated through manual measurements and 0.24 s per image processing speed， meeting rapid measurement requirements for six rebar profile parameters.

To address key challenges in aviation intelligent manufacturing, such as low data-knowledge collaboration efficiency, difficulties in tracing assembly deviation sources, lagging process parameter optimization, and insufficient virtual-real interactive verification, this study proposes an AI twin control methodology framework with industrial large models as the cognitive engine, and constructs a digital twin closed-loop control framework covering the entire "perception-diagnosis-decision-verification" process. Through networked associative modeling of knowledge graphs, semantic fusion and dynamic reasoning of multi-source heterogeneous data are realized, an industrial large model corpus for aviation manufacturing is established, and a professional knowledge base with autonomous evolution capabilities is formed. An industrial large model algorithm library for multi-scenario intelligent decision closed-loops is developed: Bayesian causal inference is used to analyze multi-level coupled causes of assembly deviations; incremental ensemble learning is integrated to achieve dynamic evolution prediction of multi-source coupled deviations; and transfer reinforcement learning is applied to break through the bottleneck of cross-scenario parameter optimization. Finally, a virtual-real bidirectional driven verification closed-loop is built using digital twin technology. Verification results based on the fuselage panel assembly of a certain type of civil aircraft show that the proposed method significantly improves the automatic assembly accuracy of stringers, with the one-time assembly and adjustment success rate of stringers increased by 24% compared with traditional methods. It also enables real-time inspection of drilling and riveting quality, achieving an accuracy rate of 98% in identifying continuous drilling and riveting defects. By constructing and evolving a domain-specific knowledge base, this study deeply drives the full-process closed-loop from deviation causal tracing to twin verification, and realizes a paradigm shift in manufacturing decision-making from experience-driven to model cognition-driven.

In the production of rubber products， the intensive temperature fluctuations and pressure instability during the mixing and extrusion processes not only adversely affect the mixing quality and the plasticization uniformity of the compound， but also significantly impact equipment energy consumption and production efficiency. To address the issues of large temperature fluctuations and poor pressure stability during rubber compounding and extrusion production， an improved fuzzy PID intelligent cooperative control strategy is proposed. By integrating real-time data acquisition from multiple sensors， including temperature， pressure， rotor speed， and torque sensors， an adaptive parameter tuning mechanism based on process characteristics and a pressure-temperature dynamic coupling compensation model are designed. Through this adaptive adjustment process， precise cooperative control of the mixer rotor and extruder screw is achieved， which deeply analyzes and compensates for the mutual interference between pressure and temperature parameters caused by the strong shear heating of the mixer rotor and the conveying compression process of the extruder screw， thereby overcoming the limitations of single-variable control. Experimental results demonstrate that the improved algorithm reduces the overshoot by 11%， shortens the settling time， and achieves a temperature control accuracy of ±0.8 ℃ in the temperature control process. The interference of pressure fluctuations on temperature is reduced from 35% to 12.7%. This system enhances temperature control precision， reduces pressure fluctuations， and significantly improves product uniformity and production efficiency.

Aiming at the collaborative optimization problem of flexible job shop scheduling and AGV material handling in intelligent factory under multi-variety and small-batch production mode， a joint scheduling method of production and handling based on Dueling Double Deep Q-Network （D3QN） is designed to minimize the maximum completion time， and the conflict-free path planning of AGV is realized by combining the A * algorithm with time window. The design algorithm is compared with a variety of rule scheduling methods， and a variety of different scale examples are designed for experimental verification. The results show that the scheduling performance of D3QN algorithm is better， and it has good optimization effect and generalization ability. At the same time， the influence of the number of AGVs on the scheduling performance is analyzed， and finally it is concluded that it conforms to the law of diminishing marginal benefit.

Path planning is an important part of the process when an unmanned aerial vehicle （UAV） executes tasks， and it is also a crucial guarantee for the successful completion of tasks by the UAV. In this paper， the three-dimensional environment is modeled to construct a cost function for UAV path planning. Based on the analysis of the Elk Herd Optimizer （EHO） algorithm， an improved algorithm is proposed to solve the UAV path planning problem. The improved algorithm （Improved Elk Herd Optimizer， IEHO） initializes the population distribution through the Logistic chaotic mapping to enhance the diversity of the population. An S-shaped transfer function is utilized to adaptively and dynamically adjust the ratio of male elks， so as to balance the abilities of exploration and exploitation. Additionally， Gaussian mutation is introduced to perturb the position update of young elks， aiming to improve the search efficiency. Furthermore， the Lévy flight strategy is integrated to prevent the population from falling into a local optimum. Meanwhile， the numerical solution of the obtained path planning is smoothed by using the Bézier curve. The IEHO algorithm is compared with the EHO algorithm and five other algorithms through the cec2017 test functions， which verifies the effectiveness of the proposed algorithm. Finally， tests were conducted under two different environmental models. The results demonstrate that IEHO outperforms EHO， DBO， and COA in UAV path planning， exhibiting superior convergence speed and path optimization capabilities.

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.