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.

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.

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.

As a representative of innovative energy storage devices, lithium-ion batteries have been widely used due to their excellent performance and environmentally friendly properties. However, the long charging time caused by slow charging and the degradation caused by fast charging remain critical issues that hinder the further promotion and development of lithium-ion batteries. To this end, the design of the fast charging strategies of lithium-ion batteries has become a hot research topic recently. To summarize the research progress, a systematic review of current research on this topic is presented from three aspects： formulation of the charging problem, establishment of battery models, and design of charging methods, all core elements in the fast charging strategy design. First, the research background of the fast charging strategy design is introduced. Specifically, how to set the optimization objectives, constraints, and design variables of the design problem is investigated. Second, the internal mechanisms of lithium-ion batteries and some commonly used battery models are briefly described, and the modeling methods that incorporate machine learning are also summarized. Additionally, various existing charging methods are especially analyzed and classified based on their characteristics. Moreover, based on the current research status, some future directions are given, aiming to offer researchers a valuable opportunity to design more efficient and user-friendly fast charging strategies.

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 solve the problems of small molding size and low printing efficiency of traditional desktop-level single-nozzle FDM 3D printer， a medium-sized FDM multi-nozzles collaborative 3D printer is designed and built. The printer adopts a Cartesian （XYZ） structure and is equipped with three side-by-side composite printing nozzles， and the materials can be selectively extruded according to the demand. The control system is divided into three parts according to the functions： main motion control module，embedded auxiliary measurement and control module and upper computer software module，while the software and hardware of these three parts are developed.Two printing modes of multi-nozzles synchronous forming and multi-nozzles stackable co-filling are designed and the corresponding path planning algorithms are proposed.After printing verification， compared with single-nozzle printing， the synchronous forming efficiency of the composite multi-nozzles printer is increased by 3 times， whereas the stackable co-filling printing time is reduced by 41%. The printing efficiency is significantly improved under the premise of ensuring the printing quality.

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.

The development trend of high thrust to weight ratio in aviation engines has driven the demand for high-quality machining of turbine blade film cooling holes. On the one hand, ultrafast laser has excellent performance in micro hole machining such as no recast layer and low hole wall roughness due to its approximate “cold machining” properties, and on the other hand, it is widely used in micro and nano machining due to its characteristics of no material selectivity, non-contact, and flexible machining. Therefore, ultrafast laser machining of film cooling holes has become a research hotspot. However, as a typical thin-walled cavity type component, the problem of ablation damage caused by laser penetrating the blade and irradiating the back wall is always difficult to avoid, and the back wall protection technology is also increasingly valued. This article is based on the ultrafast laser processing of film cooling holes. Firstly, it summarizes the ablation mechanism and simulation cases of ultrafast laser micro hole processing, and points out that simulation and experimental comparison constitute the general research route of laser processing; Furthermore, the process routes and specific processing effects of ultrafast laser film cooling hole processing are compared from three aspects: laser parameters, scanning methods, and processing steps; On this basis, the important role of key technologies such as material filling and process control in the processing of high-quality film cooling holes for back wall protection is further summarized. The performance requirements for filling, extraction, and protection of filling materials are clarified, and the feasible ideas and auxiliary positions of process control are determined. Finally, the key goals of ultrafast laser processing research and the systematic and collaborative development trend of back wall protection are pointed out, laying a foundation for the actual processing of film cooling holes.

In the industrial software ecosystem， Computer-Aided Design（CAD） interfaces play a pivotal role. This study outlines the composition and collaborative mechanisms of the industrial software ecosystem， reviews the evolutionary trajectory of CAD interface technologies， and summarizes their core roles within the ecosystem from the perspectives of data transmission， functional integration， and innovation-driven development.Building on this foundation， an in-depth analysis of the application bottlenecks and challenges faced by CAD interfaces is conducted， including data interface standards， the depth of system integration， and the convergence with emerging technologies. Furthermore，future development trends for CAD interfaces are explored， emphasizing key directions such as data standardization and semantic enrichment， multi-user collaborative design with real-time interaction， and the deep integration of artificial intelligence technologies. This work aims to provide theoretical insights and practical guidance for the research and application of CAD interfaces within the industrial software ecosystem.

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 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.

To solve the dynamic job shop scheduling problem in scenarios with variable job and machine quantities， a solution approach called Dense-D3QN， combining DenseNet， a densely connected convolutional network， with Dueling Double Deep Q-Learning （D3QN） is proposed. The disjunctive graph model is utilized to construct a single-objective job shop scheduling model aiming to minimize the maximum processing time， representing the scheduling state in the form of multi-dimensional matrices while designing a dense-sparse reward function. To validate the effectiveness of the proposed algorithm， both public benchmarks and real data are used to construct common and actual scheduling environments. The Dense-D3QN model is trained and tested in the common environment. In the actual environment， the Dense-D3QN model is trained and tested in both static and dynamic settings. The experimental results demonstrate that the Dense-D3QN model is more capable of handling dynamic job shop scheduling problems with variable scales.

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.

Micro coaxial unmanned aerial vehicles perform well in various complex environments due to their unique structure and performance advantages, especially in executing tasks with high complexity and confined spaces, such as military reconnaissance, disaster rescue, and other fields. Therefore, the research status and progress of the control mechanism and flight control algorithm of micro high-mobility coaxial UAV are reviewed. In terms of the manipulation mechanism, the attitude adjustment principle and design characteristics of the manipulation mechanism, such as tilt disk, center of gravity offset, lower rudder blade, motor cycle control and electromagnetic coil drive, are introduced, and their size parameters and main characteristics are compared. In terms of flight control algorithms, the principles and applications of traditional control methods and advanced control methods are expounded. Finally, the development characteristics and future trends of micro-UAV are analyzed, and the future trends are prospected, and it is pointed out that the integration and cooperation of multiple manipulation systems will be the future development direction to meet the increasingly diverse and complex task requirements.

To address the need for both independent control of Continuous Damping Control （CDC） dampers and coordinated control of the entire vehicle semi-active suspension system， while also improving upon the issues present in traditional semi-active suspension controller software design such as challenges in meeting real-time requirements and low CPU utilization in bare-metal development environments， this study proposes an innovative approach. Initially， the study establishes separate models for the seven-degree-of-freedom semi-active suspension system and the forward-inverse models of CDC dampers. Building upon the skyhook control strategy， the study integrates a vehicle-coordinated parallel fuzzy controller based on the Mamdani fuzzy control method. Subsequently， by transplanting the FreeRTOS-SMP multicore real-time operating system and utilizing the Infineon AURIX series 32-bit triple-core microcontroller TC275 as the main control chip， the study designs the software and hardware system for the CDC damper control unit. Furthermore， the study conducts task scheduling verification of the multicore real-time operating system and validates the effectiveness of the designed control unit and proposed strategy through hardware-in-the-loop testing using typical random road surfaces to demonstrate the improvement in overall ride comfort of the vehicle.

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.

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 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.

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.

In the process of numerical control programming for complex structural components, the difficulty in reusing machining process knowledge arises due to the heterogeneity of knowledge sources and the complexity of interconnections between knowledge. A knowledge recommendation method for structural parts machining process based on a large language model is proposed. By selecting and fine-tuning the large language model, a vertical domain model of machining process knowledge recommendation for structural parts is established. The evaluation results indicate that the model can recommend corresponding machining processes based on specific part features. To solve the problem of the model not being able to obtain the latest professional knowledge and the low accuracy of machining process recommendations, the LangChain application framework combined with a knowledge base is used to enhance the knowledge retrieval of the domain model and construct a process knowledge question answering system. Through corresponding indicator evaluation, the F1 value of the question answering system improves by 0.026 on the basis of the original domain model, and the accuracy of machining process recommendations is above 90%. In the process decision-making application of CNC programming for aviation structural components, this method recommends corresponding process knowledge based on part features. Compared with the automatic CNC programming system that does not use the method in this article, the efficiency of generating CNC codes for frame type structural components improves to a certain extent, which is of great significance for improving the decision-making efficiency of CNC programmers.

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.

At present， close range manual handles are still used at many bridge crane workplaces. To address the potential safety risks faced by operators， a remote path planning bridge crane experimental device is designed to improve the stability and gripping efficiency of the bridge crane and determine the mechanical structure scheme of the device； By studying the working characteristics of the experimental platform in three-dimensional space， the three-dimensional path planning of the device is designed； the ant colony algorithm is improved by storing pheromones on path nodes， and a search method that combines layer by layer advancement with grid plane method is used， optimizing path nodes using pruning algorithm， and updating pheromones using a combination of global and local path planning. Through the above improvement strategies， the improved ant colony algorithm obtained through simulation in MATLAB software has 3 fewer iterations， 46 fewer inflection points， 22.6874 s shorter algorithm time， and 4.4043 units shorter shortest path compared to the traditional ant colony algorithm. Finally， the operating system of the experimental platform is designed， and an experimental prototype is built. The results show that the device meets the actual work requirements， verifying the feasibility and effectiveness of improving ant colony algorithm.

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.

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.

The quality of screw tightening greatly affects the safety of mechanical products， whereas traditional diagnosis approaches are time-consuming and imprecise， and the implementation of effective fault diagnosis， therefore， bears significant engineering value. In this paper， an innovative method of fault diagnosis for screw-tightening based on LSTM and Expert knowledge is proposed. Firstly， tightening process curve under specific failure mode was studied and several expert knowledge rules were established. Secondly， a data pre-processing algorithm was established based on the characteristics of sequential data such as noise clipping， stage segmentation， fitting and sampling to improve the quality of data. After that， the feature vector obtained through LSTM was used as the input of the expert knowledge model to obtain the expert knowledge vector， and the two vectors were combined as the input of the classifier. Finally， compared with SVM and LSTM， the results show that the method has higher diagnostic accuracy in multiple failure modes.

Facing the requirements for diverse starting and ending positions as well as velocity in the field of robotic motion control， this paper proposes a novel universal S-curve velocity planning algorithm aiming at adapting to arbitrarily specified starting and ending positions and velocity conditions. The paper first introduces the velocity-to-velocity S-curve velocity planning algorithm， then elaborates on the general 7-segment S-curve velocity planning algorithm. Based on this， a more versatile S-curve velocity planning algorithm is proposed. For different input parameters， this paper classifies the S-curve （s-t curve） into ten types and provides detailed segmented planning strategies for each type. Through simulation tests， it is verified that the algorithm not only excels in efficiency but also demonstrates outstanding performance in the smoothness and positional accuracy of the planned curve. Furthermore， tests conducted on actual robotic platforms further confirm that this algorithm can effectively reduce shocks and vibrations during robotic operations， significantly enhancing the operational performance of robots while demonstrating good practicality and broad application prospects.

Aiming at the pain point of slow traditional rescue response in water areas， this paper proposes a structural design scheme of an amphibious rescue equipment that integrates the functions of unmanned aerial vehicles （UAVs） and rescue boats. The UAV adopts a lightweight fuselage and NACA4412 airfoil aerodynamic design. Through the rotation of wings and elescopic mechanism of the counter-rotating propellers， a rapid cross-medium form switching can be achieved. Meanwhile， the dynamic performance is analyzed by establishing the mathematical model and state-space model of the drone， and a fuzzy PID controller is designed. MATLAB/Simulink is used to carry out dynamic response simulation verification for the mathematical model of the drone and the designed fuzzy PID controller. The results show that when the input is a square wave and a step signal， the fuzzy PID controller designed in this paper has a faster response speed and better stability compared with the traditional PID controller.

The bionic leg landing gear system based on multilink configuration was designed to solve the problems of low intelligence and poor adaptability of traditional unmanned helicopter landing gear systems when landing on complex terrains, as an effective supplement to the original fixed landing gear. Based on the analysis of application scenario requirements, this article provides the design concept and overall configuration of a bionic leg landing gear, and conducts research on system integration and verification technology on this basis. Firstly, based on the six-leg design scheme, the structural design and drive/control system design method of the bionic leg landing gear are given. Then, for a certain type of unmanned helicopter for verification, a physical prototype of the bionic leg landing gear was constructed, and the collaborative control and fusion design method of the flight control-leg control-terrain recognition system was proposed. Finally, based on this prototype, the tests that the whole machine vibration characteristics test and ground resonance analysis, carrying capacity test in laboratory, field flight landing verification are completed. These researches indicate that, the multilink bionic leg landing gear can achieve a lightweight design that accounts for no more than 25% of the maximum takeoff weight, and can land on unstructured terrain with no more than 200 mm undulations. And through its drive/control design method, the buffering of the landing gear landing process and the stability control of the fuselage are effectively realized. Compared with the traditional landing gear, this kind of landing gear has the advantages of fordable deployment, landing attitude adjustment, and complex terrain adaptation.

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.

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.

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.

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.

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.

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.

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.

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.