This paper presents a novel dynamics-based human–robot collaboration (HRC) control method with a remote center-of-motion (RCM) constraint. The existing works rely on prescribed main task trajectories and regard the RCM constraint as a secondary task, making them inapplicable in the fully interactive mode under HRC. Our work imposes a virtual RCM constraint on the interactive HRC process so that the

To address the challenges of openness and development efficiency in heterogeneous robotic systems for agile manufacturing, this paper proposes a novel development framework leveraging Data Distribution Service (DDS) orchestration. This framework is designed to enhance system openness, streamline development, and ensure high-performance communication critical for manufacturing agility. Firstly, the

Accurate monitoring of tool wear states and wear values is crucial for reducing machine tool failures and ensuring machining accuracy and efficiency. However, wear monitoring faces significant challenges due to the imbalance of wear samples and the dynamic changes in the coupling relationships among multi-source sensing signals. Additionally, varying processing conditions further complicate the accurate

Worker resources are crucial in aircraft final assembly lines (AFAL), which are characterized by extensive manual assembly tasks. The features of AFAL, including resource constraints, makespan balancing, and flexibility in resource allocation, present greater challenges than conventional scheduling problems. This paper addresses the joint optimization problem of worker allocation under a resource dedication

This paper proposes a novel framework for human-robot collaboration (HRC) that addresses the critical need for robots to effectively collaborate with humans on shared tasks within unstructured and dynamic environments. While prior research focused on safety-related aspects, such as collision avoidance in shared workspaces, the task-oriented aspects of human-robot collaboration remain largely underexplored

In the transformative landscape of Industry 4.0 and the impending transition to Industry 5.0, the paradigm of collaborative robotics is emerging as a cornerstone, combining human and robotic distinctive abilities. This intersection is leading to a new era of 'human-centric' manufacturing, where the integration of human with robots is not just an option, but a need. In particular, the shift towards

Advances in industry, technology, and external factors like market demands have created new manufacturing challenges. In response, there has been an increase in the use of mobile manipulators, consisting of a robotic arm mounted on a mobile robot. These systems can be suitable for manufacturing operations. However, they still fall short of the precision required for high-performing industrial applications

In the context of the automotive manufacturing industry, complexity and the extensive data generated during production pose significant challenges. With ongoing technological advancements, effectively harnessing and analyzing this data has become increasingly critical. Machine learning (ML) and deep learning (DL) have emerged as powerful tools to manage complexity and leverage data for enhanced decision-making

The presence of noise or other abnormal points in the measured point clouds of the turbine blade can result in local discontinuities of the tool path curves, fitted by the machining path points generated by slicing the measured point cloud. In addition, the fluctuation exists in the tool orientation vectors corresponding to the cutter-contact (CC) points in the toolpath curves. These issues can lead

Robotic machining and automatic offline programming has been developing rapidly over the last decade, yet the absolute accuracy of industrial robots significantly impacts the processing performance, limiting their application in high-precision manufacturing fields. For dynamic non-contact continuous robotic machining tasks, such as laser cutting, precise trajectory tracking performance is especially

Accurate kinematic parameters are crucial for deploying industrial robots in high-precision manufacturing applications, such as machining workpieces. Traditional kinematic identification methods often assume that nominal parameter values are known and used as initial estimates. However, in industrial applications, obtaining such nominal values is challenging due to the limited access to detailed design

Driven by technological advancements and the increasing need for efficiency and customization, the manufacturing industry is shifting towards Shared Manufacturing. This strategy enhances global production flexibility and resource utilization by enabling diverse entities to collaboratively engage in distributed manufacturing activities. Expanded resource sharing across industries and society, along

Tool wear condition monitoring is essential for reducing production costs and improving machining precision, serving as a key strategy for achieving machine tool intelligence. However, existing methods often depend on empirically designed complex networks to achieve high recognition accuracy, which results in high computational costs, poor performance during later wear stages, and limited interpretability

In the Industry 5.0 context, ensuring effective Human-Robot Interaction (HRI) is key to supporting human involvement in production processes. Interfaces are the foundation of this collaboration and serve as vital communication channels which bridge the gap between users and robotic systems. This study compares unimodal and multimodal interfaces and their impact on user experience (UX) in an HRI context

Cloud manufacturing service composition reconfiguration (CMSCR) is an essential process for handling unpredictable service exceptions to ensure the smooth operation of the cloud manufacturing (CMfg) system in a dynamic environment. Considering the occupied status of service providers of a CMfg system (CMSPs) at the time of change occurrence, the reconfiguration can be organized only with the available

Wire arc additive manufacturing is a promising technology but is still limited by insufficient manufacturing accuracy. Despite numerous studies on process parameters to enhance manufacturing precision, the errors introduced by robot in hybrid manufacturing systems have not been effectively addressed. Unique on-site conditions such as varying robot poses and large working spaces have rendered many previous

In discrete manufacturing workshop where disturbances occur frequently, the dynamic scheduling problem that considers transportation resource constraint is complex and challenging. Additionally, rescheduling without evaluating the impact of disturbances may adversely affect production stability of workshop. To address these issues, this paper proposes a dynamic scheduling framework based on digital

Human-Robot Collaboration (HRC) and Digital Twins (DT) have significantly advanced industrial development and digital transformation. Human representations and models are essential in Industry 5.0, where human-centric is one of the key features. Despite the growing interest in human models for HRC and DT, a comprehensive overview of these models and enabling technologies currently needs to be provided

The increasing volume of electronic Waste from Electrical and Electronic Equipment (WEEE) presents significant environmental and economic challenges. Efficient recycling requires the disassembly of electronic devices, a process that is currently labor-intensive and costly. In this paper, we present a robotic system designed to automate the disassembly process, focusing on the task of unscrewing fasteners

Six-Degree-of-Freedom (6D) pose estimation is essential for robotic manipulation tasks, especially in human-robot collaboration environments. Recently, 6D pose estimation has been extended from seen objects to novel objects due to the frequent encounters with unfamiliar items in real-life scenarios. This paper presents a three-stage pipeline for 6D pose estimation of previously unseen objects, leveraging

With the growing demand for personalized production, multi-agent technology has been introduced to facilitate rapid self-organizing production execution. The application of communication protocols and dynamic scheduling algorithms supports multi-agent negotiation and real-time scheduling decisions in response to conventional production events. To address machine failures, real-time response strategies

Industrial robots have been widely used in many manufacturing scenarios, including curved surface with complicated geometric features, due to their flexible capability of posture adaptation and large machining range. This work utilizes the multi-solution feasible space characteristic of robot to construct a novel milling posture active adjustment model under multi-source heterogeneous constraints.

The growing demand for flexible production systems is driven by product diversity and fluctuating order volumes. Seasonal variations can lead to imbalances between available machines and order demands, making efficient resource configuration critical before production begins. This paper addresses the optimization of machine configuration and scheduling in the hybrid flow shop, incorporating the order

Dynamic accuracy is significant for industrial robot in application. To realize this, many methods such as increasing damping, changing materials, and optimizing structures are developed and applied to the robots with relative low load (thousands of Newtons). However, for the application of heavy load (millions of Newtons), the dynamic error generation mechanism is different, these methods are neither

The advancement of the manufacturing system towards more human-centric, emphasising not only efficiency but also the well-being of workers. However, task planning in human–robot collaborative assembly (HRCA) remains challenging, when considering the physical exertion alleviation of workers, due to the complexities of physical exertion estimation and variations in human assembly operations. Different

Surface morphology has a considerable impact on the assembly of high-precision mechanical products. To address this issue, an adapted Skin Model Shapes modeling method for on-site assembly is employed. Meanwhile, the computational time would drastically expand as the exponential growth of measurement data hlfaced the increase in workpiece size. Thus, a fast surface mating status analysis method is

With the continuous advancement of smart manufacturing technologies, traditional machine tool production is evolving toward greater integration and intelligence, particularly in addressing multi-objective optimization challenges such as energy efficiency, operational effectiveness, and noise reduction. To optimize machine tool performance under dynamically changing milling process parameters, a multi-objective

Robotic welding systems are pivotal in various manufacturing sectors, such as aerospace, construction, automotive, and maritime industries, due to their ability to operate in challenging environments with fewer physical constraints compared to human welders. However, their lack of process knowledge and adaptability necessitates heavy reliance on experienced technicians for process planning. To mitigate

Assembly complexity in manual processes has been widely addressed over the years in manufacturing-related literature. The concept of complexity indeed is linked to the cognitive and physical effort required on behalf of the human operator in completing the assembly process and is directly linked to the occurrence of process failures and inefficiencies. In the light of the introduction of novel technologies

With the rapid advancement of robotics technology, robotic welding has become essential for improving the welding efficiency of large-scale complex components, while reducing the workload of welders. However, the large size and intricate weld structures present significant challenges in obtaining comprehensive point cloud and accurate welding paths, which further hinders the development and application

Industrial robots (IRs) serve as critical equipment in advanced manufacturing systems. Building high-fidelity digital twin models of IRs is essential for various applications like precision simulation, and intelligent operation and maintenance. Despite technological potentials of digital twins, existing modeling methods for industrial robot digital twins (IRDTs) predominantly focus on isolated domains

Multi-fingered dexterous hands hold significant potential for addressing manipulation tasks in intelligent manufacturing, owing to their inherent anthropomorphic flexibility and rich perceptual capabilities. However, the functionality of dexterous hands in intelligent manufacturing remains unclear due to current technological limitations. This paper provides a review of the current research status

Force control plays a critical role in the robotic polishing process. Traditional force control methods primarily emphasize regulating the precise contact force value while paying limited attention to the distribution of force within the contact surface. Drawing inspiration from manual polishing techniques that employ tactile sensing, this study introduces an innovative robotic adaptive polishing system

In contemporary intelligent manufacturing environments, scalability and flexibility have emerged as critical requirements for human-robot collaboration (HRC) systems. As the cornerstone of effective HRC implementations, human activity recognition (HAR) is indispensable for accurate understanding of human operational intent and improving HRC in dynamic industrial environments. Recently, graph convolutional

This article presents a layout scenario of parallel multiple tracks shuttle loop system and its scheduling method within an automated storage and retrieval system. Leveraging the motion mathematical model articulated through angular coordinates and track coding, we established a multi-agent simulation environment. The simulation encompasses RGV agents, task assignment agents, track allocation agents

Continuum robots, with their slender configuration and high redundancy, gain increasing interest in industrial applications such as intervention within confined spaces. However, when the robot end effector is required to travel a long distance, the existing products need a large actuation pack and complicated control strategy for a decent accuracy. This paper presents a continuum robot with a novel

In discrete manufacturing shops, dynamic uncertainty disturbances necessitate frequent scheduling and simulation, posing significant challenges to the efficiency of traditional methods. Therefore, effective production execution logic models are required to manage these dynamics and enhance the efficiency of scheduling and simulation. However, existing production execution logic models lack comprehensive

Accurate analysis of the dynamic characteristics of a mobile industrial robot (MIR) is essential for evaluating and enhancing its machining performance. In the case of mobile heavy-load milling industrial robots, the significant weight of the end-effector and periodic external excitations highlight the flexibility of joints and links. This flexibility considerably affects the vibration characteristics

The dynamic accuracy of industrial robots is significantly influenced by the elastic drive trains of the axes. Their behavior is composed of the coupled dynamics of drive control and gear mechanics. As an undesirable consequence, increasing elasticity leads to growing tracking errors. One approach to reduce tracking errors is semi-active damping. The functional principle is based on damping the gear

The rapid advancement of Industry 4.0 has transformed manufacturing, giving rise to Flexible Smart Manufacturing Systems (FSMS) capable of adapting to fluctuating market demands and operational uncertainties—essential for achieving mass customization. However, conventional approaches that separate long-term planning from real-time scheduling struggle to meet the demands of modern manufacturing environments

Manufacturing systems have witnessed a significant transformation with the introduction of Industry 4.0, introducing new capabilities with the emergence of new technologies. One such instance is the proliferation of sensors enabling the generation and acquisition of vast amounts of data, leading to advancements in Artificial Intelligence (AI) for manufacturing. One field profiting from this is that

In response to the growing demand for intelligence and digitalization in computer numerical control (CNC) systems, particularly in virtual debugging, performance evaluation, and machining quality optimization within the manufacturing sector, this study explores the mapping process from physical entities in the physical space to twin entities in the digital twin space. It further delves into the data

Collaborative robotics cells leverage heterogeneous agents to provide agile production solutions. Effective coordination is essential to prevent inefficiencies and risks for human operators working alongside robots. This paper proposes a human-aware task allocation and scheduling model based on Mixed Integer Nonlinear Programming to optimize efficiency and safety starting from the task planning stages

Time-Series Forecasting (TSF) is a growing research area across various domains including manufacturing. Manufacturing can benefit from Artificial Intelligence (AI) and Machine Learning (ML) innovations for TSF tasks. Although numerous TSF algorithms have been developed and proposed over the past decades, the critical validation and experimental evaluation of the algorithms hold substantial value for

Manufacturing service composition (MSC) is a fundamental component of cloud manufacturing that involves multiple stakeholders and associated services. Stakeholders, viewed as autonomous entities, prioritize both temporary gains and long-term benefits, while service interactions significantly influence the feasibility and quality of MSCs. Effective MSC allocation demands a strategic approach that harmonizes

Knowledge graph (KG) is a method for managing multi-source heterogeneous data and forming knowledge for reasoning using graph structure. It has been extensively utilized in manufacturing systems to promote the advancement of intelligent manufacturing. In non-stationary manufacturing systems, the machining performance of individual elements demonstrates variability and dynamic fluctuations. The significant

Advanced human-robot interaction (HRI) is essential for the next-generation human-centric manufacturing mode such as “Industry 5.0”. Despite recent mutual cognitive approaches can enhance the understanding and collaboration between humans and robots, these methods often rely on predefined rules and are limited in adapting to new tasks or changes of the working environment. These limitations can hinder

Robotic welding envisioned for the future of factories will promote high-demanding and customised tasks with overall higher productivity and quality. Within the context, robotic welding parameter prediction is essential for maintaining high standards of quality, efficiency, safety, and cost-effectiveness in smart manufacturing. However, data acquisition of welding process parameters is limited by process

Single peg-in-hole assembly of small objects has been researched extensively. However, these studies limit applicability to multiple peg-in-hole assembly of large objects, due to the complex contact state, and the large size and weight of the objects. To address these challenges, this paper proposes a dual-arm cooperative multiple peg-in-hole assembly framework (DAC-MPiH) for large objects, leveraging

The deployment of smart sensors in confined spaces presents significant challenges due to limited visibility, environmental constraints, and communication interference. This study introduces a novel integration of digital twin technology with robotics to address these challenges, enabling precise and reliable sensor deployment in complex environments such as steel box girders. The proposed system leverages

Hybrid machine tools (HMTs), known for their fast response speed, high stiffness, and accuracy, have found wide applications in aerospace and other industries. However, maintaining stability and durability during high-speed machining necessitates careful feedrate scheduling. This study introduces a novel feedrate scheduling method for 3-prismatic-revolute-spherical (3-PRS) HMTs, ensuring that the velocities

The intricate surfaces of irregular structure workpieces present significant challenges for robotic welding path planning. To facilitate robot welding without teaching and programming, this paper proposes a weld seam extraction method that integrates semantic segmentation with point cloud features. This approach effectively harnesses the RGB-D information captured by an area array structured light

Customized manufacturing systems represent a promising production paradigm capable of producing a variety of products to meet diverse customer needs. However, limited resources and complex processes complicate the optimization of production scheduling and resource allocation. In particular, fixture shortages frequently arise in a highly customized manufacturing enterprise, as multiple new jobs may

A 2D laser profiler is commonly utilized in high-precision robotic settings to capture detailed surface profiles for 3D scanning. By collecting and combining numerous such measurements from different viewpoints, it is possible to assemble a comprehensive 3D map. However, to effectively merge these individual 2D profiles into a singular global framework, the spatial relationship between the scanners

Five-axis continuous inspection is an innovative technology that allows for the efficient and precise inspection of freeform surfaces. Traditional methods for planning the five-axis inspection path rely on manually defined objective functions, which are heavily dependent on the expertise of professionals and often result in suboptimal paths. To overcome these challenges, we have developed a Reinforcement

The integration of digital twin technology into the assembly process of complex precision mechanical products has become a significant and feasible means to improve product assembly quality and consistency by performing dynamic assembly precision prediction and henceforth assembly process optimization. Most current research predominantly focuses on modeling the actual machining error of components

In manufacturing, automating the generation of dynamic trajectories for diverse robots and loads in response to kinematic task requirements presents a significant challenge. Previous research has primarily addressed kinematic trajectory generation and dynamic motion planning as separate endeavors, with integrated solutions rarely explored. This paper presents a novel methodology that combines reinforcement

The mass production of products in recent decades has led to the excessive exploitation of global resources and environmental degradation. Researchers tackle this challenge by proposing methods for reusing end-of-life products, including remanufacturing strategies. On the other hand, today's consumers seek products that completely fulfill their needs. For this reason, leading manufacturers prioritize

Intelligent robotic assembly is becoming a pivotal component of the manufacturing sector, driven by growing demands for flexibility, sustainability, and resilience. Robots in manufacturing environments need perception, decision-making, and manipulation skills to support the flexible production of diverse products. However, traditional robotic assembly systems typically rely on time-consuming training

Serial industrial robots, as a great potential alternative to computer numerical control (CNC) machine tools, have attracted numerous attention, relying on their large workspace and low cost. However, a detailed and specific guidance is still missed to solve the problem of poor milling performance caused by their weak stiffness when facing milling tasks with high material removal rates (MRR). Combined