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Featured Application: Robotic tabletop manipulation of objects using commands in natural language. This paper outlines a conceptual design for a multi-level natural language-based planning system and describes a demonstrator. The main goal of the demonstrator is to serve as a proof-of-concept by accomplishing end-to-end execution in a real-world environment, and showing a novel way of interfacing an LLM-based planner with open-set semantic maps. The target use-case is executing sequences of tabletop pick-and-place operations using an industrial robot arm and RGB-D camera. The demonstrator processes unstructured user prompts, produces high-level action plans, queries a map for object positions and grasp poses using open-set semantics, then uses the resulting outputs to parametrize and execute a sequence of action primitives. In this paper, the overall system structure, high-level planning using language models, low-level planning through action and motion primitives, as well as the implementation of two different environment modeling schemes—2.5 or fully 3-dimensional—are described in detail. The impacts of quantizing image embeddings on object recall are assessed and high-level planner performance is evaluated using a small reference scene data set. We observe that, for the simple constrained test command data set, the high-level planner is able to achieve a total success rate of 96.40%, while the semantic maps exhibit maximum recall rates of 94.69% and 92.29% for the 2.5d and 3d versions, respectively. [ABSTRACT FROM AUTHOR]

The editorial discusses the advancements and future directions in autonomous systems for cyber-physical systems and smart industry, highlighting the critical role of these systems in improving efficiency, flexibility, and sustainability in various industrial sectors. The document presents seven contributions that explore innovations and challenges in autonomous systems, covering areas such as real-time production scheduling, process control in Industry 4.0, environmental sensing automation, human-robot collaboration, industrial IoT optimization, and traffic modeling for autonomous vehicles. These contributions emphasize the transformative potential of autonomous systems in optimizing industrial processes and enhancing decision-making in smart environments, while also addressing technical challenges that need further exploration. [Extracted from the article]

Technological progress has driven the integration of new technologies in the field of industrial automation, but a structured framework is often lacking to efficiently guide the transition from traditional industries. This article presents the implementation of advanced technologies on FESTO's (MPS-500) modular production system, using the reference architectural model for Industry 4.0 (RAMI 4.0) as a guide for scaling. It highlights the importance of the synergy between information technologies (ITs), which enables the development of a multi-level processing system. This system performs concurrent tasks, thus managing execution and manufacturing through an MES based on requests from the cloud. On the other hand, at a lower level, a fog computing system was integrated, which relieves the processing load by distributing processes locally. In addition, matrix mapping was performed to map the integrated technologies within the context of a reference model, allowing a clear alignment between the different levels of the system. The results show a significant reduction in waiting times between batches and operations, which directly improves productivity and offers greater flexibility, that is crucial for SMEs during their growth and scaling process towards Industry 4.0. [ABSTRACT FROM AUTHOR]

With the development of Industry 4.0, collaborative mobile robots are becoming increasingly prevalent in industrial settings, raising important safety considerations in human–robot interaction environments. This study examines the safety issues in collaborative mobile robotics through a case study of a smart factory utilizing quadruped robots. This research aims to contribute to the development of safety management strategies by identifying potential risk factors and analyzing the differences in risk perception among stakeholders. A survey was conducted among 93 operators in the factory to identify the main risk factors, followed by a Delphi study with four groups of experts: robot operators, safety management experts, robot developers, and academic experts. The Kruskal–Wallis and Mann–Whitney U tests were used to analyze the statistical significance of differences in perception between the groups. The results showed that collision and deviation from the path were the most concerning risk factors. Significant differences were found in the perceptions of several hazards between expert groups, with academic experts rating most hazards highly while robot developers rated them relatively low. The findings highlight how background knowledge and experience influence risk perception in collaborative robotics. These varying perspectives should be considered when developing safety management strategies for mobile robots in industrial settings, suggesting the importance of multi-stakeholder collaboration and targeted educational programs. [ABSTRACT FROM AUTHOR]

In the face of rapidly evolving cyber threats, network-based intrusion detection systems (NIDS) have become critical to the security of industrial and robotic systems. This survey explores the specialized requirements, advancements, and challenges unique to deploying NIDS within these environments, where traditional intrusion detection systems (IDS) often fall short. This paper discusses NIDS methodologies, including machine learning, deep learning, and hybrid systems, which aim to improve detection accuracy, adaptability, and real-time response. Additionally, this paper addresses the complexity of industrial settings, limitations in current datasets, and the cybersecurity needs of cyber–physical Systems (CPS) and Industrial Control Systems (ICS). The survey provides a comprehensive overview of modern approaches and their suitability for industrial applications by reviewing relevant datasets, emerging technologies, and sector-specific challenges. This underscores the importance of innovative solutions, such as federated learning, blockchain, and digital twins, to enhance the security and resilience of NIDS in safeguarding industrial and robotic systems. [ABSTRACT FROM AUTHOR]

Accurate dynamic models that update in real‐time are vital for industrial robots to adapt flexibly and robustly to evolving environments and tasks, enabling precise model‐based control, motion planning and disturbance estimation. However, the identification of a dynamic robot model involves multiple challenges. First, existing friction models cannot accurately characterize torque variance during prolonged robot operations. Second, traditional offline identification methods such as the least squares (LS) method, suffer from over‐fitting when the inertia parameters are unknown a priori. Furthermore, existing online identification approaches, such as sliding LS, fail to meet the accuracy and real‐time requirements of industrial robots. To address these challenges, this article proposes a two‐stage Bayesian framework for rapid offline identification and online updating of industrial robot dynamics. In the offline stage, we developed a recursive sparse Bayesian learning (RSBL) method to select the dominant parameters and discover a thermal‐related nonlinear dynamic friction model from data, which was then used to find a sparser and simpler inertia model. The obtained friction and inertia models were used as prior knowledge for online updating. The RSBL method significantly reduces the computational complexity and runtime, while it also accelerates online model updating. Experimental results for a 6‐DOF medium‐load robot validate dual offline improvements in prediction accuracy and model sparsity, along with higher online prediction accuracy and shorter computational time. [ABSTRACT FROM AUTHOR]

AbstractThe rotary vector reducer (RV reducer) is a key reduction mechanism for industrial robots, and its torsional stiffness directly determines positioning accuracy and dynamic characteristics. Unlike traditional time-varying torsional stiffness analysis methods considering limited factors and statics solution method, a time-varying torsional stiffness analysis method considering output torque variability and dynamic nonlinearity is proposed to assess the time-varying torsional stiffness of the reducer. Firstly, the rigid-flexible coupling multi-body dynamic model was built employing software ADAMS, with crank shaft and cycloidal gears being treated as flexible bodies. Its correctness was verified under standard condition through grey correlation theory and the torsional stiffness under different conditions was obtained, respectively. Secondly, a contact finite element model of the reducer was established, taking contact relationships between different shaft-gear-bearings into account. As a comparison, a rotating-join finite element model was also constructed. And their torsional stiffness under experimental condition was calculated. Finally, the torsional stiffness test was conducted to verify the accuracy of the three models under this condition. The results show that the time-varying torsional stiffness obtained by the proposed method is only 8.83% different from the test results and exhibiting a certain periodic pattern under standard conditions. [ABSTRACT FROM AUTHOR]

Commercial automation systems for small- and medium-sized laboratories, including research environments, are often complex to use. For liquid handling systems (LHS), development is required not only for the robot's movements but also for adapting the bioanalytical method to the automated system. This study investigates whether a more human-like automation strategy—using a robotic system (RS)—is more suitable for research laboratories than a professional automation approach utilizing a commercial automated LHS. We conducted a series of measurements for protein determination using a Bradford assay manually, with a fully automated LHS, and with our human-like RS. Although the hand-like RS approach requires more than twice the time of the LHS, it achieved the best standard deviation in this setup (RS = 0.5, manual = 0.71, LHS = 0.86). Due to the low limit of detection (LOD) and limit of quantification (LOQ), most protein samples could be quantified with the RS (samples below LOQ = 9.7%, LOD = 0.23; LOQ = 0.25) compared to manual (samples below LOQ = 28.8%, LOD = 0.24; LOQ = 0.26) and the LHS (samples below LOQ = 36.1%, LOD = 0.27; LOQ = 0.31). In another time-dependent enzymatic assay test, the RS achieved results comparable to the manual method and the LHS, although the required time could be a constraint for short incubation times. Our results demonstrate that a more hand-like automation system closely models the manual process, leading easier to accurate bioanalytical results. We conclude that such a system could be more suitable for typical research environments than a complex LHS. [ABSTRACT FROM AUTHOR]

Collaborative robots are becoming popular because they can operate without safety fences and can work in close proximity to humans. Proximity and tactile sensors on the robot's surface are important safety measures for existing robots. In this paper, we propose a string‐like ToF and self‐capacitance combined sensor that can be easily wrapped around various types of existing robots. The sensor can be freely connected to other sensors, and the length of the connected sensors attached can be adjusted depending on the shape of the robot. Prototype sensors were fabricated, and four connected sensors were wrapped around the robotic arm. Sensors on the robot's surface can detect objects within the proximity range and determine contact. In addition, a robotic arm with sensors can be controlled in real‐time using measured data from the sensors. It was confirmed that the proposed sensor can be used for proximity and tactile sensing on existing robots. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In this study, we explore the key factors that educators must consider when designing challenges based on the Challenge-Based Learning (CBL) strategy, where the industrial sector serves as the educational partner. Building on our proposed definition of the CBL technique, we outline a methodology complete with practical suggestions to effectively tackle the challenges encountered during the strategy's implementation. The recommended steps involve choosing a training partner, establishing the company's role in the challenge, taking economic factors into account, and pinpointing crucial milestones in the course development, which includes recognizing the partner's involvement and significance in the course evaluation. Our proposal draws from the authors' experiences in applying this methodology within the context of an Industrial Automation course. [ABSTRACT FROM AUTHOR]

The article describes how the combination of a longue durée perspective and rapid ethnography offers a practical and effective method for the anthropology of robotics based on finding during multi-sited fieldwork in Robotland, a term used for a place or time where robots are employed and operating in everyday life, and are taken for granted.

Industrial automation and robotics are currently a strong field of engineering that is continuously developing and bringing with it significant technological advances. As information technology advances, so too do the processes using elements of this industry, helping to increase both productivity and efficiency. In the creation of robotic environments, simulation tools are used in the design phase through which all the necessary tasks, layouts can be created in detail while minimizing time and financial resources. Currently, the robotic simulator tools market is expanding, which brings with it the solution of several application decisions. The decisionmaking focuses on determining the appropriate simulator with a wide range of capabilities for simulating handling operations in the process of different types of manufacturing. This paper focuses on the study, analysis and testing of a robotic simulator that could be implemented in the context of dental manufacturing, which has several specificities associated with it. RoboDK software was reviewed as part of the study. After creating a model corresponding to a real workplace, analysis plots of the movements of the robot's joints and gripper during manipulation operations were created. [ABSTRACT FROM AUTHOR]

Green energy transformation requires comprehensive strategies that include both innovations in energy production and more efficient energy use. This article investigates the potential for saving electrical energy in industrial automation systems by utilizing bistable switching/relay. Compared to traditional systems, these innovative solutions demonstrate significant reductions in energy consumption. A market analysis of available bistable relays, along with experimental determination of their control conditions, highlights their application potential and indicates the benefits of their implementation. The findings suggest that replacing classical relays with their bistable counterparts could significantly contribute to global sustainability efforts. The article presents the process of redesigning a standard industrial relay into a bistable design. Adding two additional elements achieved the intended bistable functionality. The article calls for increased research and investment in such technologies, emphasizing that the energy-saving potential offered by bistable switching/relay circuits should not be overlooked. [ABSTRACT FROM AUTHOR]

[Display omitted] An overview of modern technologies in the field of laboratory automation, such as robotics and the use of computer vision, is presented. The main methods and equipment required for the automation process are discussed. A brief description of the implementation of collaborative robots and computer vision systems in automated laboratory work-stations is given, and a specific example of a centrifugation procedure is considered. [ABSTRACT FROM AUTHOR]

The exploration of large-scale unknown environments can benefit from the deployment of multiple robots for collaborative mapping. Each robot explores a section of the environment and communicates onboard pose estimates and maps to a central server to build an optimized global multi-robot map. Naturally, inconsistencies can arise between onboard and server estimates due to onboard odometry drift, failures, or degeneracies. The mapping server can correct and overcome such failure cases using computationally expensive operations such as inter-robot loop closure detection and multi-modal mapping. However, the individual robots do not benefit from the collaborative map if the mapping server provides no feedback. Although server updates from the multi-robot map can greatly alleviate the robotic mission strategically, most existing work lacks them, due to their associated computational and bandwidth-related costs. Motivated by this challenge, this paper proposes a novel collaborative mapping framework that enables global mapping consistency among robots and the mapping server. In particular, we propose graph spectral analysis, at different spatial scales, to detect structural differences between robot and server graphs, and to generate necessary constraints for the individual robot pose graphs. Our approach specifically finds the nodes that correspond to the drift's origin rather than the nodes where the error becomes too large. We thoroughly analyze and validate our proposed framework using several real-world multi-robot field deployments where we show improvements of the onboard system up to 90% and can recover the onboard estimation from localization failures and even from the degeneracies within its estimation. [ABSTRACT FROM AUTHOR]

Highlights A practical workflow for optimizing sampling tours for a team of surface and aerial vehicles was developed. Proposed workflow considers unique sensing capabilities of surface vehicles when assigning sampling locations. Likely optimal tours can be found in less than 30 s for practical water quality sampling requirements. Most current marine aquaculture operations are located in coastal estuarine areas within one mile of the shoreline, and water quality in these production areas can quickly become unfavorable due to hydrodynamic processes and excessive runoff. The deployment of autonomous, robotic systems can improve the speed and spatiotemporal resolution of water sampling and sensing in mariculture production areas to assess water quality in the context of food safety. Specifically, teams of both aerial and surface vehicles can be deployed simultaneously to capitalize on the benefits of each system; however, a method to optimally design a feasible sampling tour for each robot is needed to maximize sample capacity and ensure efficient water sampling missions. This research brief presents the problem formulation and a solution method to determine optimal tours for a team of aquatic surface and aerial vehicles while considering different vehicle sampling capacities and endurance constraints. This method was implemented to design sampling missions of 15, 20, and 30 samples in both a 0.25 km2 and 3.9 km2 site, using sampling capacity and endurance constraints corresponding to real-world robots used for water sampling in mariculture environments. Results indicate that this optimization problem can be solved in near-real time in the field and yields feasible sampling tours for surface and aerial vehicles under different constraints. This work is a practical step towards developing teams of collaborative robots to persistently monitor adverse mariculture growing conditions so producers can implement data-driven, timely management strategies. [ABSTRACT FROM AUTHOR]

Industrial robot machining systems are used extensively in processing and manufacturing sectors including aerospace, large ships, rail transit, engineering machinery, semiconductor processing, medical equipment, and new energy vehicles. However, chatter during the machining process can considerably diminish the machining accuracy and surface quality. In severe cases, it can cause damage to the tool and the machining robot. Robot machining chatter research is required to improve the stability of the machining process for high-efficiency, high-quality robotic machining. This review comprehensively summarizes research progress in three areas: robot machining chatter mechanisms, chatter stability prediction and online monitoring, and chatter suppression strategies. First, the generation mechanisms of regenerative chattering and modal-coupling chattering are clarified. Second, the methods of chattering stability prediction and online monitoring of robot machining are summarized. The differences between passive suppression, semi-active suppression, and active suppression strategies are explained. Existing problems and future directions in robot machining chattering research are summarized and prospected. [ABSTRACT FROM AUTHOR]

The flange holes on large aluminum ball shells are processed on-site by industrial robots, and the flange holes need to ensure the hole position and axial accuracy requirements at the same time, but it is challenging to ensure that these two indexes meet the requirements at the same time due to the lower machining accuracy of the industrial robots. For this reason, the paper proposes a robot integrated error compensation method for hole position and axial deviation constraints. First, the robot machining path is generated, and the cutting allowance and hole inner wall measurement strategy are determined by running the machining path empty before machining. Integrated error compensation is then introduced and a new hole surface is constructed as a mirror surface under its conditions. The tool path is adjusted according to the mirror compensation principle to ensure consistency between the machined and target holes. The reconstructed target hole surface satisfies the integrated error constraints and realizes the balanced constraints of positional and axial tolerances, making full use of both tolerances. Finally, the method's effectiveness has been verified in a large-scale work. Experimental results show that hole position error is reduced from uncompensated (1.03, − 0.51) mm to compensated (0.25, − 0.005) mm, and axial error from uncompensated 22.32 mm to compensated 1.39 mm. [ABSTRACT FROM AUTHOR]

Robotic machining is becoming increasingly popular in modern manufacturing owing to its cost and flexibility advantages. Compared with serial robots, a hexapod-based industrial robotic work cell has a much smaller footprint for equivalent rigidity. Thus, a comprehensive understanding of the hexapod's positioning performance under machining and non-machining conditions is crucial to attain the desired robotic machining outcomes of the developed hexapod machining cell, which consists of two Fanuc F-200iB hexapods. The ballbar was chosen as the primary tool to measure the positioning performance, and a novel method based on the ballbar was proposed to facilitate measurement in various machining states. In the no-load running scenario, the positioning performance of the hexapod ranged from 33.8 µm to 99.3 µm (circularity) across 30 positions while considering thermal drift and feeding speeds. During the machining setup without cooling, the positioning performance of the hexapod machining cell demonstrated variations influenced by different machining parameters. Notably, spindle speed was identified as the most impactful factor, followed closely by feeding speed and cutting depth. By leveraging these findings, we enhanced the positioning accuracy of the robotic machining system through compensation, ultimately resulting in improved machining quality for future work. [ABSTRACT FROM AUTHOR]

Due to the COVID-19 outbreak, industries have gained a thrust on contactless processing for computing technologies and industrial automation. Cloud of Things (CoT) is one of the emerging computing technologies for such applications. CoT combines the most emerging cloud computing and the Internet of Things. The development in industrial automation made them highly interdependent because the cloud computing works like a backbone in IoT technology. This supports the data storage, analytics, processing, commercial application development, deployment, and security compliances. Now amalgamation of cloud technologies with IoT is making utilities more useful, smart, service-oriented, and secure application for sustainable development of industrial processes. As the pandemic has increased access to computing utilities remotely, cyber-attacks have been increased exponentially. This paper reviews the CoT's contribution to industrial automation and the various security features provided by different tools and applications used for the circular economy. The in-depth analysis of security threats, availability of different features corresponding the security issues in traditional and non-traditional CoT platforms used in industrial automation have been analysed. The security issues and challenges faced by IIoT and AIoT in industrial automation have also been addressed. [ABSTRACT FROM AUTHOR]

The key technologies analysis of modern high-tech production digitalization within the "Industry 4.0" concept implementation is proposed, related to its organization, hardware and software automation, creation of new mechanisms of inter-machine interaction, taking into account the place and functions of such technologies in the structure of the general scientific and technological development of high-tech enterprises. The concept, essence and application potential of following basic tools are considered - digital transformation as a cyber-physical system, Industrial IoT, product life cycle management system, collaborative work, technological analysis of big data, artificial intelligence and adaptive 3D printing technologies. The deep interconnection and mutual influence of such technologies in high-tech production reflects the systemic nature of the industrial structures transformation and complex application in accordance with the level of automation and robotics. [ABSTRACT FROM AUTHOR]

Summary: The application of computer technologies associated with sensors and artificial intelligence (AI) in the quantification and qualification of quality parameters of meat products of various domestic species is an area of research, development, and innovation of great relevance in the agri‐food industry. This review covers the most recent advances in this area, highlighting the importance of computer vision, artificial intelligence, and ultrasonography in evaluating quality and efficiency in meat products' production and monitoring processes. Various techniques and methodologies used to evaluate quality parameters such as colour, water holding capacity (WHC), pH, moisture, texture, and intramuscular fat, among others related to animal origin, breed and handling, are discussed. In addition, the benefits and practical applications of the technology in the meat industry are examined, such as the automation of inspection processes, accurate product classification, traceability, and food safety. While the potential of artificial intelligence associated with sensor development in the meat industry is promising, it is crucial to recognise that this is an evolving field. This technology offers innovative solutions that enable efficient, cost‐effective, and consumer‐oriented production. However, it also underlines the urgent need for further research and development of new techniques and tools such as artificial intelligence algorithms, the development of more sensitive and accurate multispectral sensors, advances in computer vision for 3D image analysis and automated detection, and the integration of advanced ultrasonography with other technologies. Also crucial is the development of autonomous robotic systems for the automation of inspection processes, the implementation of real‐time monitoring systems for traceability and food safety, and the creation of intuitive interfaces for human‐machine interaction. In addition, the automation of sensory analysis and the optimisation of sustainability and energy efficiency are key areas that require immediate attention to address the current challenges in this agri‐food and agri‐industrial sector, highlighting and emphasising the importance of ongoing innovation in the field. [ABSTRACT FROM AUTHOR]

Industrial robots, both in manufacturing and nonmanufacturing sectors, are evolving rapidly, driven by advancements in artificial intelligence (AI). This paper presents a comprehensive survey of industrial robots, framed through the lens of Howard Gardner's theory of multiple intelligences. By categorising various AI capabilities in industrial robots--such as visual recognition, decision-making, and collaborative interaction--based on Gardner's intelligence framework, we provide a novel taxonomy that bridges human cognitive abilities and artificial systems. The survey explores the historical development of industrial robots, the current state of AI implementation, and future trends in robotics. Additionally, we discuss the implications of these advancements for industries and their workforce, as well as the ethical considerations surrounding the growing autonomy of AI systems. This paper aims to serve as a reference point for researchers and professionals seeking to understand the intersection of cognitive science and industrial AI, highlighting the potential and challenges of integrating AI into robotic systems. [ABSTRACT FROM AUTHOR]

This article proposes a novel framework for the rapid implementation of automation programs in industrial heating and cooling systems. The global push for sustainability necessitates significant infrastructural transformations within these systems, which currently rely heavily on fossil fuels and are responsible for 75% of industrial final energy consumption. Our research highlights the critical role of design patterns and object-oriented programming principles to address the complex integration of additional energy converters and storage into automation programs. By leveraging design patterns, our framework encapsulates the intricacies of various components, such as actuators, sensors, and storage, within a comprehensive object-oriented model that also allows the integration of different control strategies. Qualitatively, this approach enhances the reusability, scalability, and adaptability of automation programs. Therefore, quantitatively, our framework enables a more resilient and efficient energy system. The framework is validated through its application to a complex, cross-linked industrial heating and cooling system at the ETA Research Factory of the Technical University of Darmstadt. Using the developed framework reduces implementation effort significantly due to its consistent and modular structure resulting from the reusable design patterns. [ABSTRACT FROM AUTHOR]

Myocardial infarction (MI), which causes irreversible myocardium necrosis, affects 0.25 billion people globally and has become one of the most significant epidemics of our time. Over the past few years, bioprinting has moved beyond a concept of simply incorporating cells into biomaterials, to strategically defining the microenvironment (e.g., architecture, biomolecular signalling, mechanical stimuli, etc.) within which the cells are printed. Among the different bioprinting applications, myocardial repair is a field that has seen some of the most significant advances towards the management of the repaired tissue microenvironment. This review critically assesses the most recent biomedical innovations being carried out in cardiac patch bioprinting, with specific considerations given to the biomaterial design parameters, growth factors/cytokines, biomechanical and bioelectrical conditioning, as well as innovative biomaterial-based "4D" bioprinting (3D scaffold structure + temporal morphology changes) of myocardial tissues, immunomodulation and sustained delivery systems used in myocardium bioprinting. Key challenges include the ability to generate large quantities of cardiac cells, achieve high-density capillary networks, establish biomaterial designs that are comparable to native cardiac extracellular matrix, and manage the sophisticated systems needed for combining cardiac tissue microenvironmental cues while simultaneously establishing bioprinting technologies yielding both high-speed and precision. This must be achieved while considering quality assurance towards enabling reproducibility and clinical translation. Moreover, this manuscript thoroughly discussed the current clinical translational hurdles and regulatory issues associated with the post-bioprinting evaluation, storage, delivery and implantation of the bioprinted myocardial patches. Overall, this paper provides insights into how the clinical feasibility and important regulatory concerns may influence the design of the bioink (biomaterials, cell sources), fabrication and post-fabrication processes associated with bioprinting of the cardiac patches. This paper emphasizes that cardiac patch bioprinting requires extensive collaborations from imaging and 3D modelling technical experts, biomaterial scientists, additive manufacturing experts and healthcare professionals. Further, the work can also guide the field of cardiac patch bioprinting moving forward, by shedding light on the potential use of robotics and automation to increase productivity, reduce financial cost, and enable standardization and true commercialization of bioprinted cardiac patches. The manuscript provides a critical review of important themes currently pursued for heart patch bioprinting, including critical biomaterial design parameters, physiologically-relevant cardiac tissue stimulations, and newly emerging cardiac tissue bioprinting strategies. This review describes the limited number of studies, to date in the literature, that describe systemic approaches to combine multiple design parameters, including capabilities to yield high-density capillary networks, establish biomaterial composite designs similar to native cardiac extracellular matrix, and incorporate cardiac tissue microenvironmental cues, while simultaneously establishing bioprinting technologies that yield high-speed and precision. New tools such as artificial intelligence may provide the analytical power to consider multiple design parameters and identify an optimized work-flow(s) for enabling the clinical translation of bioprinted cardiac patches. [Display omitted] [ABSTRACT FROM AUTHOR]

The manipulator actuated by pneumatic artificial muscles (PAM) is a widely used type of robotic arm in industrial automation. However, its performance can be limited by non-linear dynamics and uncertainties in the system. To overcome these limitations, this paper proposes a synergetic control strategy (SACT) to improve the performance of the SACT, a social spider optimisation algorithm (SSO) has been suggested for adjusting its parameters. To verify the performance of a PAM-actuated manipulator based on an optimal SACT controller, a computer simulation study was conducted using MATLAB software. Moreover, a comparison study between the optimal synergetic algorithm control theory and the optimal sliding mode controller (SMC) has been made in terms of robustness and transient behaviour characteristics. The provided simulation results have shown that the SACT controller exhibited quicker convergence towards the desired trajectory and maintained a lower steadystate error as compared to the SMC controller. Additionally, the SACT controller demonstrated more resilience to variations in parameters and showed more robust characteristics. [ABSTRACT FROM AUTHOR]

Copyright of Bearing is the property of Bearing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accurate control robotic arms in twodimensional environments present significant challenges, particularly in dynamic, real-time applications. Traditional model-based approaches require substantial system modeling, rendering them computationally extensive. This paper presents an adaptive Artificial Intelligence (AI)-driven approach through the use of Deep Q-Networks (DQN) control for a two-link robotic arm thus supporting better scalability. The DQN algorithm, a model-free Reinforcement Learning (RL) technique, allows the robotic arm to independently learn optimal control strategies by interaction with the environment and adapting to dynamic conditions. The task of the robot established reaches a specific target (red point) within a limited number of episodes. Key components of the methodology contain problem statement, DQN architecture, representation of the state and action spaces, a reward function, and the training process. Experimental results indicate that the DQN agent effectively learns to find optimal actions with high accuracy and robustness in guiding the arm to the target. The performance steadily improves during initial training, followed by stabilization, indicating an effective control policy. This study contributes to the knowledge of reinforcement learning in robotic control tasks and demonstrates, in particular, the potential of DQN for solving complex, goal-oriented tasks with minimal prior modeling. Compared to conventional control approaches, the DQN-driven one reveals higher flexibility, scalability, and efficiency. Although carried out in a simplified 2D environment, the novelty of this research lies in its emphasis on enabling the robotic arm to accomplish goaloriented reaching tasks, lays a strong foundation for future applications in industrial automation and service robotics. [ABSTRACT FROM AUTHOR]

SCARA robots have been used in various fields of robotics, such as biomedical engineering, automation, industrial, and gaming. However, our SCARA (Selective Compliance Assembly Robot Arm) VR model stands out with its realistic design and construction assumptions. The VR testing of the robot's motion envelope has facilitated a more precise inverse kinematics solution and verification of the dynamic process. The intelligent controller of this application, based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) technique and a classical proportional-integral-derivative (PID) controller, offers an optimized solution to the accuracy problem. The hybrid ANFIS controller starts with the PID setting parameters of the resultant solution. Following thorough testing of the suggested SCARA manipulator with an intelligent controller in a virtual reality environment, researchers recognized the physical system's potential for implementation utilizing multiple control approaches. Despite the intricacy of its design and implementation, the intelligent controller's software ensures that the system runs at top efficiency. This application replicates the user interface of the MATLAB/SIMULINK var (2022b), which produced promising robotics results, demonstrating its trustworthiness as a realistic, intelligent model, and virtual reality was critical in the development of the SCARA manipulator. It digs into the design and analysis of a hybrid intelligent controller for SCARA robots, which are widely used in assembly lines and manufacturing. Finally, the proposed controller combines the best features of an Adaptive Neuro-Fuzzy Inference System (ANFIS) with a conventional proportional-integral-derivative (PID) controller to resolve application accuracy difficulties as efficiently as possible. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Distance Education (1672-0008) is the property of Zhejiang Open University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this article, an adaptive iterative learning control (AILC) scheme has been proposed to enhance the accuracy of the dynamic path tracking of 6-degrees of freedom industrial robots. Based on the memorized data and current feedback from a three-dimensional visual measurement instrument, an adaptive algorithm is developed to update the time-varying control parameters of the AILC scheme iteratively. A new compensation signal is calculated to adjust the control inputs produced by the dynamic path tracking control module at each time interval. Through the adaptation algorithm, the identical initial conditions can be relaxed to some extent with the AILC scheme. Moreover, the stability analysis of the proposed AILC scheme is presented. Experimental results on FANUC M20iA, using C-Track 780 as a photogrammetry sensor, demonstrate the superior performance of the developed AILC scheme in terms of pose accuracy, disturbance rejection ability, and control performance. [ABSTRACT FROM AUTHOR]

This paper explores groundbreaking advancements in control strategies for cooperative mobile robots used in large-scale material transport, a critical aspect of modern industrial, manufacturing, logistics, and construction sectors. It delves into the development of sophisticated systems that enable seamless coordination among multiple mobile robot systems. The research presents a novel hierarchical finite state automaton for dynamic mission adaptation and a null space-based control scheme for precise task execution and enhanced system resilience. The introduction of Mecanum wheels facilitates flexible movement and manipulation of materials, thereby increasing the operational efficiency and safety. Cutting-edge sensory technology, including LiDAR (Light Detection and Ranging), and the implementation of Robot Operating System are highlighted for their roles in enhancing autonomous navigation and intelligent operation. Additionally, the paper discusses the impact of centralized and decentralized control methods in ensuring safe cooperative object transport. The findings contribute to the vision of Industry 4.0 by promoting the integration of automation and robotic cooperation in complex environments and present a foundational blueprint for further research. Challenges for future work such as scalability, communication efficiency, collision avoidance, and energy efficiency are also considered, underscoring the need for ongoing development of robust and scalable robotic systems to address modern transport challenges. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive fault diagnosis approach for a delta robot utilizing advanced feature extraction and classification techniques. A four-arm delta robot prototype is designed in SolidWorks for realistic fault analysis. Two case studies investigate faults through control effort and vibration signals, with control effort detecting motor and encoder faults, while vibration signals identify bearing faults. This study compares time-domain signal features and wavelet scattering networks, applied by classification algorithms including wide neural networks (WNNs), efficient linear support vector machine (ELSVM), efficient logistic regression (ELR), and kernel naive Bayes (KNB). Results indicate that a WNN, using wavelet scattering features ranked by one-way anova, is optimal due to its consistency and reliability, while these features enhance computational efficiency by reducing classifier size. Sensitivity analysis demonstrates the classifier's capacity to detect untrained faults, highlighting the importance of effective feature extraction and classification methods for fault diagnosis in complex robotic systems. This research significantly contributes to fault diagnosis in delta robots and lays the groundwork for future studies on fault tolerance control and predictive maintenance planning. Future work will focus on the physical implementation of the delta robot in laboratory settings, aiming to improve operational efficiency and reliability in industrial applications. [ABSTRACT FROM AUTHOR]

Water is a critical resource for human survival worldwide, and its availability and quality in natural reservoirs such as lakes and rivers must be monitored. In that way, wireless dynamic sensor networks can help monitor water quality. These networks have significantly advanced across various sectors, including industrial automation and environmental monitoring. Moreover, the Internet of Things has emerged as a global technological marvel, garnering interest for its ability to facilitate information visualization and ease of deployment—the combination of wireless dynamic sensor networks and the Internet of Things improves water monitoring and helps to care for this vital resource. This article presents the design and deployment of a wireless dynamic sensor network comprising a mobile node outfitted with multiple sensors for remote aquatic navigation and a stationary node similarly equipped and linked to a server via the IoT. Both nodes can measure parameters like pH, temperature, and total dissolved solids (TDS), enabling real-time data monitoring through a user interface and generating a database for future reference. The integrated control system within the developed interface enhances the mobile node's ability to survey various points of interest. The developed project enabled real-time monitoring of the aforementioned parameters, with the recorded data being stored in a database for subsequent graphing and analysis using the IoT. The system facilitated data collection at various points of interest, allowing for a graphical representation of parameter evolution. This included consistent temperature trends, neutral and alkaline zone data for pH levels, and variations in total dissolved solids (TDS) recorded by the mobile node, reaching up to 100 ppm. [ABSTRACT FROM AUTHOR]

The article deals with the issue of verification of selected geometric characteristics of a special robot intended for the nuclear industry. The design of the robot is specific in that the robot works in both semi-automatic and manual mode. This leads to frequent collisions of some parts of the robot with fragmented equipment. Therefore, it was necessary to ensure the accuracy and repeatability of the robot after its production and during its operation. Verification of the parameters of the robot after one year of operation was carried out in the controlled zone of the nuclear power plant, which was specific due to the presence of ionizing radiation. The methodology, technique and method of verification had to be adapted to this. [ABSTRACT FROM AUTHOR]

By integrating deep neural networks with reinforcement learning, the Double Deep Q Network (DDQN) algorithm overcomes the limitations of Q-learning in handling continuous spaces and is widely applied in the path planning of mobile robots. However, the traditional DDQN algorithm suffers from sparse rewards and inefficient utilization of high-quality data. Targeting those problems, an improved DDQN algorithm based on average Q-value estimation and reward redistribution was proposed. First, to enhance the precision of the target Q-value, the average of multiple previously learned Q-values from the target Q network is used to replace the single Q-value from the current target Q network. Next, a reward redistribution mechanism is designed to overcome the sparse reward problem by adjusting the final reward of each action using the round reward from trajectory information. Additionally, a reward-prioritized experience selection method is introduced, which ranks experience samples according to reward values to ensure frequent utilization of high-quality data. Finally, simulation experiments are conducted to verify the effectiveness of the proposed algorithm in fixed-position scenario and random environments. The experimental results show that compared to the traditional DDQN algorithm, the proposed algorithm achieves shorter average running time, higher average return and fewer average steps. The performance of the proposed algorithm is improved by 11.43% in the fixed scenario and 8.33% in random environments. It not only plans economic and safe paths but also significantly improves efficiency and generalization in path planning, making it suitable for widespread application in autonomous navigation and industrial automation. [ABSTRACT FROM AUTHOR]

Do new manufacturing technologies of the Industry 4.0 (I4.0) boost TFP growth? By adopting a distance-to-frontier framework, this paper explores whether the adoption of (advanced) digital technologies affect the sectoral TFP growth rates across manufacturing industries of 14 European countries, during the period 2009–2019. We rely on a novel measure of adoption of I4.0 technologies (namely, advanced industrial robots, additive manufacturing and industrial internet of things), exploiting highly detailed (8-digit level) information on imports of capital goods embodying such technologies. Our results suggest that adopting new digital manufacturing technologies of the I4.0 brings quantitatively important and statistically significant contributions to sectoral TFP growth rates, although these are mostly concentrated in countries close to the technology frontier. In turn, these technologies seem to have hampered the process of convergence between European technological leaders and laggards over the last decade. [ABSTRACT FROM AUTHOR]

We use French data over the 1994–2013 period to study how imports of industrial robots affect firm-level outcomes. Guided by a simple model, we develop a novel empirical strategy to identify the causal effects of robot adoption. Our results suggest that, while demand shocks generate a positive correlation between robot imports and employment at the firm level, exogenous exposure to automation leads to job losses. We also find that robot exposure increases labour productivity and some evidence that it may raise the relative demand for high-skill professions. [ABSTRACT FROM AUTHOR]

In this paper, the design of a driving mechanism for a heavy-duty collaborative robot (cobot) capable of lifting payloads up to 20 kg is presented. This study focuses on an articulated robot utilizing a water-based Electro-Hydraulic Actuator (EHA). The Denavit–Hartenberg (D–H) representation was employed to relate the rotational angles and the end-effector's location, facilitating the design of the actuators. The maximum required torques for joints 2 and 3, responsible for lifting for 12 s, were calculated under quasi-static and dynamic loading conditions. The results showed that the maximum required torques were 126.67 Nm and 58.86 Nm for joint 2 and 3, respectively. The maximum torque for joint 2 occurs when the pitch links are fully extended, whereas the maximum torque for joint 3 occurs when the third link is parallel to the ground. The torques, due to the inertia and Coriolis dynamic terms, were also calculated and found to be lower than those required for the gravitational term. Various maneuvering scenarios, along with Ansys Motion simulation, were analyzed for the verification of the results. Based on the calculated maximum torques, the linear actuators of the EHA were designed. The heavy-duty cobot can be built with the developed actuator proposed in this paper. The total weight of the entire frame was measured to be 14.59 kg, resulting in a high Payload/Weight (P/W) ratio of 1.37. In conclusion, the robot was made lighter and can operate more efficiently, particularly for heavy loads up to 20 kg. [ABSTRACT FROM AUTHOR]

To meet real-time grasping demands in complex environments, this paper proposes a lightweight yet high-performance robotic grasping model. The model integrates large kernel convolution and residual connections to generate grasping information for unknown objects from RGB and depth images, enabling real-time generation of stable grasping plans from the images. The proposed model achieved favorable accuracy on both the Cornell and Jacquard standard grasping datasets. Compared to other methods, the proposed model significantly reduces the number of parameters while achieving comparable performance, making it a lightweight model. Additionally, real-world experiments were conducted using a six-axis collaborative robot on a set of previously unseen household objects with diverse and adversarial shapes, achieving a comprehensive grasping success rate of 93.7%. Experimental results demonstrate that the proposed model not only improves grasping accuracy but also has strong potential for practical applications, particularly in resource-constrained robotic systems. [ABSTRACT FROM AUTHOR]

The research presented in this paper aligns with the advancement of Industry 4.0 by integrating intelligent machine tools and industrial robots within Flexible Manufacturing Systems (FMS). Primarily, a development approach for Digital Twin (DT) is presented, beginning from the design, sizing, and configuration stages of the system and extending through its implementation, commissioning, operation, and simulation-based optimization. The digitization of current industrial processes entails the development of applications based on modern technologies, utilizing state-of-the-art tools and software. The general objective was to create a digital replica of a process to propose optimization solutions through simulation and subsequently achieve virtual commissioning. The practical nature of the research is reflected in the design and implementation of a Digital Twin for a real physical system processing a family of cylindrical parts within an existing experimental FMS. A digital model of the system was created by defining each individual device and piece of equipment from the physical system, so the virtual model operates just like the real one. By implementing the Digital Twin, both time-based and event-based simulations were performed. Through the execution of multiple scenarios, it was possible to identify system errors and collisions, and propose optimization solutions by implementing complex, collaborative-robot equipment where multiple interactions occur simultaneously. [ABSTRACT FROM AUTHOR]

The present paper proposes a detailed Failure Mode, Effects, and Criticality Analysis (FMECA) on harmonic drives, focusing on their integration within the UR5 cobot. While harmonic drives are crucial for precision and efficiency in robotic manipulators, they are also prone to several failure modes that may affect the overall reliability of a system. This work provides a comprehensive analysis intended as a benchmark for advancements in predictive maintenance and condition-based monitoring. The results not only offer insights into improving the operational lifespan of harmonic drives, but also provide guidance for engineers working with similar systems across various robotic platforms. Robotic systems have advanced significantly; however, maintaining their reliability is essential, especially in industrial applications where even minor faults can lead to costly downtimes. This article examines the impact of harmonic drive degradation on industrial robots, with a focus on collaborative robotic arms. Condition-Based Maintenance (CBM) and Prognostics and Health Management (PHM) approaches are discussed, highlighting how digital twins and data-driven models can enhance fault detection. A case study using the UR5 collaborative robot illustrates the importance of fault diagnosis in harmonic drives. The analysis of fault-to-failure mechanisms, including wear, pitting, and crack propagation, shows how early detection strategies, such as vibration analysis and proactive maintenance approaches, can improve system reliability. The findings offer insights into failure mode identification, criticality analysis, and recommendations for improving fault tolerance in robotic systems. [ABSTRACT FROM AUTHOR]

Various types of numerical control (NC) machine tools can be standardly operated and controlled based on NC data that can be easily generated using widespread CAD/CAM systems. On the other hand, the operation environments of industrial robots still depend on conventional teaching and playback systems provided by the makers, so it seems that they have not been standardized and unified like NC machine tools yet. Additionally, robotic functional extensions, e.g., the easy implementation of a machine learning model, such as a convolutional neural network (CNN), a visual feedback controller, cooperative control for multiple robots, and so on, has not been sufficiently realized yet. In this paper, a hyper cutter location source (HCLS)-data-based robotic interface is proposed to cope with the issues. Due to the HCLS-data-based robot interface, the robotic control sequence can be visually and unifiedly described as NC codes. In addition, a VGG19-based CNN model for defect detection, whose classification accuracy is over 99% and average time for forward calculation is 70 ms, can be systematically incorporated into a robotic control application that handles multiple robots. The effectiveness and validity of the proposed system are demonstrated through a cooperative pick and place task using three small-sized industrial robot MG400s and a peg-in-hole task while checking undesirable defects in workpieces with a CNN model without using any programmable logic controller (PLC). The specifications of the PC used for the experiments are CPU: Intel(R) Core(TM) i9-10850K CPU 3.60 GHz, GPU: NVIDIA GeForce RTX 3090, Main memory: 64 GB. [ABSTRACT FROM AUTHOR]

Force control is one of the core modules for surface finishing such as grinding, polishing and sanding. However, the current force control methods based on human skills lack in-depth analysis of data patterns or are only applicable to flat surfaces. In addition, surface finishing is mainly performed by hand, resulting in low processing efficiency and poor product consistency. Therefore, this paper proposes a force control method that incorporates human skills to achieve relatively accurate force skill transfer and complex surface finishing. Firstly, human skills consisting of the force skill and the motion skill are learned. The force skill is used to generate the desired force. Then, a series of discrete poses are obtained based on human demonstration and combined with the motion skill to generate the desired trajectory. Finally, a computed-torque impedance control method is proposed to achieve relatively accurate force skill transfer and complex surface finishing by incorporating the desired trajectory and the desired force. The experiments are conducted on a platform composed of a 7-DOF collaborative robot manipulator from Franka Emika and a complex violin surface. The results demonstrate that the proposed force control method can achieve relatively accurate force skill transfer and improve the surface quality of the workpiece. [ABSTRACT FROM AUTHOR]

This paper explores the effects of industrial robots and information and communication technology (ICT) on regional employment in European Union countries. The empirical analysis relies on a harmonised comprehensive regional dataset that combines business statistics and national and regional accounts data. This rich dataset enables us to provide detailed insights into the employment effects of automation and computerisation in EU regions for the period 2001–16. The results suggest relatively weak effects on regional total employment dynamics. However, industrial robots show negative employment effects in local manufacturing industries and positive employment effects in local non-manufacturing industries. While the negative effect is concentrated in particular local manufacturing industries, the positive effect has operated in local service industries. Information technology investments show positive employment effects in local manufacturing industries and some individual local service industries, while communication technology investments are shown to be irrelevant for employment dynamics. In contrast, software and database investments have had a predominantly negative association with local employment. [ABSTRACT FROM AUTHOR]

This paper provides an in-depth analysis of a novel methodology to enhance the commissioning processes of robotic production lines in the automotive sector, with a particular emphasis on the implementation of offline programming (OLP) methods. The proposed innovative methodology, verified within the automotive industry, introduces a systematic, iterative process for calibrating and aligning the local user coordinate system (UCS) with high-precision external measurements, ensuring minimal discrepancy between simulated and actual robot paths. A significant contribution of this study is an original adjustment of the numerical algorithm applying a closed-form solution to the absolute orientation problem where unit quaternions are used to establish a UCS and evaluate positioning errors. The experimental validation study draws from 485 measurement datasets gathered across more than 300 robot stations, with each dataset comprising at least six measured point pairs, using readings from both internal robot positioning systems and a Leica AT403 laser tracker, aligned with nominal tooling values. This approach addresses discrepancies between simulated and actual environments, and our findings show an 83.51% success rate for direct implementation of simulated robot path programs. This result underscores the effectiveness of the proposed method and demonstrates the accuracy of the developed numerical algorithm, providing a reliable measure of real OLP implementation effectiveness in the automotive sector. This method further streamlines multi-robot station setup through centralized UCS alignment, significantly reducing commissioning time and enhancing efficiency in both the assembly and commissioning stages of robotized production lines. The proposed methodology facilitates precise alignment in the commissioning stage and highlights the need for synchronized simulation updates, robust offline programming practices, and regular kinematic error verification to further enhance OLP accuracy. [ABSTRACT FROM AUTHOR]