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Warehouse operations have been significantly improved because of the rapid advancement of cyber-physical system technologies, preparing for Work-of-the-Future. The challenge, however, is how to design a collaborative system to deliver optimal performance by multiple agents who are highly distributed but interconnected and operate with technologies that provide massive amounts of real-time data. To address the challenge, in this article, the Cyber Collaborative Protocol for Dual-Cycle Task in Future Warehouse is developed to minimise total operation cost and time. The problem is addressed in two phases; the Global and Local phases. The global phase has higher computational power, maintaining a mathematical model, while the local phase has limited computational power and time, utilising heuristics to deliver the outcome. Computer experiments are utilised for validating the designed protocol compared with other alternatives. The results show that, in all given scenarios, the newly designed protocol outperforms alternatives with statistical significance. The original contribution of this research is the design and control of Cyber Collaborative Warehouse operations with a new focus on collaborative multi-agent interactions. In addition, a major implication is that future warehouses can benefit competitively by operating with Task Administration Protocols such as the new Cyber Collaborative Protocol for Dual-Cycle Task in Future Warehouse. [ABSTRACT FROM AUTHOR]

Objective: This study investigated the effects of different approach directions, movement speeds, and trajectories of a co-robot's end-effector on workers' mental stress during handover tasks. Background: Human–robot collaboration (HRC) is gaining attention in industry and academia. Understanding robot-related factors causing mental stress is crucial for designing collaborative tasks that minimize workers' stress. Methods: Mental stress in HRC tasks was measured subjectively through self-reports and objectively through galvanic skin response (GSR) and electromyography (EMG). Robot-related factors including approach direction, movement speed, and trajectory were analyzed. Results: Movement speed and approach direction had significant effects on subjective ratings, EMG, and GSR. High-speed and approaching from one side consistently resulted in higher fear, lower comfort, and predictability, as well as increased EMG and GSR signals, indicating higher mental stress. Movement trajectory affected GSR, with the sudden stop condition eliciting a stronger response compared to the constrained trajectory. Interaction effects between speed and approach direction were observed for "surprise" and "predictability" subjective ratings. At high speed, approach direction did not significantly differ, but at low speeds, approaching from the side was found to be more surprising and unpredictable compared to approaching from the front. Conclusion: The mental stress of workers during HRC is lower when the robot's end effector (1) approaches a worker within the worker's field of view, (2) approaches at a lower speed, or (3) follows a constrained trajectory. Application: The outcome of this study can serve as a guide to design HRC tasks with a low level of workers' mental stress. [ABSTRACT FROM AUTHOR]

Molds are assembled manually due to a shortage of skilled workers and challenges associated with automating operations, which arise from the low-volume, high-variety characteristics of mold production. This study proposed a human–robot collaborative mold assembly using two heterogeneous collaborative robots to address the ergonomic concerns. The use of two heterogeneous cobots enables the handling of different assembly requirements. The diversity of mold structure and different specifications of resources require comprehensive knowledge management to enable interaction and collaboration among resources. However, knowledge management in the domain of mold assembly is yet to be developed in a format understandable by both human and robots. Therefore, a framework of knowledge management is proposed to manage the knowledge within the human–robot collaboration (HRC) in a mold assembly domain. This framework includes an ontology-based decision making that utilizes outcomes from task assignment to decide the mold parts arrangement within the HRC workspace. A set of rules are modeled in the developed ontology for knowledge reasoning according to the use case of collaborative assembly of two-plate injection mold. In addition to part arrangement, the developed HRC ontology can be used to extract data and information based on user's request and decisions, such as tool selection for subtask execution. The HRC mold assembly ontology serves as a stepping stone towards developing a context-based decision making for multi-resources HRC in future implementation. [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]

Traditionally, in robotic surface finishing, the entire workpiece is processed at a uniform speed, predetermined by the operator, which does not account for variations in the machinability across different regions of the workpiece. This conventional approach often leads to inefficiencies, especially given the diverse geometrical characteristics of workpieces that could potentially allow for different machining speeds. Our study introduces a region-based approach, which improves surface finishing machining time by allowing variable speeds and directions tailored to each region's specific characteristics. This method leverages a task-oriented strategy integrating robot kinematics and workpiece surface geometry, subdivided by the clustering algorithm. Subsequently, methods for optimization algorithms were developed to calculate each region's optimal machining speeds and directions. The efficacy of this approach was validated through numerical results on two distinct workpieces, demonstrating significant improvements in machining times. The region-based approach yielded up to a 37% reduction in machining time compared to traditional single-direction machining. Further enhancements were achieved by optimizing the workpiece positioning, which, in our case, added up to an additional 16% improvement from the initial position. Validation processes were conducted to ensure the collaborative robot's joint velocities remained within safe operational limits while executing the region-based surface finishing strategy. [ABSTRACT FROM AUTHOR]

Recently, there have been increasing attempts to apply computer vision technology to the coffee industry. However, most research has focused on classifying the quality or characteristics of the coffee beans. This paper explore relatively low-complexity computer vision algorithms that can evaluate the quality of crema through comparative index values of images taken after espresso extraction. Twelve types of coffee beans are prepared varying in variety, blend, and roast level, and four algorithms of two-dimensional discrete cosine transform, edge detection, clustering & variance and uniform rotational-invariant local binary pattern are applied to the espresso images of them. Through statistical analysis of the resulting images after applying the algorithms, it is examined whether they have the potential to serve as crema evaluation tools. As a result, the density of edge points obtained through the Canny edge detection algorithm is found to be useful in assessing the quality of the crema, regardless of the type of coffee beans. [ABSTRACT FROM AUTHOR]

Cooperative robots that perform safety functions as part of cooperative work with humans have been recently drawing attention. Proximity and tactile sensors that can be retrofitted to a robot's surface are important safety measures for existing robots that are not manufactured as cooperative robots to be used as cooperative robots. In this paper, a string‐like self‐capacitance‐based proximity and tactile sensor that can be easily wrapped around various types of existing robots is proposed. The novel and useful point in this paper is that it can be easily mounted on various robots by a flexible string‐like proximity and tactile sensor. The sensor module can be adjusted to the required length by connecting different numbers of modules depending on the mounting surface. As a demonstration, six prototype sensor modules were connected by each connector and wrapped around a robotic arm. The sensor modules on the robot's surface could detect an object within its proximity range and detect the rough pressure after contact. In addition, the robotic arm with the sensor modules could be controlled in real time using the data obtained from the sensor that are object detection data at proximity range. These results confirm that the proposed sensor module can be used in the proximity and tactile sensors of existing robots. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The article presents the possibilities of automating production pre-processing and post-processing operations for the Material Extrusion - MEX process based on Fused Filament Fabrication technology. Automation is based on hardware and software solutions. For this purpose, a special research station was developed, equipped with a warehouse of working platforms, a 3D printer and a collaborative robot that integrates individual elements of the manufacturing process. The developed solution allows for increasing the efficiency of the manufacturing cell and reducing the operator's involvement in manual operations at the pre-processing and post-processing stages. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She 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.)

This paper proposes a method for solving the path planning problem for a collaborative robot. The time-optimal, smooth, collision-free B-spline path is obtained by the application of a nature-inspired optimization algorithm. The proposed approach can be especially useful when moving items that are delicate or contain a liquid in an open container using a robotic arm. The goal of the optimization is to obtain the shortest execution time of the production cycle, taking into account the velocity, velocity and jerk limits, and the derivative continuity of the final trajectory. For this purpose, the velocity profiling algorithm for B-spline paths is proposed. The methodology has been applied to the production cycle optimization of the pick-and-place process using a collaborative robot. In comparison with point-to-point movement and the solution provided by the RRT* algorithm with the same velocity profiling to ensure the same motion limitations, the proposed path planning algorithm decreased the entire production cycle time by 11.28% and 57.5%, respectively. The obtained results have been examined in a simulation with the entire production cycle visualization. Moreover, the smoothness of the movement of the robotic arm has been validated experimentally using a robotic arm. [ABSTRACT FROM AUTHOR]

This study investigates the potential of a voice-controlled collaborative robot to promote computational thinking (CT) skills in preschoolers aged 4 to 6 in Southern Chile. During a week-long workshop, 34 children engaged in activities designed to develop several CT components while interacting with a robotic arm. Pre- and post-workshop assessments utilizing the TechCheck-K tool revealed significant improvements in CT skills. The findings of this preliminary study demonstrate the effectiveness of voice-controlled robots as innovative tools in early education, significantly enhancing young learners' understanding of CT concepts, practices, and perspectives. This research advocates for further exploration of robotic integration in educational settings, highlighting their potential to improve or extend preschool learning environments and foster CT skill development. [ABSTRACT FROM AUTHOR]

Robots in hazardous environments demand precise and advanced motion control, making extensive simulations crucial for verifying the safety of motion planning. This paper presents a simulation system that enables interactive path editing, allowing for motion planning in a simulated collaborative robot environment and its real-world application. The system includes a simulation host, a control board, and a robot. Unity 3D on a Windows platform provides the simulation environment, while a virtual Linux environment runs ROS2 for execution. Unity sends edited motion paths to ROS2 using the Unity ROS TCP Connector package. The ROS2 MoveIt framework generates trajectories, which are synchronized back to Unity for simulation and real-world validation. To control the six-axis Indy7 collaborative robot, we used the MIO5272 embedded board as an EtherCAT master. Verified trajectories are sent to the target board, synchronizing the robot with the simulation in position and speed. Data are relayed from the host to the MIO5272 using ROS2 and the Data Distribution Service (DDS) to control the robot via EtherCAT communication. The system enables direct simulation and control of various trajectories for robots in hazardous environments. It represents a major advancement by providing safe and optimized trajectories through efficient motion planning and repeated simulations, offering a clear improvement over traditional time-consuming and error-prone teach pendant methods. [ABSTRACT FROM AUTHOR]

Collaborative robots are used in scenarios requiring interaction with humans. In order to improve the safety and adaptability of collaborative robots during human–robot interaction, this paper proposes a modular wire-actuated robotic arm with symmetric variable-stiffness units. The variable-stiffness unit is employed to extend the stiffness-adjustment range of the robotic arm. The variable-stiffness unit is designed based on flexure, featuring a compact and simple structure. The stiffness–force relationship of the variable-stiffness unit can be fitted by a quadratic function with an R-squared value of 0.99981, indicating weak nonlinearity. Based on the kinematics and stiffness analysis of the symmetric joint module of the robotic arm, the orientation of the joint module can be adjusted by regulating the length of the wires and the stiffness of the joint module can be adjusted by regulating the tension of the wires. Because of the actuation redundancy, the orientation and stiffness of the joint module can be adjusted synchronously. Furthermore, a direct method is proposed for the stiffness-oriented wire-tension-distribution problem of the 1-DOF joint module. A simulation is carried out to verify the proposed method. The simulation result shows that the deviation between the calculated stiffness and the desired stiffness was less than 0.005%. [ABSTRACT FROM AUTHOR]

This study, rooted in dynamic capability theory, explores the impact of Collaborative robot (cobots) adoption on manufacturing firm performance. Cobots positively influence sensing, seizing, and transforming capabilities, leading to enhanced market, operational, and human resources performance. Sensing and transforming capabilities mediate the relationship between cobots adoption and operational performance, while seizing capability mediates with market and human resources performance. These findings offer crucial theoretical insights for strategic management in manufacturing enterprises navigating cobots adoption amid global competition and workforce challenges, ensuring a nuanced understanding of the dynamics at play.

Currently, collaborative robots (cobots) are mostly programmed to do one task repetitively. They can be programmed at different speeds and work near human operators. The goal of our research was to investigate the effect of robot speed on acceptance, subjective and objective stress, and cognitive workload of individuals. Therefore, we organized a repeated measures experiment in which participants (N = 25) conducted an assembly task with the YuMi cobot from ABB at a low and at a high speed. Subjective and physiological responses were collected, and participants were subjected to a standardized stress test. Our results indicate that when working with a cobot at a high speed, people believe they can work faster and be more productive but also experience a higher workload and higher perceived stress. We also found that tonic EDA is a significant physiological predictor for monitoring perceived stress in humans. We observed a greater relative increase in tonic EDA from baseline to task execution during high-speed mode compared to low-speed mode. Additionally, this increase in tonic EDA significantly correlated with participants' perceived stress levels. However, workload could not be predicted by any of the physiological measures. Future research should explore the effect of higher cobot working speeds and the use of physiological measures (such as stress) as input to guide the collaboration between individuals and cobots. [ABSTRACT FROM AUTHOR]

The depth image obtained by consumer-level depth cameras generally has low resolution and missing regions due to the limitations of the depth camera hardware and the method of depth image generation. Despite the fact that many studies have been done on RGB image completion and super-resolution, a key issue with depth images is that there will be evident jagged boundaries and a significant loss of geometric information. To address these issues, we introduce a multi-scale progressive fusion network for depth image completion and super-resolution in this paper, which has an asymptotic structure for integrating hierarchical features in different domains. We employ two separate branches to learn the features of a multi-scale image given a depth image and its corresponding RGB image. The extracted features are then fused into different level features of these two branches using a step-by-step strategy to recreate the final depth image. To confine distinct borders and geometric features, a multi-dimension loss is also designed. Extensive depth completion and super-resolution studies reveal that our proposed method outperforms state-of-the-art methods both qualitatively and quantitatively. The proposed methods are also applied to two human–robot interaction applications, including a remote-controlled robot based on an unmanned ground vehicle (UGV), AR-based toolpath planning, and automatic toolpath extraction. All these experimental results indicate the effectiveness and potential benefits of the proposed methods. [ABSTRACT FROM AUTHOR]

Collaborative robots are an integral component of the intelligent manufacturing field. The recognition of hand motions based on surface electromyography signals is even more significant for advancing the research of collaborative robots. However, supervised learning based hand motions recognition methods require an enormous quantity of data as support and must meet the condition of independent and identical distribution. In real-world scenarios, gender classifications, collecting conditions, and even individual-to-individual variances may influence the data, posing a challenge for the recognition of hand motions from new participants. Consequently, we propose a domain adaptive framework based on subspace and second-order statistical distribution alignment (SSDA) to overcome the issue of non-independently and identically distributed data shift. We combine the second-order statistical distribution alignment and subspace alignment. SSDA diminishes the geometric and statistical distribution discrepancies between the training and test sets in hand motions recognition. SSDA enhances the average accuracy of hand motions recognition by 26.66%, 14.43%, and 25.76% in three cross-domain scenarios (cross-gender, cross-circumstance, and cross-individual), respectively, compared with the baseline method of direct classification. Experimental results indicate that the proposed method is effective in solving the problem of distribution shift between target data (test set) and priori data (training set) in hand motions recognition. Simultaneously, it improves the recognition accuracy of classifier for different distributed data, thereby providing a new idea for achieving efficient human–robot collaboration. [ABSTRACT FROM AUTHOR]

In the era of Industry 4.0, manufacturing enterprises are actively adopting collaborative robots (Cobots) in their productions. Current online and offline robot programming methods are difficult to use and require extensive experience or skills. On the other hand, the manufacturing industries are experiencing a labor shortage. An essential question, therefore, is: how would a new robot programming method help novice users complete complex tasks effectively, efficiently, and intuitively? To answer this question, we proposed HA R 2 bot, a novel human-centered augmented reality programming interface with awareness of cognitive load. Using NASA's system design theory and the cognitive load theory, a set of guidelines for designing an AR-based human-robot interaction system is obtained through a human-centered design process. Based on these guidelines, we designed and implemented a human-in-the-loop workflow with features for cognitive load management. The effectiveness and efficiency of HA R 2 bot are verified in two complex tasks compared with existing online programming methods. We also evaluated HA R 2 bot quantitatively and qualitatively through a user study with 16 participants. According to the user study, compared with existing methods, HA R 2 bot has higher efficiency, a lower overall cognitive load, lower cognitive loads for each type, and higher safety. [ABSTRACT FROM AUTHOR]

Smart factories build on cyber-physical systems as one of the most promising technological concepts. Within smart factories, condition-based and predictive maintenance are key solutions to improve competitiveness by reducing downtimes and increasing the overall equipment effectiveness. Besides, the growing interest towards operation flexibility has pushed companies to introduce novel solutions on the shop floor, leading to install cobots for advanced human-machine collaboration. Despite their reliability, also cobots are subjected to degradation and functional failures may influence their operation, leading to anomalous trajectories. In this context, the literature shows gaps in what concerns a systematic adoption of condition-based and predictive maintenance to monitor and predict the health state of cobots to finally assure their expected performance. This work proposes an approach that leverages on a framework for fault detection and diagnostics of cobots inspired by the Prognostics and Health Management process as a guideline. The goal is to habilitate first-level maintenance, which aims at informing the operator about anomalous trajectories. The framework is enabled by a modular structure consisting of hybrid series modelling of unsupervised Artificial Intelligence algorithms, and it is assessed by inducing three functional failures in a 7-axis collaborative robot used for pick and place operations. The framework demonstrates the capability to accommodate and handle different trajectories while notifying the unhealthy state of cobots. Thanks to its structure, the framework is open to testing and comparing more algorithms in future research to identify the best-in-class in each of the proposed steps given the operational context on the shop floor. [ABSTRACT FROM AUTHOR]

The development of a 3-DOF robotic arm powered by pneumatic artificial muscles is represented as an innovative approach in the field of robotics, with the advantages of lightweight and flexible design being combined with the power and control benefits of pneumatic actuation. The design process, challenges, and solutions that were encountered in the development of the robotic arm are outlined in this paper, with an emphasis on its potential applications in industrial and research settings being highlighted. Experimental results demonstrated that the arm can handle loads up to 250 grams, maintain precise horizontal positioning, achieve a 90° rotation at each joint, and operate within a minimum workspace radius of 250 mm. Advanced control algorithms, including sliding mode control, were implemented to optimize the arm's performance. The control system successfully maintained the set angles within a tolerance range, achieving a minimum angular error of approximately 0.7°. Additionally, the robotic arm's modular design is enabled to allow easy customization and scalability, making it possible for the arm to be tailored to a wide range of tasks, from precise laboratory work to more robust industrial applications. [ABSTRACT FROM AUTHOR]

The idea of sensorizing a strain wave gear to measure the transmitted torque has been reported since the 1980s. The strain in the elastic flex spline is typically measured by strain gages attached to it. The resulting voltages relate to the transmitted torque in the gear. However, periodic inaccuracies in the measured torque signal (sensing ripple), resulting from positioning inaccuracies of strain gages on the flex spline, prevented this technology from being used outside a lab environment. Regardless of these difficulties, measuring the torque directly in the strain wave gear would bring many advantages, especially in robotic applications, where design space is highly limited. Traditionally, robotic joints are equipped with link-sided torque sensors, which reduce the available design volume, lower the joint stiffness, and require complex cable routing. This paper presents an experimental study of a novel sensorized strain wave gear named RT1-T, which was developed by Schaeffler Technologies. The study was implemented on a joint testbed, including a high-resolution reference torque sensor at the link side. In addition to the measurement accuracy and linearity, a torque ripple analysis is performed. The joint torque control capabilities are determined along dynamic trajectories and compared to the performance achieved with a link-sided reference sensor. The sensor employed in the testbed has a static torque error of 0.42 Nm and an average closed-loop torque control error of 0.65 Nm above the reference sensor.

Vehicle manufacturing requires error-free processes, as modern vehicles are made up of thousands of parts, including around 280 critical components for safe driving. According to the National Highway Traffic Safety Administration (NHTSA), 2% of vehicle accidents will be caused by defective parts. Current inspection systems in manufacturing plants have limitations, with a high risk of defective parts reaching consumers and leading to recalls. This study aims to develop a real-time, deep learning-based engine part inspection system to improve accuracy and efficiency in mass production. The system, implemented in a large automotive manufacturing plant, uses a Fanuc CR-15ia collaborative robot to inspect engine parts. Combining the single-shot detector (SSD) and faster region-based convolutional neural network (R-CNN) algorithms, the system achieves 99.9% accuracy measured after four months of use, with an Average Precision (AP) of 0.994 for Faster R-CNN and 0.955 for SSD. The inspection system addresses cycle time concerns and is integrated with factory systems for real-time data sharing. Ongoing enhancements aim to improve system performance further.

The article presents the possibilities of automating production pre-processing and post-processing operations for the Material Extrusion - MEX process based on Fused Filament Fabrication technology. Automation is based on hardware and software solutions. For this purpose, a special research station was developed, equipped with a warehouse of working platforms, a 3D printer and a collaborative robot that integrates individual elements of the manufacturing process. The developed solution allows for increasing the efficiency of the manufacturing cell and reducing the operator's involvement in manual operations at the pre-processing and post-processing stages.

Humans use collaborative robots as tools for accomplishing various tasks. The interaction between humans and robots happens in tight shared workspaces. However, these machines must be safe to operate alongside humans to minimize the risk of accidental collisions. Ensuring safety imposes many constraints, such as reduced torque and velocity limits during operation, thus increasing the time to accomplish many tasks. However, for applications such as using collaborative robots as haptic interfaces with intermittent contacts for virtual reality applications, speed limitations result in poor user experiences. This research aims to improve the efficiency of a collaborative robot while improving the safety of the human user. We used Gaussian process models to predict human hand motion and developed strategies for human intention detection based on hand motion and gaze to improve the time for the robot and human security in a virtual environment. We then studied the effect of prediction. Results from comparisons show that the prediction models improved the robot time by 3% and safety by 17%. When used alongside gaze, prediction with Gaussian process models resulted in an improvement of the robot time by 2% and the safety by 13%.

In this paper, we propose a novel system in which a human and a device work in unison on a single task with the aim to simultaneously support human cognitive and physical functions. In this system, the cognitive and physical burdens of the task are shared between the human and device, with the human understanding the entire task and performing rough upstream actions. Additionally, the device understands the surrounding environment, objects, and the human and executes detailed downstream actions. A prototype device was developed to assist in solving silhouette puzzles that abstract and generalize cognitive and physical tasks. The prototype is a pen-shaped device held in the user’s hand. The device picks up puzzle pieces and rotates them to the appropriate position. A display is placed in front of the user and provides feedback information for communication between the system and user. Experiments showed that the proposed device reduced the time required to solve these puzzles. Furthermore, the work was found to be accurate with no errors, and the time required was stable with no irregularities. The subjective evaluation results using the NASA Task Load Index showed that the proposed system can decrease mental load and burden. Moreover, it was confirmed that the human and device must communicate with each other to work simultaneously on one task. In conclusion, the proposed device can support human cognitive and physical functions simultaneously.

Traditionally, in robotic surface finishing, the entire workpiece is processed at a uniform speed, predetermined by the operator, which does not account for variations in the machinability across different regions of the workpiece. This conventional approach often leads to inefficiencies, especially given the diverse geometrical characteristics of workpieces that could potentially allow for different machining speeds. Our study introduces a region-based approach, which improves surface finishing machining time by allowing variable speeds and directions tailored to each region’s specific characteristics. This method leverages a task-oriented strategy integrating robot kinematics and workpiece surface geometry, subdivided by the clustering algorithm. Subsequently, methods for optimization algorithms were developed to calculate each region’s optimal machining speeds and directions. The efficacy of this approach was validated through numerical results on two distinct workpieces, demonstrating significant improvements in machining times. The region-based approach yielded up to a 37% reduction in machining time compared to traditional single-direction machining. Further enhancements were achieved by optimizing the workpiece positioning, which, in our case, added up to an additional 16% improvement from the initial position. Validation processes were conducted to ensure the collaborative robot’s joint velocities remained within safe operational limits while executing the region-based surface finishing strategy.

Collaborative robots (cobots) are important components of the Industry 4.0 paradigm and smart manufacturing. Cobots are known for their ability to interact with the operators in a shared workspace. Due to their spread in the last decade, cobot research proliferated. However, most individual studies focused on specific aspects of cobot deployment, and only scant attention was given to their evaluation (mostly not based on productivity criteria). Thus, better support is needed for cobot acquisition and deployment decisions. This paper answers this need by presenting a summary of the major considerations related to cobots acquisition and deployment, and providing a productivity analysis procedure that supports cobot acquisition and deployment decisions. Defining the cobots' required characteristics and capabilities, effectively narrows the possible selection of cobots. However, it does not give information as to the economic value of acquiring and deploying a specific cobot. So, in addition to cobots' characteristics and capabilities, this paper presents computational techniques to analyse and support this decision for a single workstation per se, and for a station in an assembly line. The difference between these two cases is discussed and analysed, and corresponding models are presented with computational examples. [ABSTRACT FROM AUTHOR]

Human–robot collaborative systems bring several benefits in using human and robot capabilities simultaneously. One of the critical questions is the impact of these systems on production process efficiency. The search for high-level efficiency is severely dependent on collaborative robot characteristics and motion parameters, and the ability of humans to adjust to changing circumstances. Therefore, our research analyzes the effect of the changing collaborative robot motion parameters, acoustic parameters and visual factors in a specific assembly operation, where efficiency is measured through operation times. To conduct our study, we designed a digital twin-based model and a laboratory environment experiment in the form of a collaborative workplace. The results show that changing the motion, acoustic and visual parameters of the collaborative workplace impact the assembly process efficiency significantly. [ABSTRACT FROM AUTHOR]

Robot workpiece machining is interesting in industry as it offers some advantages, such as higher flexibility in comparison with the conventional approach based on CNC technology. However, in recent years, we have been facing a strong progressive shift to custom-based manufacturing and low-volume/high-mix production, which require a novel approach to automation via the employment of collaborative robotics. However, collaborative robots feature only limited motion capability to provide safety in cooperation with human workers. Thus, it is highly necessary to perform more detailed robot task planning to ensure its feasibility and optimal performance. In this paper, we deal with the problem of studying kinematic robot performance in the case of such manufacturing tasks, where the robot tool is constrained to follow the machining path embedded on the workpiece surface at a prescribed orientation. The presented approach is based on the well-known concept of manipulability, although the latter suffers from physical inconsistency due to mixing different units of linear and angular velocity in a general 6 DOF task case. Therefore, we introduce the workpiece surface constraint in the robot kinematic analysis, which enables an evaluation of its available velocity capability in a reduced dimension space. Such constrained robot kinematics transform the robot's task space to a two-dimensional surface tangent plane, and the manipulability analysis may be limited to the space of linear velocity only. Thus, the problem of physical inconsistency is avoided effectively. We show the theoretical derivation of the proposed method, which was verified by numerical experiments. [ABSTRACT FROM AUTHOR]

The rapid advancement of robotics and artificial intelligence paves the way for collaborative robotics to revolutionise industrial operations. Collaborative robotics can bring significant improvements in productivity, efficiency, and safety. Recently, the concept of Symbiotic Human-Robot Collaboration (SHRC) has emerged within the field of collaborative robotics, emphasising the seamless and adaptive integration of human and robotic capabilities within a shared workspace to optimise overall production performance. However, existing collaborative systems frequently face difficulties in establishing dynamic relationships with humans and adapting to evolving conditions, ultimately restricting their overall potential. This paper investigates the essential features required for collaborative workstations to enable SHRC. Moreover, with the aim of fully harnessing the power of collaborative robotics, the paper introduces a design methodology that supports the development of symbiotic workstations for specific assembly operations. A real-world case study is provided, demonstrating the practical implementation and benefits of the proposed design approach. [ABSTRACT FROM AUTHOR]

Bio-simulants and finite element (FE) models have been used to investigate internal injuries caused by external impacts. As the collaboration between humans and industrial robots increases, it is important to understand the expected injuries or damage caused by collaborative robots. In this paper, FE models of the human chest were built to investigate injuries caused by low-speed non-penetrating blunt impacts from collaborative robots. The FE models were validated by comparing them with experimental results. The validated FE models were then used to calculate the maximum force and deformation that would be experienced by the human chest under different impact conditions. The results of the FE analysis were compared with relevant real human test studies. [ABSTRACT FROM AUTHOR]

Objective: Trade-offs between productivity, physical workload (PWL), and mental workload (MWL) were studied when integrating collaborative robots (cobots) into existing manual work by optimizing the allocation of tasks. Background: As cobots become more widely introduced in the workplace and their capabilities greatly improved, there is a need to consider how they can best help their human partners. Methods: A theoretical data-driven analysis was conducted using the O*NET Content Model to evaluate 16 selected jobs for associated work context, skills, and constraints. Associated work activities were ranked by potential for substitution by a cobot. PWL and MWL were estimated using variables from the O*Net database that represent variables for the Strain Index and NASA-TLX. An algorithm was developed to optimize work activity assignment to cobots and human workers according to their most suited abilities. Results: Human workload for some jobs decreased while workload for some jobs increased after cobots were reassigned tasks, and residual human capacity was used to perform job activities designated the most important to increase productivity. The human workload for other jobs remained unchanged. Conclusions: The changes in human workload from the introduction of cobots may not always be beneficial for the human worker unless trade-offs are considered. Application : The framework of this study may be applied to existing jobs to identify the relationship between productivity and worker tolerances that integrate cobots into specific tasks. [ABSTRACT FROM AUTHOR]

As a critical component of the joint gearbox, spur gear pairs play a crucial role in energy conversion, limiting the performance of a collaborative robot. Accurately assessing their instantaneous efficiency and torque fluctuation is essential for developing high-precision robot joint control models. This study proposes a computational model to predict the instantaneous efficiency and torque fluctuation of spur gears under typical operating conditions. The model incorporates a torque balance model, a load distribution model, and a friction model to reflect the relationship between gear meshing position and efficiency. The instantaneous efficiency and torque fluctuation of gear pairs were compared with the Coulomb friction model with an average friction coefficient and the elastohydrodynamic lubrication model with a time-varying friction coefficient. The effect of gear contact ratio on efficiency is analysed, while the instantaneous efficiency and torque fluctuation of gears are studied under varying operating conditions. The results indicate a maximum efficiency difference of 1.86 % between the two friction coefficient models. Under specific operating conditions, the instantaneous efficiency variation of the gear pair can reach 3.34 %, and the torque fluctuation can reach 5.19 Nm. Finally, this study demonstrates the effectiveness and accuracy of the proposed method through comparative analysis. [ABSTRACT FROM AUTHOR]

Aim: To compare the implant accuracy, safety and morbidity between robot‐assisted and freehand dental implant placement. Materials and Methods: Subjects requiring single‐site dental implant placement were recruited. Patients were randomly allocated to freehand implant placement and robot‐assisted implant placement. Differences in positional accuracy of the implant, surgical morbidity and complications were assessed. The significance of intergroup differences was tested with an intention‐to‐treat analysis and a per‐protocol (PP) analysis (excluding one patient due to calibration error). Results: Twenty patients (with a median age of 37, 13 female) were included. One subject assigned to the robotic arm was excluded from the PP analysis because of a large calibration error due to the dislodgement of the index. For robot‐assisted and freehand implant placement, with the PP analysis, the median (25th–75th percentile) platform global deviation, apex global deviation and angular deviation were 1.23 (0.9–1.4) mm/1.9 (1.2–2.3) mm (p =.03, the Mann–Whitney U‐test), 1.40 (1.1–1.6) mm/2.1 (1.7–3.9) mm (p <.01) and 3.0 (0.9–6.0)°/6.7 (2.2–13.9)° (p =.08), respectively. Both methods showed limited damage to the alveolar ridge and had similar peri‐ and post‐operative morbidity and safety. Conclusions: Robot‐assisted implant placement enabled greater positional accuracy of the implant compared to freehand placement in this pilot trial. The robotic system should be further developed to simplify surgical procedures and improve accuracy and be validated in properly sized trials assessing the full spectrum of relevant outcomes. [ABSTRACT FROM AUTHOR]

Due to the presence of clearances in the gearing of 3K planetary gear reducers, transmission clearances were introduced into the transmission chain, resulting in a decrease in transmission accuracy and causing impacts during driving direction changes. By utilizing the characteristics that the carrier did not participate in torque transmission in 3K planetary gear transmission, a flexible planetary carrier was innovatively proposed to eliminate clearances of 3K planetary gear reducers, and the effectiveness of the proposed clearance elimination mechanisms was verified through simulation analysis. Gear matching and efficiency optimization were performed to achieve high forward and backward driving efficiency. Through the development of a prototype and testing of transmission accuracy, hysteresis characteristics, sinusoidal response error, transmission efficiency, and reverse starting torque, the effectiveness of the proposed flexible planetary carrier in eliminating clearances, transmission accuracy improvement, transmission efficiency and reverse transmission performance promoting was verified. [ABSTRACT FROM AUTHOR]

To solve the problem that the intelligent meter box may collide in the transportation workflow, the paper studies the collision detection of the cooperative robot. Firstly, the architecture and mechanical structure of the collaborative robot intelligent meter box push–pull system is analyzed. Secondly, using the relationship between joint torque and selection LuGre model of joint friction modeling. The second-order external moment observer is constructed based on the robot’s dynamics and generalized momentum equation. Furthermore, the performance of the external moment observer is optimized, and the real-time performance and anti-interference ability of the collision detection algorithm is improved by introducing PD control and speed feedback control. Finally, the results of the designed experiments reveal that the collision detection functions are better realized using the generalized momentum observer method.

This document is a summary of a special issue in the Journal of Intelligent Manufacturing titled "Intelligent cobot systems: human-cobot collaboration in manufacturing." The special issue includes 10 papers that cover various aspects of cobot systems design and management from a human perspective. Topics covered include adaptive safety constraints, augmented reality programming, task allocation, human-robot collaboration disassembly, role transitions and adaptation, intelligent assistance systems, fault detection and diagnostics, automated visual inspection, and intelligent digital twins. The papers highlight the advantages and challenges of cobot systems in manufacturing and provide insights for future research. The authors of the document are Maurizio Faccio and Yuval Cohen. [Extracted from the article]

This study investigates the potential of a voice-controlled collaborative robot to promote computational thinking (CT) skills in preschoolers aged 4 to 6 in Southern Chile. During a week-long workshop, 34 children engaged in activities designed to develop several CT components while interacting with a robotic arm. Pre- and post-workshop assessments utilizing the TechCheck-K tool revealed significant improvements in CT skills. The findings of this preliminary study demonstrate the effectiveness of voice-controlled robots as innovative tools in early education, significantly enhancing young learners’ understanding of CT concepts, practices, and perspectives. This research advocates for further exploration of robotic integration in educational settings, highlighting their potential to improve or extend preschool learning environments and foster CT skill development.

This paper proposes a method for solving the path planning problem for a collaborative robot. The time-optimal, smooth, collision-free B-spline path is obtained by the application of a nature-inspired optimization algorithm. The proposed approach can be especially useful when moving items that are delicate or contain a liquid in an open container using a robotic arm. The goal of the optimization is to obtain the shortest execution time of the production cycle, taking into account the velocity, velocity and jerk limits, and the derivative continuity of the final trajectory. For this purpose, the velocity profiling algorithm for B-spline paths is proposed. The methodology has been applied to the production cycle optimization of the pick-and-place process using a collaborative robot. In comparison with point-to-point movement and the solution provided by the RRT* algorithm with the same velocity profiling to ensure the same motion limitations, the proposed path planning algorithm decreased the entire production cycle time by 11.28% and 57.5%, respectively. The obtained results have been examined in a simulation with the entire production cycle visualization. Moreover, the smoothness of the movement of the robotic arm has been validated experimentally using a robotic arm.

Collaborative robots are used in scenarios requiring interaction with humans. In order to improve the safety and adaptability of collaborative robots during human–robot interaction, this paper proposes a modular wire-actuated robotic arm with symmetric variable-stiffness units. The variable-stiffness unit is employed to extend the stiffness-adjustment range of the robotic arm. The variable-stiffness unit is designed based on flexure, featuring a compact and simple structure. The stiffness–force relationship of the variable-stiffness unit can be fitted by a quadratic function with an R-squared value of 0.99981, indicating weak nonlinearity. Based on the kinematics and stiffness analysis of the symmetric joint module of the robotic arm, the orientation of the joint module can be adjusted by regulating the length of the wires and the stiffness of the joint module can be adjusted by regulating the tension of the wires. Because of the actuation redundancy, the orientation and stiffness of the joint module can be adjusted synchronously. Furthermore, a direct method is proposed for the stiffness-oriented wire-tension-distribution problem of the 1-DOF joint module. A simulation is carried out to verify the proposed method. The simulation result shows that the deviation between the calculated stiffness and the desired stiffness was less than 0.005%.