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Due to the growing demand for customised products, companies have faced increasing product and process complexity levels. To address this issue, manufacturing processes should become more flexible. One of the most promising technologies to achieve this goal is collaborative robotics (or 'cobots'). In collaborative assembly processes, human and robot combine their skills. However, the co-existence of humans and cobots in the same workspace may influence the operators' perception of assembly complexity. The analysis and control of assembly complexity are crucial to achieving better performances in terms of process quality and operators' well-being. Many qualitative methods have been proposed in the literature to provide a holistic assessment of assembly complexity. This paper proposes a novel method to define a quantitative scale of perceived assembly complexity, based on Thurstone Law of Comparative Judgements. This method was applied to an experimental case-study concerning the assembly of three different products in two modalities (i.e. manual and collaborative). Regression analysis showed that the perceived complexity may be related to the occurrence of process failures and to the perceived workload. The method also proved capable of identifying assembly processes where cobot assistance was helpful, providing process designers with a supporting tool to minimise perceived complexity. [ABSTRACT FROM AUTHOR]

Human-robot collaborative disassembly lines are widely used by remanufacturing companies to disassemble end-of-life (EOL) products. When disassembling large-sized EOL products, each workstation on a disassembly line is generally divided into multiple operating positions, so that different operators can disassemble the same product at their respective positions at the same time, thereby greatly improving efficiency. This paper focuses on a position-constrained human-robot collaborative disassembly planning (PC-HRCDP) problem for the above-mentioned lines, including three subproblems of disassembly sequence planning, disassembly line balancing and robot path planning. A multi-objective mixed integer programming model for PC-HRCDP is developed to solve smallscale instances. Furthermore, a multi-objective multi-fidelity optimisation (MO-MFO) algorithm is proposed to solve large-scale instances. Comprehensive experiments are conducted based on 10 problem instances generated in this study. Experimental results show that the proposed MO-MFO is better than a high-fidelity optimisation algorithm in terms of running time. In addition, benefiting from the strategy of MO-MFO to allocate the limited high-fidelity computational budget to solutions in the two stages of multi-objective optimisation and optimal sampling, MO-MFO is significantly better than the existing representative multi-fidelity optimisation algorithms in terms of the hyper-volume and the inverted generational distance. [ABSTRACT FROM AUTHOR]

Herein, we present a real-time multi-agent deep reinforcement learning model as a disassembly planning framework for human-robot collaboration. This disassembly plan optimises sequences to minimise operation time and the disassembling costs of end-of-life (EoL) products. Combining different data-driven decision-making tools, the plan aims to handle the complexities and uncertainties of disassembly tasks. Based on the physical features and geometric limitations of EoL product components, we calculate product disassembly difficulty scores. Subsequently, the deep reinforcement learning model integrates these scores into planning process. The model allocates tasks in real time according to the online conditions of the human operator, cobot, and product, enabling the model to cope with uncertainties that may change the process routine. We also present different scenarios wherein a cobot collaborates with human operators with different skill levels. To evaluate model performance, we compare it with baseline models in terms of the convergence time and incorporated disassembly features. The analysis indicates that our model converges three times faster than a baseline model applied to the same case study. Moreover, our model includes more features of the disassembly problem in its decision-making process than any other baseline model. [ABSTRACT FROM AUTHOR]

Human–robot collaboration has been incorporated into production and assembly processes to promote system flexibility, changeability and adaptability. However, it poses new challenges to resource allocation and production scheduling due to its intrinsic uncertainty and also the increasing complexity of resources. This paper investigates a stochastic flow shop scheduling problem in the context of human–robot collaboration. The goal is to achieve efficient utilisation of flexible resources including human workers and cobots and take full advantage of human–robot collaboration in production scheduling. A stochastic Cobb–Douglas production function is utilised to evaluate the production efficiency of human–robot collaboration considering instabilities of human performance. A joint chance-constrained programming model is formulated to ensure that the required system performance can be achieved. A CVaR approximation-based approach is proposed to solve the formulated model with mixed-integer variables and a nonconvex constraint. The effectiveness of the formulated model and the efficiency of the proposed solution approach are evaluated via numerical experiments. Computational results show the superiority of our solution approach over three other approaches including Bonferroni approximation, scenario approach and individual chance-constrained programming. [ABSTRACT FROM AUTHOR]

Nowadays, the manufacturing sector needs higher levels of flexibility to confront the extremely volatile market. Accordingly, exploiting both machine and workforce reconfigurability as two critical sources of flexibility is advantageous. In this regard, for the first time, this paper explores an integrated production scheduling and workforce planning problem in a Reconfigurable Manufacturing System (RMS) benefiting from reconfigurable machines and human-robot collaboration. A new Mixed-Integer Linear Programming (MILP) model and an efficient Constraint Programming (CP) model are developed to formulate the problem, minimising the makespan as the performance metric. Due to the high complexity of the problem, the MILP model cannot handle large-sized instances. Hence, to evaluate the performance of the CP model in large-sized instances, a lower bound is derived based on the relaxation of the problem. Finally, extensive computational experiments are carried out to assess the performance of the devised MILP and CP models and provide general recommendations for managers dealing with such a complex problem. The results reveal the superiority of the CP model over the MILP model in small- and medium-sized instances. Moreover, the CP model can find high-quality solutions for large-sized instances within a reasonable computational time. [ABSTRACT FROM AUTHOR]

With the increased use of collaborative robots, a new production model of the human-robot collaborative hybrid assembly cell (HRCHAC) is becoming a new trend in customised production. Collaborative assembly between workers and robots in assembly cells can significantly increase productivity and improve the well-being of workers once the distribution of tasks and resources is optimised. This paper proposes a new integrated task allocation model to better utilise human-robot collaboration to increase productivity and improve worker well-being. The developed model enables the skills of both workers and robots to be fully utilised while ensuring economic efficiency and the effective protection of workers' physiological and psychological health. First, the product assembly process is decomposed into several assembly tasks, and the characteristics of each task are analysed. Second, a bi-objective mixed-integer planning model is developed with the objectives of minimising unit product assembly time and maximising total task matching. The ergonomics-related objectives are considered in terms of both the physiological and psychological fatigue of the worker, and relevant constraints are established. An improved NSGA-II algorithm is developed to determine the final task allocation scheme. Finally, the proposed method is applied to a real industrial case to verify the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

Referring expression comprehension (REC) system solves a task to localize objects in a given image, based on natural language expression. We propose a novel approach to adapting the pre-trained REC model for the manufacturing domain. Despite significant advances in REC research, current REC datasets fail to recognize objects from specific yet important domains such as manufacturing due to the absence of domain-specific samples during training. Thus, we introduce a synthetic data-based domain adaptation approach for REC. To adapt a REC model to the manufacturing domain, we generated a synthetic REC dataset RefMD that consists of two sub-datasets: (1) dataset for manufacturing object classification, and (2) dataset for REC adaptation to manufacturing. Each dataset serves as one step toward the REC adaptation. Adaptation of the object classification network (visual backbone) is carried out by training ResNet50 on domain-specific labeled data, while the REC adaptation completes with the adaptation of modules in RealGIN altogether. The manufacturing domain-adapted model is further enhanced with the capability to handle ambiguous referring expressions through human-in-the-loop (HITL) interaction. The experiments on 3D-printed manufacturing objects demonstrate that the interactive REC model can accurately comprehend human instructions with an 82% accuracy. This paper introduces an approach to facilitate domain adaptation using solely synthetic data for the specific case of the manufacturing domain. However, the proposed adaptation methodology can be applied to any other domain by following the same synthetic data generation process. [ABSTRACT FROM AUTHOR]

This study investigates the impact of job demands and perceived burdensomeness on employees' willingness to collaborate with service robots (SRs) in the workplace, with a focus on the moderating effects of organizational support, self-esteem, and self-development. Conducted among 200 employees in South Korea, the results show that higher job demands significantly increase perceived burdensomeness, which negatively affects employees' willingness to collaborate with SRs. Organizational support moderates the negative impact of job demands, reducing their detrimental effects, although it was less effective in buffering the reduction in social interaction. Self-esteem and self-development positively moderated the relationship between burdensomeness and collaboration. This study contributes to the understanding of sustainable workforce management by highlighting the importance of fostering personal and organizational resources to ensure that SR integration enhances both operational efficiency and employee well-being. The findings align with broader sustainability goals by promoting human–robot collaboration that optimizes resource use, enhances workforce adaptability, and maintains social and psychological balance in the workplace. [ABSTRACT FROM AUTHOR]

According to the UNHCR, 117.2 million people have been displaced in 2023, with the rate and scale of displacement surpassing the resources available to assist those impacted. Modular construction is favored over traditional methods to meet the urgent demand for affordable housing as it overcomes challenges like long project timelines, high costs, and safety and environmental issues. However, manual assembly is often slow and prone to errors, resulting in inefficiencies and higher costs. While advanced technologies could improve the process, there is limited research on automating assembly in modular construction. This research aims to assess how automation affects the time efficiency and cost of the modular construction process, aiming to improve affordable housing production by automating modular construction. The research follows a quantitative approach, collecting data through simulation to evaluate the effectiveness of integrating robotics in modular construction assembly processes. It compares the performance of automated and manual assembly processes, focusing on resource utilization, time savings, and cost implications. The results reveal that modular manufacturing automated assembly offers faster assembly times and improved cost efficiency, leading to higher productivity and lower overall construction costs. By conducting a detailed analysis, this research provides insights that can guide the integration of automation into modular construction manufacturing. [ABSTRACT FROM AUTHOR]

Hybrid robots can assist human workers in various tasks due to their integration of mobility and manipulability. The rapid diffusion of these robots in factories has significantly elevated the automation and intelligence level of manufacturing, while also brings challenges to human–robot collaboration. Traditionally, human workers need to instruct robots to perform a range of tasks by explicitly demonstrating these operations. However, this process imposes excessive burdens on workers as the tasks and environment for robots become more and more diversified and complex. To alleviate this issue, we propose an innovative robotic manipulation framework based on continual knowledge graph embedding. This framework enables hybrid robots to break free from the constraints of fixed rules set by human demonstrations, instead endowing them with inferring capability. The core idea is to utilize semantic information related to objects (such as category, material, and components) and tasks assigned to infer appropriate operational parameters for robots via a knowledge graph. These operational parameters include the suitable type of gripper, the proper area for object manipulation, and the reasonable force range for effective grasping. We conduct an experimental analysis of the proposed framework with a real-world hybrid robot, which performed 158 different tasks involving 46 objects commonly seen in industry, achieving a success rate of up to 96.8%. Furthermore, our framework can continuously enhance the adaptability of robotic operations and effectively balance the learning of new and old knowledge. This research contributes to the development of advanced robotic manipulation method in the context of industrial human–robot collaboration. [ABSTRACT FROM AUTHOR]

In human-robot collaboration (HRC), operators work side by side with collaborative robots (cobots), a new type of machines able to function safely on tasks shared with humans. Despite the increasing presence of cobots in factories, the quality of experience associated by workers with HRC is an underexplored topic. This review is focused on the mental workload (MWL) reported by operators interacting with cobots, its major sources, and the potential solutions to optimize it during HRC. Out of 165 papers identified, 23 were selected as specifically devoted to the exploration of workers' MWL in a HRC activity. Cobot motion, predictability, task organization and communication patterns emerged as the major factors contributing to operators' MWL during HRC. Endowing cobots with the capacity to meet both task demands and human needs through modulation of motion rhythm, flexible physical interaction, and more efficient communication patterns may contribute to mitigating workers' MWL. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) and brain-machine interface (BMI) are two emerging technologies that can enhance human-robot collaboration (HRC) in domains such as industry or healthcare. HAR uses sensors or cameras to capture and analyze the movements and actions of humans, while BMI uses human brain signals to decode action intentions. Both technologies face challenges impacting accuracy, reliability, and usability. In this article, we review the state-of-the-art techniques and methods for HAR and BMI and highlight their strengths and limitations. We then propose a hybrid framework that fuses HAR and BMI data, which can integrate the complementary information from the brain and body motion signals and improve the performance of human state decoding. We also discuss our hybrid method's potential benefits and implications for HRC. [ABSTRACT FROM AUTHOR]

This paper investigates the perceptions of Construction Engineering and Management instructors regarding the competencies for Human-Robot Collaboration (HRC) in construction to determine knowledge areas, skills, and abilities prioritized for facilitating effective collaboration between humans and robots in the industry. A two-round Delphi study was employed to evaluate the perceptions of construction instructors regarding HRC competencies. This study's findings revealed that human-robot collaboration knowledge areas prioritized by the instructors include HRC ethics and regulation, robot anatomy and technical specifications, construction robot applications, sensors, and task planning. The instructors prioritized skills such as task planning, application of machine learning algorithms, safety management, human-robot interface proficiency, and effective communication. HRC abilities prioritized include decision-making, continuous learning, critical thinking, attention to detail, analytical aptitude, and adaptability. This research established the competencies prioritized by the instructors for implementing HRC in the construction industry and recommended future research directions. [ABSTRACT FROM AUTHOR]

This paper focuses on improving the ergonomics of industrial workers. It addresses the critical implications of poor ergonomics, which can lead to musculoskeletal disorders over time. A novel methodology for a path-planning algorithm designed for human–robot collaboration was introduced to tackle this challenge. The algorithm's essential contribution lies in determining the most ergonomic path for a robot to guide a human's hand during task execution, facilitating a transition toward an optimized body configuration. The algorithm effectively charts the ergonomic path by adopting a Cartesian path-planning approach and employing the cell decomposition method. The methodology was implemented on a dataset of ten individuals, representing a diverse group of male and female subjects aged between 20 and 35, with one participant being left-handed. The algorithm was applied to three different activities: "stacking an item," "taking an object from a shelf," and "assembling an object by sitting over a table." The results demonstrated a significant improvement in the REBA score (as a measure of ergonomics condition) of the individuals after applying the algorithm. This outcome reinforces the efficacy of the methodology in enhancing the ergonomics of industrial workers. Furthermore, the study compared the performance of A* with three heuristic functions against Dijkstra's algorithm, aiming to identify the most effective approach for achieving optimal ergonomic paths in human–robot collaboration. The findings revealed that A* with a specific heuristic function surpassed Dijkstra's algorithm, underscoring its superiority in this context. The findings highlight the potential for optimizing human–robot collaboration and offer practical implications for designing more efficient industrial work environments. [ABSTRACT FROM AUTHOR]

Silent speech interfaces offer an alternative and efficient communication modality for individuals with voice disorders and when the vocalized speech communication is compromised by noisy environments. Despite the recent progress in developing silent speech interfaces, these systems face several challenges that prevent their wide acceptance, such as bulkiness, obtrusiveness, and immobility. Herein, the material optimization, structural design, deep learning algorithm, and system integration of mechanically and visually unobtrusive silent speech interfaces are presented that can realize both speaker identification and speech content identification. Conformal, transparent, and self‐adhesive electromyography electrode arrays are designed for capturing speech‐relevant muscle activities. Temporal convolutional networks are employed for recognizing speakers and converting sensing signals into spoken content. The resulting silent speech interfaces achieve a 97.5% speaker classification accuracy and 91.5% keyword classification accuracy using four electrodes. The speech interface is further integrated with an optical hand‐tracking system and a robotic manipulator for human‐robot collaboration in both assembly and disassembly processes. The integrated system achieves the control of the robot manipulator by silent speech and facilitates the hand‐over process by hand motion trajectory detection. The developed framework enables natural robot control in noisy environments and lays the ground for collaborative human‐robot tasks involving multiple human operators. [ABSTRACT FROM AUTHOR]

Collaborative robots, or cobots, offer a unique combination of productivity and flexibility that has led to significant growth in adoption over the past decade. Moreover, recently, there has been a shift towards a human-centered design of the workspace, known as one of the drivers of Industry 5.0, which prioritizes the well-being of operators. To achieve this, various human factors such as ergonomics, mental workload, personal skills, and capabilities need to be considered in the workspace design, and their impact on system productivity must be evaluated. The integration of a human and a cobot in the same workplace can affect the performance of the human operator, as the perception of the cobot can impact their work. This highlights the importance of taking human factors into account, as a lack of consideration in these aspects has contributed to the failure of many implementations. To link the objectives of productivity, flexibility, and human factors consideration, a dynamic real-time multi-objective task allocation strategy for collaborative assembly systems is developed. This approach considers the different characteristics of the resources and optimizes for two objectives, makespan, and energy expenditure of the operator. By using this approach, it is possible to modify the behavior of the cobot by reallocating tasks between the two resources based on the operator's current needs. In other words, if the operator appears too stressed due to time constraints or their energy rate level is too high, some of their assigned tasks can be transferred to the cobot. This helps to maintain a balanced system while reducing the operator's stress. [ABSTRACT FROM AUTHOR]

Human-Robot Collaboration (HRC) is key to achieving the flexible automation required by the mass personalization trend, especially towards human-centric intelligent manufacturing. Nevertheless, existing HRC systems suffer from poor task understanding and poor ergonomic satisfaction, which impede empathetic teamwork skills in task execution. To overcome the bottleneck, a Mixed Reality (MR) and visual reasoning-based method is proposed in this research, providing mutual-cognitive task assignment for human and robotic agents' operations. Firstly, an MR-enabled mutual-cognitive HRC architecture is proposed, with the characteristic of monitoring Digital Twins states, reasoning co-working strategies, and providing cognitive services. Secondly, a visual reasoning approach is introduced, which learns scene interpretation from the visual perception of each agent's actions and environmental changes to make task planning strategies satisfying human–robot operation needs. Lastly, a safe, ergonomic, and proactive robot motion planning algorithm is proposed to let a robot execute generated co-working strategies, while a human operator is supported with intuitive task operation guidance in the MR environment, achieving empathetic collaboration. Through a demonstration of a disassembly task of aging Electric Vehicle Batteries, the experimental result facilitates cognitive intelligence in Proactive HRC for flexible automation. [ABSTRACT FROM AUTHOR]

In the context of Industry 4.0 and Industry 5.0, the introduction of collaborative workplaces, where humans and robots work together, represents a growing trend to improve the productivity, adaptability, and flexibility of production plants. Indeed, human–robot collaboration (HRC) is a very deepened topic in the scientific community and the designing of collaborative workplaces is a challenging issue due to the high level of complexity and multidisciplinary of its features. This work tackles the complexity of collaborative workplaces and proposes a structured framework to support strategic decisions in designing. A multi-level designing framework is proposed as a supporting tool for designers. Within five domains of collaborative robotics, the elements of a collaborative workplace are identified and proposed in a framework in order to better consider human safety and working conditions during the designing process. A decomposition matrix and an adjacency matrix are used to develop a multi-level designing workflow. Finally, an interactive tool is presented, named "Smart Graph Interface" (SGI), to read and exploit the contents of the framework. The SGI is applied to three case studies from the literature, to spread out principal outcomes in terms of applicability and robustness. [ABSTRACT FROM AUTHOR]

This study examines the impact of sensor placement and multimodal sensor fusion on the performance of a Long Short-Term Memory (LSTM)-based model for human activity classification taking place in an agricultural harvesting scenario involving human-robot collaboration. Data were collected from twenty participants performing six distinct activities using five wearable inertial measurement units placed at various anatomical locations. The signals collected from the sensors were first processed to eliminate noise and then input into an LSTM neural network for recognizing features in sequential time-dependent data. Results indicated that the chest-mounted sensor provided the highest F1-score of 0.939, representing superior performance over other placements and combinations of them. Moreover, the magnetometer surpassed the accelerometer and gyroscope, highlighting its superior ability to capture crucial orientation and motion data related to the investigated activities. However, multimodal fusion of accelerometer, gyroscope, and magnetometer data showed the benefit of integrating data from different sensor types to improve classification accuracy. The study emphasizes the effectiveness of strategic sensor placement and fusion in optimizing human activity recognition, thus minimizing data requirements and computational expenses, and resulting in a cost-optimal system configuration. Overall, this research contributes to the development of more intelligent, safe, cost-effective adaptive synergistic systems that can be integrated into a variety of applications. [ABSTRACT FROM AUTHOR]

In open-field agricultural environments, the inherent unpredictable situations pose significant challenges for effective human–robot interaction. This study aims to enhance natural communication between humans and robots in such challenging conditions by converting the detection of a range of dynamic human movements into specific robot actions. Various machine learning models were evaluated to classify these movements, with Long Short-Term Memory (LSTM) demonstrating the highest performance. Furthermore, the Robot Operating System (ROS) software (Melodic Version) capabilities were employed to interpret the movements into certain actions to be performed by the unmanned ground vehicle (UGV). The novel interaction framework exploiting vision-based human activity recognition was successfully tested through three scenarios taking place in an orchard, including (a) a UGV following the authorized participant; (b) GPS-based navigation to a specified site of the orchard; and (c) a combined harvesting scenario with the UGV following participants and aid by transporting crates from the harvest site to designated sites. The main challenge was the precise detection of the dynamic hand gesture "come" alongside navigating through intricate environments with complexities in background surroundings and obstacle avoidance. Overall, this study lays a foundation for future advancements in human–robot collaboration in agriculture, offering insights into how integrating dynamic human movements can enhance natural communication, trust, and safety. [ABSTRACT FROM AUTHOR]

An assembly line requires flexibility due to a shorter product life cycle. A way to increase flexibility is to utilize collaborative robots or cobots. Due to frequent product changes, redesigning an assembly line requires an efficient algorithm. This research aims to develop a genetic algorithm (GA) for solving a human-cobots assembly line design. The setup time of cobots is considered due to the flexibility of conducting multiple tasks by exchanging tools / end-effectors. The main contribution of the research is the efficient GA for solving assembly lines considering setup time. Secondly, the study proposed an upper limit parameter that enables faster computation without sacrificing the quality of the solution. The computational results showed that the algorithm could achieve an optimal solution with the number of tasks less than 35. Experiments of several data prove the proposed GA obtained solutions with an average gap of 3.83% to the optimal solution. Also, a faster computation time with an average difference of 64.66%. The proposed GA obtained a reasonable solution with fast computing time that helps improve efficiency and effectiveness in decision-making related to frequent redesigning of assembly lines. [ABSTRACT FROM AUTHOR]

Achieving safe collaboration between humans and robots in an industrial work-cell requires effective communication. This can be achieved through a robot perception system developed using data-driven machine learning. The challenge for human–robot communication is the availability of extensive, labelled datasets for training. Due to the variations in human behaviour and the impact of environmental conditions on the performance of perception models, models trained on standard, publicly available datasets fail to generalize well to domain and application-specific scenarios. Thus, model personalization involving the adaptation of such models to the individual humans involved in the task in the given environment would lead to better model performance. A novel framework is presented that leverages robust modes of communication and gathers feedback from the human partner to auto-label the mode with the sparse dataset. The strength of the contribution lies in using in-commensurable multimodes of inputs for personalizing models with user-specific data. The personalization through feedback-enabled human–robot communication (PF-HRCom) framework is implemented on the use of facial expression recognition as a safety feature to ensure that the human partner is engaged in the collaborative task with the robot. Additionally, PF-HRCom has been applied to a real-time human–robot handover task with a robotic manipulator. The perception module of the manipulator adapts to the user's facial expressions and personalizes the model using feedback. Having said that, the framework is applicable to other combinations of multimodal inputs in human–robot collaboration applications. [ABSTRACT FROM AUTHOR]

Collaborative human-robot task execution approaches require mutual adaptation, allowing both the human and robot partners to take active roles in action selection and role assignment to achieve a single shared goal. Prior works have utilized a leader-follower paradigm in which either agent must follow the actions specified by the other agent. We introduce the User-aware Hierarchical Task Planning (UHTP) framework, a communication-free human-robot collaborative approach for adaptive execution of multi-step tasks that moves beyond the leader-follower paradigm. Specifically, our approach enables the robot to observe the human, perform actions that support the human's decisions, and actively select actions that maximize the expected efficiency of the collaborative task. In turn, the human chooses actions based on their observation of the task and the robot, without being dictated by a scheduler or the robot. We evaluate UHTP both in simulation and in a human subjects experiment of a collaborative drill assembly task. Our results show that UHTP achieves more efficient task plans and shorter task completion times than non-adaptive baselines across a wide range of human behaviors, that interacting with a UHTP-controlled robot reduces the human's cognitive workload, and that humans prefer to work with our adaptive robot over a fixed-policy alternative. [ABSTRACT FROM AUTHOR]

Remote robot manipulation with human control enables applications in which safety and environmental constraints are adverse to humans (e.g., underwater, space robotics and disaster response) or the complexity of the task demands human-level cognition and dexterity (e.g., robotic surgery and manufacturing). These systems typically use direct teleoperation at the motion level and are usually limited to low-DOF arms and two-dimensional (2D) perception. Improving dexterity and situational awareness demands new interaction and planning workflows. We explore the use of human–robot teaming through teleautonomy with assisted planning for remote control of a dual-arm dexterous robot for multi-step manipulation, and conduct a within-subjects experimental assessment (n = 12 expert users) to compare it with direct teleoperation with an imitation controller with 2D and three-dimensional (3D) perception, as well as teleoperation through a teleautonomy interface. The proposed assisted planning approach achieves task times comparable with direct teleoperation while improving other objective and subjective metrics, including re-grasps, collisions, and TLX workload. Assisted planning in the teleautonomy interface achieves faster task execution and removes a significant interaction with the operator's expertise level, resulting in a performance equalizer across users. Our study protocol, metrics, and models for statistical analysis might also serve as a general benchmarking framework in teleoperation domains. Accompanying video and reference R code: https://people.csail.mit.edu/cdarpino/THRIteleop/ [ABSTRACT FROM AUTHOR]

With the rise in customized product demands, the production of small batches with a wide variety of products is becoming more common. A high degree of flexibility is required from operators to manage changes in volumes and products, which has led to the use of Human-Robot Collaboration (HRC) systems for custom manufacturing. However, this variety introduces complexity that affects production time, cost, and quality. To address this issue, multivariate control charts are used as diagnostic tools to evaluate the stability of several parameters related to both product/process and human well-being in HRC systems. These key parameters monitored include assembly time, quality control time, total defects, and operator stress, providing a more holistic view of system performance. Real-time monitoring of process performance along with human-related factors, which is rarely considered in statistical process control, provides comprehensive stability control over all customized product variants produced in the HRC system. The proposed approach includes defining the parameters to be monitored, constructing control charts, collecting data after product variant assembly, and verifying that the set of parameters is under control via control charts. This increases the system's responsiveness to both process inefficiencies and human well-being. The procedure can be automated by embedding control chart routines in the software of the HRC system or its digital twin, without adding additional tasks to the operator's workload. Its practicality and effectiveness are evidenced in custom electronic board assembly, highlighting its role in optimizing HRC system performance. [ABSTRACT FROM AUTHOR]

Collaborative robotics possesses the potential to revolutionise industrial automation by offering affordable and accessible solutions with reasonable skill requirements. However, identifying the most valuable and appropriate applications for this technology remains a challenge. This study conducted a comprehensive literature review to analyse the existing collaborative robotics applications, and the results showed that only a limited number of applications can be considered true collaboration, with even fewer classified as intelligent collaboration. The study comprised a survey designed to offer valuable insights to not only enhance the state-of-the-art analysis in the identification of existing challenges in the field of collaborative robotics but also to provide motivation to guide future advancements. By leveraging the survey results, researchers and practitioners will be better equipped to navigate the complex landscape of collaborative robotics and develop innovative solutions to tackle the identified challenges. This study also informs on the latest research and development in the field. [ABSTRACT FROM AUTHOR]

ARIAC is a robotic simulation competition promoted by NIST annually since 2017, aiming to present competitors’ with contemporary industry problems to be solved using agile robotics. For the 2023 competition, ARIAC competitors must perform assembly and kitting tasks by controlling four autonomous ground vehicles (AGVs), one floor-based robot, and one ceiling-based (Gantry) robot in an attempt to overcome a range of agility challenges in the supplied simulated environment, itself based on the Robot Operating System (ROS 2) and Gazebo. The 2023 competition also included a “human” agility challenge, comprising a (simulated) human operator working among robots on the factory floor. This development was motivated by the fact that, while robots and automation play an increasingly significant role in modern manufacturing, there still remains a close relationship between machines and humans. They should complement each other’s strengths and cover each other’s limitations while also observing any required safety rules. For example, the ISO standard “Robots and Robotic Devices – Collaborative robots” (ISO 15066:2016) prescribes the distances required between humans and robots. Within the ARIAC simulation environment, each human operator is controlled using autonomous Belief-Desire-Intention (BDI) agents. At the same time, competitors can monitor the position of each human operator at any time by subscribing to the relevant ROS topic. In this article, we analyse the effects of this (simulated) human presence in the 2023 ARIAC competition and perform a detailed analysis of how the three different human personalities that were implemented affect the assembly tasks undertaken at the four different locations of the assembly stations. Given how the system is currently implemented, it appears that the influence of each encoded personality on the competitors is not as predictable as anticipated. We expand on why this may be a problem when addressing real collaborative spaces involving humans and industrial robots and the improvements that can be undertaken to mitigate the ensuing problems. [ABSTRACT FROM AUTHOR]

Human-Robot Interaction is an increasingly important topic in both research and industry fields. Since human safety must be always guaranteed and accidental contact with the operator avoided, it is necessary to investigate real-time obstacle avoidance strategies. The transfer from simulation environments, where algorithms are tested, to the real world is challenging from different points of view, e.g., the continuous tracking of the obstacle and the configuration of different manipulators. In this paper, the authors describe the implementation of a collision avoidance strategy based on the potential field method for off-line trajectory planning and on-line motion control, paired with the Motion Capture system Optitrack PrimeX 22 for obstacle tracking. Several experiments show the performance of the proposed strategy in the case of a fixed and dynamic obstacle, disturbing the robot’s trajectory from multiple directions. Two different avoidance modalities are adapted and tested for both standard and redundant robot manipulators. The results show the possibility of safely implementing the proposed avoidance strategy on real systems. [ABSTRACT FROM AUTHOR]

In the Industry 4.0 scenario, human–robot collaboration (HRC) plays a key role in factories to reduce costs, increase production, and help aged and/or sick workers maintain their job. The approaches of the ISO 11228 series commonly used for biomechanical risk assessments cannot be applied in Industry 4.0, as they do not involve interactions between workers and HRC technologies. The use of wearable sensor networks and software for biomechanical risk assessments could help us develop a more reliable idea about the effectiveness of collaborative robots (coBots) in reducing the biomechanical load for workers. The aim of the present study was to investigate some biomechanical parameters with the 3D Static Strength Prediction Program (3DSSPP) software v.7.1.3, on workers executing a practical manual material-handling task, by comparing a dual-arm coBot-assisted scenario with a no-coBot scenario. In this study, we calculated the mean and the standard deviation (SD) values from eleven participants for some 3DSSPP parameters. We considered the following parameters: the percentage of maximum voluntary contraction (%MVC), the maximum allowed static exertion time (MaxST), the low-back spine compression forces at the L4/L5 level (L4Ort), and the strength percent capable value (SPC). The advantages of introducing the coBot, according to our statistics, concerned trunk flexion (SPC from 85.8% without coBot to 95.2%; %MVC from 63.5% without coBot to 43.4%; MaxST from 33.9 s without coBot to 86.2 s), left shoulder abdo-adduction (%MVC from 46.1% without coBot to 32.6%; MaxST from 32.7 s without coBot to 65 s), and right shoulder abdo-adduction (%MVC from 43.9% without coBot to 30.0%; MaxST from 37.2 s without coBot to 70.7 s) in Phase 1, and right shoulder humeral rotation (%MVC from 68.4% without coBot to 7.4%; MaxST from 873.0 s without coBot to 125.2 s), right shoulder abdo-adduction (%MVC from 31.0% without coBot to 18.3%; MaxST from 60.3 s without coBot to 183.6 s), and right wrist flexion/extension rotation (%MVC from 50.2% without coBot to 3.0%; MaxST from 58.8 s without coBot to 1200.0 s) in Phase 2. Moreover, Phase 3, which consisted of another manual handling task, would be removed by using a coBot. In summary, using a coBot in this industrial scenario would reduce the biomechanical risk for workers, particularly for the trunk, both shoulders, and the right wrist. Finally, the 3DSSPP software could be an easy, fast, and costless tool for biomechanical risk assessments in an Industry 4.0 scenario where ISO 11228 series cannot be applied; it could be used by occupational medicine physicians and health and safety technicians, and could also help employers to justify a long-term investment. [ABSTRACT FROM AUTHOR]

Abstract Human–robot collaboration (HRC) is a novel manufacturing paradigm designed to fully leverage the advantage of humans and robots, efficiently and flexibly accomplishing customized manufacturing tasks. However, existing HRC systems lack the transfer and generalization capability for environment perception and task reasoning. These limitations manifest in: (1) current methods rely on specialized models to perceive scenes; and need retraining the model when facing unseen objects. (2) current methods only address predefined tasks, and cannot support undefined task reasoning. To avoid these limitations, this paper proposes a novel HRC approach based on Foundation Models (FMs), including Large Language models (LLMs) and Vision Foundation Models (VFMs). Specifically, a LLMs-based task reasoning method is introduced, utilizing prompt learning to transfer LLMs into the domain of HRC tasks, supporting undefined task reasoning. A VFMs-based scene semantic perception method is proposed, integrating various VFMs to achieve scene perception without training. Finally, a FMs-based HRC system is developed, comprising perception, reasoning, and execution modules for more flexible and generalized HRC. The superior performances of FMs in perception and reasoning are demonstrated by extensive experiments. Furthermore, the feasibility and effectiveness of the FMs-based HRC system are validated through an part assembly case involving a satellite component model.

Taiwan has been developing digital technology for many years. Whether it is industry or government, Taiwan's digitalisation level is among the highest in the world. This research aims to show how the application of chatbots and AI technology affects Taiwan's local governments, what problems have arisen from its operations so far, and what key factors provide a better understanding of the effectiveness of chatbots. Contributions to the transformation of existing digital governments can be used to strengthen governance and political participation. This article adopts a case study approach. The research findings are based on the evaluation model constructed by literature review and in-depth interviews; this ideal model examined the operational effectiveness of existing local government chatbots. Through in-depth interviews, we discovered that human-robot collaboration would be the most beneficial model for using artificial intelligence in the future. In order to maximise the benefits of human-robot collaboration, government agencies must enhance organisational members' understanding of artificial intelligence through education and training to help cross-domain communication and coordination activities with entrusted units and through education and training to build an organisational culture that supports the organisation's digital transformation. [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]

The objective of Assembly Line Balancing (ALB) is to find the proper assignment of tasks to workstations, taking into consideration various types of constraints and defined management goals. Early research in the field focused on solving the Simple Assembly Line Balancing problem, a basic simplified version of the general problem. As the production environment became more complex, several new ALB problem types appeared, and almost all ALB problems are NP-hard, meaning that finding a solution requires a lot of time, resources, and computational power. Methods with custom-made algorithms and generic approaches have been developed for solving these problems. While custom-made algorithms are generally more efficient, generic approaches can be more easily extended to cover other variations of the problem. Over the past few decades, automation has played an increasingly important role in various operations, although complete automation is often not possible. As a result, there is a growing need for partially automated assembly line balancing models. In these circumstances, the flexibility of a generic approach is essential. This paper compares two generic approaches: mixed integer linear programming (MILP) and constraint programming (CP), for two types of partially automated assembly line balancing problems. While CP is relatively slower in solving the simpler allocation problems, it is more efficient than MILP when an increased number of constraints is applied to the ALB and an allocation and scheduling problem needs to be solved. [ABSTRACT FROM AUTHOR]

In open-field agricultural environments, the inherent unpredictable situations pose significant challenges for effective human–robot interaction. This study aims to enhance natural communication between humans and robots in such challenging conditions by converting the detection of a range of dynamic human movements into specific robot actions. Various machine learning models were evaluated to classify these movements, with Long Short-Term Memory (LSTM) demonstrating the highest performance. Furthermore, the Robot Operating System (ROS) software (Melodic Version) capabilities were employed to interpret the movements into certain actions to be performed by the unmanned ground vehicle (UGV). The novel interaction framework exploiting vision-based human activity recognition was successfully tested through three scenarios taking place in an orchard, including (a) a UGV following the authorized participant; (b) GPS-based navigation to a specified site of the orchard; and (c) a combined harvesting scenario with the UGV following participants and aid by transporting crates from the harvest site to designated sites. The main challenge was the precise detection of the dynamic hand gesture “come” alongside navigating through intricate environments with complexities in background surroundings and obstacle avoidance. Overall, this study lays a foundation for future advancements in human–robot collaboration in agriculture, offering insights into how integrating dynamic human movements can enhance natural communication, trust, and safety.

Abstract High-density electromyography (HD-EMG) can provide a natural interface to enhance human–computer interaction (HCI). This study aims to demonstrate the capability of a novel HD-EMG forearm sleeve equipped with up to 150 electrodes to capture high-resolution muscle activity, decode complex hand gestures, and estimate continuous hand position via joint angle predictions. Ten able-bodied participants performed 37 hand movements and grasps while EMG was recorded using the HD-EMG sleeve. Simultaneously, an 18-sensor motion capture glove calculated 23 joint angles from the hand and fingers across all movements for training regression models. For classifying across the 37 gestures, our decoding algorithm was able to differentiate between sequential movements with $$97.3 \pm 0.3\%$$ 97.3 ± 0.3 % accuracy calculated on a 100 ms bin-by-bin basis. In a separate mixed dataset consisting of 19 movements randomly interspersed, decoding performance achieved an average bin-wise accuracy of $$92.8 \pm 0.8\%$$ 92.8 ± 0.8 % . When evaluating decoders for use in real-time scenarios, we found that decoders can reliably decode both movements and movement transitions, achieving an average accuracy of $$93.3 \pm 0.9\%$$ 93.3 ± 0.9 % on the sequential set and $$88.5 \pm 0.9\%$$ 88.5 ± 0.9 % on the mixed set. Furthermore, we estimated continuous joint angles from the EMG sleeve data, achieving a $$R^2$$ R 2 of $$0.884 \pm 0.003$$ 0.884 ± 0.003 in the sequential set and $$0.750 \pm 0.008$$ 0.750 ± 0.008 in the mixed set. Median absolute error (MAE) was kept below 10° across all joints, with a grand average MAE of $$1.8 \pm 0.04^\circ$$ 1.8 ± 0 . 04 ∘ and $$3.4 \pm 0.07^\circ$$ 3.4 ± 0 . 07 ∘ for the sequential and mixed datasets, respectively. We also assessed two algorithm modifications to address specific challenges for EMG-driven HCI applications. To minimize decoder latency, we used a method that accounts for reaction time by dynamically shifting cue labels in time. To reduce training requirements, we show that pretraining models with historical data provided an increase in decoding performance compared with models that were not pretrained when reducing the in-session training data to only one attempt of each movement. The HD-EMG sleeve, combined with sophisticated machine learning algorithms, can be a powerful tool for hand gesture recognition and joint angle estimation. This technology holds significant promise for applications in HCI, such as prosthetics, assistive technology, rehabilitation, and human–robot collaboration.

In the vision of Industry 5.0, collaborative robots (or cobots) play a central supporting role in various industries, especially manufacturing. Close interaction with cobots requires special attention to user experience to fully exploit the benefits of this paradigm. Consequently, understanding the impact of a cobot's physical size on user experience becomes critical to optimizing human–robot collaboration (HRC). This research aims to investigate the relationship between cobot size (UR3e – small cobot vs. UR10e – large cobot) and user experience in HRC contexts, in conjunction with other factors (i.e., cobot movement speed and product assembly complexity). Through a series of controlled experiments involving 32 participants, user experience data were obtained by collecting physiological measures (i.e., electro-dermal activity, heart activity, eye-tracking metrics) and subjective responses with questionnaires (i.e., perceived workload, interaction quality, and affective state). Results showed that the large cobot was generally perceived to be safer, more natural, efficient, fluid, and trustworthy. With the large cobot, there was a decrease in dominance; however, it was offset by the learning effect. Perceived workload was mainly influenced by product complexity. No clear difference in terms of mental strain emerged from the physiological data comparing the cobot sizes. In addition, the interaction term between cobot size and cobot movement speed never emerged as significant. The results of this research can offer practical insights to improve the effectiveness and acceptance of cobots during the implementation phase. [ABSTRACT FROM AUTHOR]

Improving risk assessment (RA) for human-robot collaboration (HRC) is crucial, given challenges in existing RA tools. For example, simulation and testing RA tools lack realism due to simplified models, limited dynamic realism, and sensor integration constraints. This study explores how Artificial Intelligence (AI) can enhance risk assessment methods. Using a systematic literature review, the study analyzes three risk assessment methods: PFMEA, HAZOP, and FTA to which AI has been integrated. Results highlight strengths (e.g., systematic process failure identification, thorough hazard identification, complex system modeling) and limitations (e.g., limited coverage of human-robot dynamics, reliance on historical data, adaptability constraints, resource intensity, design dependency, complexity, human factor oversight, data dependency) in addressing HRC risks. Challenges and opportunities of AI in risk assessment are identified, emphasizing reliability, adaptation, safety, and method accuracy. Adhering to standards significantly improves the trustworthiness of AI-driven risk assessments, ensuring consistent, and validated results across diverse HRC scenarios. The study proposes a hybrid approach that combines multiple methods and incorporates image processing as a practical AI feature to enhance risk detection and prevention. The study advances AI-based risk assessment for HRC, offering a comprehensive overview of the current state of the art, highlighting gaps for future research, and suggesting a holistic solution. [ABSTRACT FROM AUTHOR]

High-density electromyography (HD-EMG) can provide a natural interface to enhance human–computer interaction (HCI). This study aims to demonstrate the capability of a novel HD-EMG forearm sleeve equipped with up to 150 electrodes to capture high-resolution muscle activity, decode complex hand gestures, and estimate continuous hand position via joint angle predictions. Ten able-bodied participants performed 37 hand movements and grasps while EMG was recorded using the HD-EMG sleeve. Simultaneously, an 18-sensor motion capture glove calculated 23 joint angles from the hand and fingers across all movements for training regression models. For classifying across the 37 gestures, our decoding algorithm was able to differentiate between sequential movements with 97.3 ± 0.3 % accuracy calculated on a 100 ms bin-by-bin basis. In a separate mixed dataset consisting of 19 movements randomly interspersed, decoding performance achieved an average bin-wise accuracy of 92.8 ± 0.8 % . When evaluating decoders for use in real-time scenarios, we found that decoders can reliably decode both movements and movement transitions, achieving an average accuracy of 93.3 ± 0.9 % on the sequential set and 88.5 ± 0.9 % on the mixed set. Furthermore, we estimated continuous joint angles from the EMG sleeve data, achieving a R 2 of 0.884 ± 0.003 in the sequential set and 0.750 ± 0.008 in the mixed set. Median absolute error (MAE) was kept below 10° across all joints, with a grand average MAE of 1.8 ± 0. 04 ∘ and 3.4 ± 0. 07 ∘ for the sequential and mixed datasets, respectively. We also assessed two algorithm modifications to address specific challenges for EMG-driven HCI applications. To minimize decoder latency, we used a method that accounts for reaction time by dynamically shifting cue labels in time. To reduce training requirements, we show that pretraining models with historical data provided an increase in decoding performance compared with models that were not pretrained when reducing the in-session training data to only one attempt of each movement. The HD-EMG sleeve, combined with sophisticated machine learning algorithms, can be a powerful tool for hand gesture recognition and joint angle estimation. This technology holds significant promise for applications in HCI, such as prosthetics, assistive technology, rehabilitation, and human–robot collaboration. [ABSTRACT FROM AUTHOR]

For nearly three decades, researchers have explored the use of augmented reality for facilitating collaboration between humans and robots. In this survey paper, we review the prominent, relevant literature published since 2008, the last date that a similar review article was published. We begin with a look at the various forms of the augmented reality (AR) technology itself, as utilized for human–robot collaboration (HRC). We then highlight specific application areas of AR for HRC, as well as the main technological contributions of the literature. Next, we present commonly used methods of evaluation with suggestions for implementation. We end with a look towards future research directions for this burgeoning field. This review serves as a primer and comprehensive reference for those whose work involves the combination of augmented reality with any kind of human–robot collaboration. [ABSTRACT FROM AUTHOR]

The extreme radiation environment in nuclear power plant leads to leakage of nuclear pipe equipment. It is important to repair the pipe equipment timely for nuclear safety. Manual repair on field incurs a health risk to worker. The use of robot for remote repair shows an excellent prospect. Therefore, the multirobot system (MRS) was developed and designed for field remote repair of nuclear reprocessing pipe equipment. The sensor modules and composite control strategy were developed for remote repair in the radiation environment. The remote repair of nuclear pipe equipment including both overall and partial repair has been achieved. The remote repair of the MRS is successfully performed on a full‐size stainless steel pipe model. The experimental results show that the MRS performed 63 processes with an overall time of 12,798 s, while the manual repair requires multiple people to spend many days to complete the repair. Moreover, the MRS achieves great repair quality and appearance. The MRS achieves excellent performance in remote repair of nuclear pipe equipment under extreme radiation conditions, demonstrating its capability for effective and safe operation. [ABSTRACT FROM AUTHOR]

The current building industry is facing challenges of labor shortages and labor-intensive practices. Effectively collaborating with robots will be crucial for industry upgrading. This research introduces a MR iterative design and robot-assisted construction mode based on human–robot collaboration, facilitating an integrated process innovation from design to construction. The development of the ROCOS (Robot Collaboration System) comprises three key aspects: (1) Layout Stage: using MR technology to layout the site, forming a full-scale integrated virtual and physical digital twin design environment. (2) Design Stage: conducting virtual iterative design in the digital twin environment and automatically simulating assembly processes. (3) Assembly Stage: translating simulated results into assembly path commands and driving a robotic arm to perform actual assembly. In the end, this research setup two experiments to examine the feasibility of this iterative design–construction loop script. The results showed that although the presence of obstacles reduced the designer's freedom and increased the number of steps, the designer could still finish both tasks. This means that the ROCOS has value in the prototype of human–robot collaboration. In addition, some valuable findings from users' feedback showed that potential improvements can be addressed in operability, customization, and real construction scenarios. [ABSTRACT FROM AUTHOR]

During the transition from Industry 4.0 to Industry 5.0, industrial robotics technology faces the need for intelligent and highly integrated development. Metaverse technology creates immersive and interactive virtual environments, allowing technicians to perform simulations and experiments in the virtual world, and overcoming the limitations of traditional industrial operations. This paper explores the application and evolution of metaverse technology in the field of industrial robotics, focusing on the realization of virtual–real integration and human–machine collaboration. It proposes a design framework for a virtual–real interaction system based on the ROS and WEB technologies, supporting robot connectivity, posture display, coordinate axis conversion, and cross-platform multi-robot loading. This paper emphasizes the study of two key technologies for the system: virtual–real model communication and virtual–real model transformation. A general communication mechanism is designed and implemented based on the ROS, using the ROS topic subscription to achieve connection and real-time data communication between physical robots and virtual models, and utilizing URDF model transformation technology for model invocation and display. Compared with traditional simulation software, i.e., KUKA Sim PRO (version 1.1) and RobotStudio (version 6.08), the system improves model loading by 45.58% and 24.72%, and the drive response by 41.50% and 28.75%. This system not only supports virtual simulation and training but also enables the operation of physical industrial robots, provides persistent data storage, and supports action reproduction and offline data analysis and decision making. [ABSTRACT FROM AUTHOR]

Collaborative robotics is a major topic in current robotics research, posing new challenges, especially in human–robot interaction. The main aspect in this area of research focuses on understanding the behavior of robots when engaging with humans, where reinforcement learning is a key discipline that allows us to explore sophisticated emerging reactions. This review aims to delve into the relevance of different sensors and techniques, with special attention to EEG (electroencephalography data on brain activity) and its influence on the behavior of robots interacting with humans. In addition, mechanisms available to mitigate potential risks during the experimentation process such as virtual reality are also be addressed. In the final part of the paper, future lines of research combining the areas of collaborative robotics, reinforcement learning, virtual reality, and human factors are explored, as this last aspect is vital to ensuring safe and effective human–robot interactions. [ABSTRACT FROM AUTHOR]

As resources become increasingly scarce and environmental demands grow, the recycling of products at the end of their lifecycle becomes crucial. Disassembly, as a key stage in the recycling process, plays a decisive role in the sustainability of the entire operation. Advances in automation technology and the integration of Industry 5.0 principles make the balance of human–robot collaborative disassembly lines an important research topic. This study uses disassembly-precedence graphs to clarify disassembly-task information and converts it into a task-precedence matrix. This matrix includes both symmetry and asymmetry, reflecting the dependencies and independencies among disassembly tasks. Based on this, we develop a multiobjective optimisation model that integrates disassembly-task allocation, operation mode selection, and the use of collaborative robots. The objectives are to minimise the number of workstations, the idle rate of the disassembly line, and the energy consumption. Given the asymmetry in disassembly-task attributes, such as the time differences required for disassembling various components and the diverse operation modes, this study employs an evolutionary algorithm to address potential asymmetric optimisation problems. Specifically, we introduce an advanced multi-objective discrete bee algorithm and validate its effectiveness and superiority for solving the disassembly-line balancing problem through a comparative analysis with other algorithms. This research not only provides innovative optimisation strategies for the product-recycling field but also offers valuable experience and reference for the further development of industrial automation and human–robot collaboration. [ABSTRACT FROM AUTHOR]

The use and the rapid growth of the cobot in industry are changing working conditions. New jobs can imply new advantages and inconveniences, which call for new occupational risk assessments. The aim here is to assess occupational risks in terms of mental stress, so as to determine whether a worker experiences greater stress when working in collaboration with a cobot rather than with another person while performing the same production-line process. The study involved a total of 32 volunteers of various ages, with no previous experience of cobots. An eye-tracker system that records a range of biometric data was used to quantify stress. Pupil diameter was mainly used in this investigation, as well as the number of gaze fixations by zones. The data registered were analyzed using the T-test method, with which data on two groups can be compared to test for significant differences. In addition, other secondary parameters were also analyzed, such as the time required to complete each test, and the number of errors that were committed. Among the most important conclusions, it was noted that working with cobots in no way increased stress levels, confirming one of the objectives for which these robots were designed. [ABSTRACT FROM AUTHOR]

The advent of collaborative robots allows humans and robots to cooperate in a direct and physical way. While this leads to amazing new opportunities to create novel robotics applications, it is challenging to make the collaboration intuitive for the human. From a system’s perspective, understanding the human intentions seems to be one promising way to get there. However, human behavior exhibits large variations between individuals, such as for instance preferences or physical abilities. This paper presents a novel concept for simultaneously learning a model of the human intentions and preferences incrementally during collaboration with a robot. Starting out with a nominal model, the system acquires collaborative skills step-by-step within only very few trials. The concept is based on a combination of model-based reinforcement learning and inverse reinforcement learning, adapted to fit collaborations in which human and robot think and act independently. We test the method and compare it to two baselines: one that imitates the human and one that uses plain maximum entropy inverse reinforcement learning, both in simulation and in a user study with a Franka Emika Panda robot arm. [ABSTRACT FROM AUTHOR]

This paper introduces DAC-HRC, a novel cognitive architecture designed to optimize human-robot collaboration (HRC) in industrial settings, particularly within the context of Industry 4.0. The architecture is grounded in the Distributed Adaptive Control theory and the principles of joint intentionality and interdependence, which are key to effective HRC. Joint intentionality refers to the shared goals and mutual understanding between a human and a robot, while interdependence emphasizes the reliance on each other's capabilities to complete tasks. DAC-HRC is applied to a hybrid recycling plant for the disassembly and recycling of Waste Electrical and Electronic Equipment (WEEE) devices. The architecture incorporates several cognitive modules operating at different timescales and abstraction levels, fostering adaptive collaboration that is personalized to each human user. The effectiveness of DAC-HRC is demonstrated through several pilot studies, showcasing functionalities such as turn-taking interaction, personalized error-handling mechanisms, adaptive safety measures, and gesture-based communication. These features enhance human-robot collaboration in the recycling plant by promoting real-time robot adaptation to human needs and preferences. The DAC-HRC architecture aims to contribute to the development of a new HRC paradigm by paving the way for more seamless and efficient collaboration in Industry 4.0 by relying on socially adept cognitive architectures. [ABSTRACT FROM AUTHOR]

Industry 5.0 envisions a future where seamless collaboration between humans and robots enhances efficiency, innovation, and coevolution. While collaborative robots have found widespread applications in manufacturing, particularly in tasks like pick-and-place and assembly, their integration into handcrafted manufacturing processes presents unique challenges. This article focuses on advancing technology in the less-explored field of cobot-assisted handcrafted manufacturing, specifically in the fashion industry, with a priority on reducing work-related risks. In handcrafted processes, which often involve intricate and artistic work, cobots face challenges related to nuanced decision-making, adaptability to customizations, and the need for precise manual dexterity. The study delves into the cobot-assisted polishing of leather shoes, addressing issues associated with product delicacy, process and knowledge formalization, versatility, and integration into existing manufacturing processes. To overcome these challenges, the research proposes the application of cobots in the initial polishing phase, which is the most physically demanding, allowing artisans to focus on finalization, quality control, and process supervision. The study also applies the concept of human-in-the-loop (HITL) and virtual reality simulation to optimize collaboration, ensuring safety, ergonomics, and efficiency. The article contributes to the scientific and industrial communities by pioneering the study of collaborative robotics in craftsmanship, successfully implementing human–robot collaboration (HRC) in an industrial setting, demonstrating the effectiveness of virtual simulation and HITL, and prioritizing human factors throughout the design and development of HRC. The insights gained from this research are crucial for achieving practical solutions in industrial environments while aligning with the performance objectives of companies and workers' well-being. [ABSTRACT FROM AUTHOR]

The article delves into the crucial role of effective human-robot interactions in collaborative tasks, exploring diverse communication channels like hearing, speech, sight, touch, and learning. Topics include human-robot collaboration mirroring human-human collaboration, brain-computer interfaces, and emotional intelligence perception in robotics.