Human-Robot Collaboration in a Smart Industry Context: Does HRM Matter?

Twin Systems (DTs) on Human-Robot Collaboration (HRC) within the context of Industry 5.0, drawing insights from 18 selected studies. It highlights three primary applications of DTs in HRC: optimizing production workflows through real-time simulation and analysis for efficiency enhancement, leveraging real-time data for dynamic task allocation to increase system flexibility, and improving worker safety by intelligently managing robot movements based on safety distance monitoring. In order to serve as a reference for upcoming researchers, this study provides an overview of recent research accomplishments in the primary applications of digital twin systems in HRC. This research explores Digital Twin Systems (DTs) in Human-Robot Collaboration (HRC) in Industry 5.0 in detail, emphasizing how they might improve productivity, adaptability, and safety. Furthermore, while examining the potential paths for digital twin evolution, the study's findings provide insightful information for upcoming investigations and applications pertaining to human-robot cooperation. This paper emphasizes how crucial DTs are to the development of intelligent, effective, and safe industrial processes.

PurposeIn the present era of Industry 4.0, the manufacturing automation is moving toward mass production and mass customization through human–robot collaboration. The purpose of this paper is to describe various human–robot collaborative (HRC) techniques and their applicability for various manufacturing methods along with key challenges.Design/methodology/approachNumerous recent relevant research literature has been analyzed, and various human–robot interaction methods have been identified, and detailed discussions are made on one- and two-way human–robot collaboration.FindingsThe challenges in implementing human–robot collaboration for various manufacturing process and the challenges in one- and two-way collaboration between human and robot are found and discussed.Originality/valueThe authors have attempted to classify the HRC techniques and demonstrated the challenges in different modes.

Within the context of Industry 4.0 and of the new emerging Industry 5.0, human factors are becoming increasingly important, especially in Human-Robot Collaboration (HRC). This paper provides a novel study focused on the human aspects involved in industrial HRC by exploring the effects of various HRC setting factors. In particular, this paper aims at investigating the impact of industrial HRC on user experience, affective state, and stress, assessed through both subjective measures (i.e., questionnaires) and objective ones (i.e., physiological signals). A collaborative assembly task was implemented with different configurations, in which the robot movement speed, the distance between the operator and the robot workspace, and the control of the task execution time were varied. Forty-two participants were involved in the study and provided feedbacks on interaction quality and their affective state. Participants’ physiological responses (i.e., electrodermal activity and heart rate) were also collected non-invasively to monitor the amount of stress generated by the interaction. Analysis of both subjective and objective responses revealed how the configuration factors considered influence them. Robot movement speed and control of the task execution time resulted to be the most influential factors. The results also showed the need for customization of HRC to improve ergonomics, both psychological and physical, and the well-being of the operator.

The collaboration between humans and autonomous AI-driven robots in industrial contexts is a promising vision that will have an impact on the sociotechnical system. Taking research from the field of human teamwork as guiding principles as well as results from human robot collaboration studies this study addresses open questions regarding the design and impact of communicative transparency and behavioral autonomy in a human robot collaboration. In an experimental approach, we tested whether an AI-narrative and communication panels of a robot-arm trigger the attribution of more human like traits and expectations going along with a changed attribution of blame and failure in a flawed collaboration.

Human-Robot Collaboration in Mixed-Flow Assembly Line Balancing under Uncertainty: An Efficient Discrete Bees Algorithm

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.

Repetitive industrial tasks can be easily performed by traditional robotic systems. However, many other works require cognitive knowledge that only humans can provide. Human-Robot Collaboration (HRC) emerges as an ideal concept of co-working between a human operator and a robot, representing one of the most significant subjects for human-life improvement.The ultimate goal is to achieve physical interaction, where handing over an object plays a crucial role for an effective task accomplishment. Considerable research work had been developed in this particular field in recent years, where several solutions were already proposed. Nonetheless, some particular issues regarding Human-Robot Collaboration still hold an open path to truly important research improvements. This paper provides a literature overview, defining the HRC concept, enumerating the distinct human-robot communication channels, and discussing the physical interaction that this collaboration entails. Moreover, future challenges for a natural and intuitive collaboration are exposed: the machine must behave like a human especially in the pre-grasping/grasping phases and the handover procedure should be fluent and bidirectional, for an articulated function development. These are the focus of the near future investigation aiming to shed light on the complex combination of predictive and reactive control mechanisms promoting coordination and understanding. Following recent progress in artificial intelligence, learning exploration stand as the key element to allow the generation of coordinated actions and their shaping by experience.

Collaborative robotic solutions, where human workers and robots share their skills, are emerging in the industrial context. In order to achieve an appropriate level of Human-Robot Collaboration (HRC), the workstations’ design has to be human-centered and adaptive to the workers’ characteristics/limitations, considering ergonomic criteria. This study corresponds to the first phase of a research project to apply HRC to minimize the musculoskeletal risk associated with a manual assembly task (industrial furniture manufacturing). A new workstation was designed and an ergonomic approach was developed to assess the main risk factors, as well as to optimize the future task allocation between human workers and robots. A questionnaire about working conditions and musculoskeletal symptomology was applied to a selected group of 8 workers. Rapid Upper Limb Assessment and Strain Index were applied (across 38 postures) to assess musculoskeletal risk related to the assembly tasks. In this study, we propose an ergonomic approach to orient the design and task allocation of HRC work systems. This approach allowed the identification of workers’ complaints and risk factors that can be mitigated with future implementations of HRC.

Industrial human-robot collaboration (HRC) is a promising production mode that enables humans and robots complete a joint set of tasks in a shared workplace. In this context, to facilitate efficient and safe collaboration, an industrial robot needs to understand its human teammate’s behavior and develop human-aware motion planning. However, the systematic theoretical explanation on this subject is limited. Shared autonomy allows the human intervention in the control loop of the autonomous robot to achieve human-robot common goals. In this paper, the framework of shared autonomy into industrial HRC context is presented. In the sight of shared autonomy, considering the intention of human behavior is partially observable, we formalize the human-aware motion planning as a Partially Observable Markov Decision Process (POMDP), where the robot addresses the sequential decision making problems under the uncertainty of human’s intention. Moreover, the shared autonomy framework and its detailed systematic enabling approaches for human-aware motion planning is presented. The feasibility of the presented framework and approaches is also validated by the case study of a HRC assembly scenario, which could accomplish more fluent and safe collaboration.

Simple SummaryIn the last few years, the physical health and mental health of workers in an industrial context while interacting with collaborative robots have become of primary interest in the research field. The characterization of the mental and psychophysical health of workers and subjects can be investigated with biomechanical analysis. In this work, a biomechanical model was used to perform simulations of the biomechanics of reaching against gravity movements as a paradigmatic motion primitive underlying several functional tasks. Motion tracking data were acquired with motion capture, and, in a simulation environment, multiple movement speeds and carried loads were changed in order to analyze the effects on upper-limb kinematics and dynamics. An optimal range of velocities for human motion was found in which the expended energy was lower. The deviation from energy optimum for the upper limb was evaluated when testing nonoptimal movement conditions. These results can be useful in a human–robot collaboration scenario to tune setup parameters to preserve the physical and mental health of the workers, to assess biomechanics, and to define fatigue and effort indices.In the last few years, there has been increased interest in the preservation of physical and mental health of workers that cooperate with robots in industrial contexts, such as in the framework of the European H2020 Mindbot Project. Since biomechanical analysis contributes to the characterization of the subject interacting with a robotic setup and platform, we tested different speed and loading conditions in a simulated environment to determine upper-limb optimal performance. The simulations were performed starting from laboratory data of people executing upper-limb frontal reaching movements, by scaling the motion law and imposing various carried loads at the hand. The simulated velocity ranged from 20% to 200% of the original natural speed, with step increments of 10%, while the hand loads were 0, 0.5, 1, and 2 kg, simulating carried objects. A 3D inverse kinematic and dynamic model was used to compute upper-limb kinematics and dynamics, including shoulder flexion, shoulder abduction, and elbow flexion. An optimal range of velocities was found in which the expended energy was lower. Interestingly, the optimal speed corresponding to lower exerted torque and energy decreased when the load applied increased. Lastly, we introduced a preliminary movement inefficiency index to evaluate the deviation of the power and expended energy for the shoulder flexion degree of freedom when not coinciding with the minimum energy condition. These results can be useful in human–robot collaboration to design minimum-fatigue collaborative tasks, tune setup parameters and robot behavior, and support physical and mental health for workers.

Abstract: This volume provides a comprehensive analysis of advanced robotics as a transformative force in modern manufacturing, focusing on its role in automating production and enhancing operational efficiency. The conceptual framework is grounded in the evolution from fixed automation to adaptive, intelligent systems, integrating cyber-physical architectures, edge–cloud computing, and Industry 4.0 principles. The central challenge addressed is the persistent gap between the technical potential of robotics and its scalable, secure, and interoperable implementation across diverse industrial contexts. The methodology combines theoretical synthesis with empirical examination, incorporating digital twin modeling, predictive maintenance protocols, machine vision–driven quality assurance, and multi-objective optimization across manufacturing hierarchies. Case studies from automotive, electronics, and precision engineering sectors illustrate the application of articulated robots, SCARA systems, autonomous mobile robots, and collaborative platforms in production environments. Comparative performance metrics and integration frameworks provide actionable guidance for deployment. Key results demonstrate that strategic adoption of robotics, when coupled with data-driven analytics, human-robot collaboration, and secure interoperability standards, delivers measurable gains in overall equipment effectiveness, defect reduction, and energy efficiency. The book’s findings have significant implications for both research and practice, offering a structured roadmap for scaling advanced automation while maintaining adaptability, safety, and sustainability in global manufacturing ecosystems. Keywords advanced robotics, manufacturing automation, Industry 4.0, cyber-physical systems, collaborative robots, autonomous mobile robots, SCARA robots, digital twins, predictive maintenance, machine vision, quality assurance, multi-objective optimization, human-robot collaboration, interoperability, sustainable manufacturing

To realize human-robot collaboration in manufacturing, industrial robots need to share an environment with humans and to work hand in hand. This introduces safety concerns but also provides the opportunity to take advantage of human-robot interactions to control the robot. The main objective of this work is to provide HRI without compromising safety issues in a realistic industrial context. In the paper, a region-based filtering and reasoning method for safety has been developed and integrated into a human-robot collaboration system. The proposed method has been successfully demonstrated keeping safety during the showcase evaluation of the European robotics challenges with a real mobile manipulator.

Human robot collaboration (HRC) with heavy-duty industrial robots is required in various production and recycling processes. They require optimal sensing methodology, which ensure safety from collision while allowing high robot velocity. Variety of local and global sensing approaches exist in industrial context. However, they are either only applicable for small robots, or limit the maximum robot velocity. This work proposes a novel integrated local and global sensing methodology for optimal collision detection and avoidance. The sensing methodologies is realized by complying to speed regulation from TS15066. It is realized by a) transforming robot model in the two sensing reference frames, b) estimating parallel shortest distance between the human and the robot in the sensing reference frames and finally by c) regulating the robot velocity based on relative human position. The global sensing ensures the robot deceleration as the human moves towards the robot. This allows using constant sized local search zones, which reduce false detections with close proximity worker at robot acceleration. The proposed system is validated using a heavy-duty industrial robot for a constant human presence. The methodology allows 11% more process efficiency compared to global only sensing system, while allowing 0% increase in the production space requirement, which makes it applicable to retrofit previously, installed robotic cells.

Due to its high complexity and the varied assembly processes, hybrid assembly systems characterized by human–robot collaboration (HRC) are meaningful. Suitable use cases must be identified efficiently to ensure cost-effectiveness and successful deployment in the respective assembly systems. This paper presents a method for evaluating the potential of HRC to derive automation suitability based on existing or to-be-collected time data. This should enable a quick and favorable statement to be made about processes, for efficient application in potential analyses. The method is based on the Methods–Time–Measurement Basic System (MTM-1) procedure, widely used in the industry. This ensures good adaptability in an industrial context. It extends existing models and examines how much assembly activities and processes can be optimized by efficiently allocating between humans and robots. In the process model, the assembly processes are subdivided and analyzed with the help of the specified MTM motion time system. The suitability of the individual activities and sub-processes for automation are evaluated based on criteria derived from existing methods. Two four-field matrices were used to interpret and classify the analysis results. The process is assessed using an example product from electrolyzer production, which is currently mainly assembled by hand. To achieve high statement reliability, further work is required to classify the results comprehensively.

In recent years, psychological empowerment has gained significant attention in organizational psychology and business contexts. However, its application to human-robot employees, particularly in the domain of collaborative robots (COBOTscobots), remains underexplored, especially in Southeast Asia and Malaysia where empirical studies are sparse. This pioneering study addresses this gap by applying Spreitzer's (1995) framework and using PLS-SEM to investigate psychological empowerment among Malaysian COBOTcobot-integrated operators in smart manufacturing. The study examines role clarity as a novel moderator alongside traditional antecedents and incorporates authentic leadership and psychological capital as new constructs. Key findings reveal that role clarity significantly enhances the positive effects of psychological empowerment on job engagement but does not impact innovative work behavior. Additionally, authentic leadership and psychological capital are crucial in fostering empowerment. This extended study of the nomological psychology of empowerment provides valuable insights for future research direction on human-robot employees and collaboration dynamics.