Human-robot Collaboration Systems Research Articles

Work Roles in Human–Robot Collaborative Systems: Effects on Cognitive Ergonomics for the Manufacturing Industry

Human–robot collaborative systems have been adopted by manufacturing organizations with the objective of releasing physical workload to the human factor. However, the roles and responsibilities of human operators in these semi-automated systems have not been properly analyzed. This might carry important consequences in the cognitive dimension of ergonomics, which then contradicts the main well-being goals of collaborative work. Therefore, we designed a series of collaborative scenarios where we shifted the assignment of work responsibilities between humans and robots while executing a quality inspection task. Variations in the state of cognitive ergonomics were estimated with subjective and objective techniques via workload tests and physiological responses respectively. Furthermore, we introduced a work design framework based on 50 state-of-the-art applications for a structured implementation of human–robot collaborative systems that contemplates the underlying organizational and technological components necessary to fulfill its basic functionalities. Human operators that possessed responsibility roles over collaborative robots presented better results in terms of cognitive workload and spare mental capacity alike. In this regard, mental demand is seen as a key workload variable to consider when designing collaborative work in current manufacturing settings.

A vision-enabled fatigue-sensitive human digital twin towards human-centric human-robot collaboration

Within a Human-centric Human-Robot Collaboration (HHRC) system, monitoring, assessing, and optimizing for an operator’s well-being is essential to creating an efficient and comfortable working environment. Currently, monitoring systems are used for independent assessment of human factors. However, the rise of the Human Digital Twin (HDT) has provided the framework for synchronizing multiple operator well-being assessments to create a comprehensive understanding of the operator’s performance and health. Within manufacturing, an operator’s dynamic well-being can be attributed to their physical and cognitive fatigue across the assembly process. As such, we apply non-invasive video understanding techniques to extract relevant assembly process information for automatic physical fatigue assessment. Our novelty involves a video-based fatigue estimation method, in which the boundary-aware dual-stream MS-TCN combined with an LSTM is proposed to detect the operation type, operation repetitions, and the target arm performing each task in an assembly process video. The detected results are then input into our physical fatigue profile to automatically assess the operator’s localized physical fatigue impact. The assembly process of a real-world bookshelf is recorded and tested against, with our algorithm results showing superiority in operation segmentation and target arm detection as opposed to other recent action segmentation models. In addition, we integrate a cognitive fatigue assessment tool that captures operator physiological signals in real-time for body response detection caused by stress. This provides a more robust HDT of the operator for an HHRC system.

A deep learning-enabled visual-inertial fusion method for human pose estimation in occluded human-robot collaborative assembly scenarios

In the context of human-centric smart manufacturing, human-robot collaboration (HRC) systems leverage the strengths of both humans and machines to achieve more flexible and efficient manufacturing. In particular, estimating and monitoring human motion status determines when and how the robots cooperate. However, the presence of occlusion in industrial settings seriously affects the performance of human pose estimation (HPE). Using more sensors can alleviate the occlusion issue, but it may cause additional computational costs and lower workers' comfort. To address this issue, this work proposes a visual-inertial fusion-based method for HPE in HRC, aiming to achieve accurate and robust estimation while minimizing the influence on human motion. A part-specific cross-modal fusion mechanism is designed to integrate spatial information provided by a monocular camera and six Inertial Measurement Units (IMUs). A multi-scale temporal module is developed to model the motion dependence between frames at different granularities. Our approach achieves 34.9 mm Mean Per Joint Positional Error (MPJPE) on the TotalCapture dataset and 53.9 mm on the 3DPW dataset, outperforming state-of-the-art visual-inertial fusion-based methods. Tests on a synthetic-occlusion dataset further validate the occlusion robustness of our network. Quantitative and qualitative experiments on a real assembly case verified the superiority and potential of our approach in HRC. It is expected that this work can be a reference for human motion perception in occluded HRC scenarios.

A multivariate fusion collision detection method for dynamic operations of human-robot collaboration systems

Real-time human-robot collision detection is crucial for ensuring the safety of operators during human-robot collaboration(HRC) and for improving the efficiency of such collaboration. It plays an important role in promoting the development of intelligent manufacturing. To address this issue, our team developed a multi-faceted collision detection system using eXtended Reality (XR) technology, specifically designed for complex and dynamic human-robot collaborative operations. The system integrates three different methods: a Virtual Reality (VR) detection method that enables robots to better perceive and detect human operators. An Augmented Reality (AR) detection method that enhances the operator’s perception of the robot. And a fusion detection and evaluation method. This detection and evaluation method assesses the effectiveness of collaboration by analyzing key performance indicators, such as real-time distance between human and robot, changes in the operator’s Heart Rate(HR), and overall task completion time. Through empirical research on the human-robot collaborative assembly task of T-series spiral bevel gear reducers, the effectiveness of the innovative method is verified. The research results show that this method significantly improves safety and operational efficiency, providing a novel solution detection in industrial manufacturing environments.

Foundation models assist in human–robot collaboration assembly

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.

Human-robot collaborative decision method of hexapod robot based on prior knowledge and negotiation strategy

Against the unstructured terrain environment, human drivers often rely on remote operating patterns to accomplish tasks with robots. With the gradual improvement in robot intelligence, a key problem that urgently requires attention is how to bridge cognitive and perceptual differences between human and robot in collaborative decision-making processes. Particularly effective resolution of differences in decision outcomes is crucial. Therefore, this paper utilizes the decision logic process and prior knowledge model of human drivers to form a robot's decision framework, narrowing the cognitive differences between human and robot. Besides, embedding the human-robot-environment features that affect the decision-making process as prior knowledge into the robot's decision-making system can effectively reduce perceptual gaps. Under the same decision framework, a negotiation strategy is proposed for forming a unified decision outcome between human and robot by finding an optimal concession rate. This study utilizes a hexapod robot simulated driving platform to gather experimental data and develop prior knowledge for robots. It also leverages virtual reality equipment and visual augmentation technologies to establish a remote human-robot collaborative decision-making experimental system. The experimental results conducted in complex obstacle terrain demonstrate the effectiveness of adopting the negotiation strategy for human-robot collaborative decision-making. Compared with the individual human or robot decision, the human-robot collaborative decision method significantly enhances robot stability, reduces energy consumption, and minimizes collisions.

Applying vision-guided graph neural networks for adaptive task planning in dynamic human robot collaborative scenarios

ABSTRACT The Assemble-To-Order (ATO) strategy is increasingly becoming prevalent in the manufacturing sector due to the high demand for high-volume personalised and customised goods. The use of Human-Robot Collaborative (HRC) Systems are increasingly being investigated in order to make use of the dexterous strength of human hands while at the same time make use of the ability of robots to carry massive loads. However, current HRC systems struggle to adapt dynamically to varying human actions and cluttered workspaces. In this paper, we propose a novel neural network framework that integrates both Graph Neural Network (GNN) and Long Short-Term Memory (LSTM) for adaptive response during HRC scenarios. Our framework enables a robot to interpret human actions and generate detailed action plans while dealing with objects in a cluttered workspace thereby addressing the challenges of dynamic human-robot collaboration. Experimental results demonstrate improvements in assembly efficiency and flexibility, making our approach the first integration of iterative grasping and flexible HRC within a unified neural network architecture.

Blockchain-based cloud-edge collaborative data management for human-robot collaboration digital twin system

Human-robot collaboration demonstrates broad application prospects in product customization. Digital twin represents an advanced real-virtual interaction technology that plays an essential role in enhancing perception and interaction for human-robot collaboration. A digital twin-based human-robot collaboration system has been proposed to devise collaborative strategies, simulate collaborative processes, and ensure human safety. However, there exist research gaps in implementing human-robot collaboration digital twin systems. A significant challenge lies in constructing data models for describing data types and content in human-robot collaboration digital twin systems. Additionally, addressing data management aspects, including data sharing and storage, is crucial for the effective operation of human-robot collaboration digital twin systems. To bridge existing deficiencies, a novel approach is introduced for managing data in human-robot collaboration digital twin systems through a blockchain-based cloud-edge collaborative method. Initially, a conceptualization of the human-robot collaboration digital twin system alongside a cloud-edge data management framework is introduced. Subsequently, a data model is delineated to outline data categories and contents of human-robot collaboration digital twin systems. Following this, an exploration is conducted on methodologies for data sharing and storage utilizing blockchain and cloud technologies. Ultimately, the efficacy of the proposed approaches is validated through a case study.

Human–robot collaborative handling of curtain walls using dynamic motion primitives and real-time human intention recognition

Human–robot collaboration fully leverages the strengths of both humans and robots, which is crucial for handling large, heavy objects at construction sites. To address the challenges of human–machine cooperation in handling large-scale, heavy objects — specifically building curtain walls — a human–robot collaboration system was designed based on the concept of “human–centered with machine support”. This system allows the handling of curtain walls according to different human intentions. First, a robot trajectory learning and generalization model based on dynamic motion primitives was developed. The operator’s motion intent was then characterized by their speed, force, and torque, with the force impulse introduced to define the operator’s intentions for acceleration and deceleration. Finally, a collaborative experiment was conducted on an experimental platform to validate the robot’s understanding of human handling intentions and to verify its ability to handle curtain wall. Collaboration between humans and robots ensured a smooth and labor-saving handling process.

Real-Time Monitoring of Human and Process Performance Parameters in Collaborative Assembly Systems using Multivariate Control Charts

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.

Human-Robot Collaboration With a Corrective Shared Controlled Robot in a Sanding Task.

Physical and cognitive workloads and performance were studied for a corrective shared control (CSC) human-robot collaborative (HRC) sanding task. Manual sanding is physically demanding. Collaborative robots (cobots) can potentially reduce physical stress, but fully autonomous implementation has been particularly challenging due to skill, task variability, and robot limitations. CSC is an HRC method where the robot operates semi-autonomously while the human provides real-time corrections. Twenty laboratory participants removed paint using an orbital sander, both manually and with a CSC robot. A fully automated robot was also tested. The CSC robot improved subjective discomfort compared to manual sanding in the upper arm by 29.5%, lower arm by 32%, hand by 36.5%, front of the shoulder by 24%, and back of the shoulder by 17.5%. Muscle fatigue measured using EMG, was observed in the medial deltoid and flexor carpi radialis for the manual condition. The composite cognitive workload on the NASA-TLX increased by 14.3% for manual sanding due to high physical demand and effort, while mental demand was 14% greater for the CSC robot. Digital imaging showed that the CSC robot outperformed the automated condition by 7.16% for uniformity, 4.96% for quantity, and 6.06% in total. In this example, we found that human skills and techniques were integral to sanding and can be successfully incorporated into HRC systems. Humans performed the task using the CSC robot with less fatigue and discomfort. The results can influence implementation of future HRC systems in manufacturing environments.

Multi-scale control and action recognition based human-robot collaboration framework facing new generation intelligent manufacturing

Facing the new generation intelligent manufacturing, traditional manufacturing models are transitioning towards large-scale customized productions, improving the efficiency and flexibility of complex manufacturing processes. This is crucial for enhancing the stability and core competitiveness of the manufacturing industry, and human-robot collaboration systems are an important means to achieve this goal. At present, mainstream manufacturing human-robot collaboration systems are modeled for specific scenarios and actions, with poor scalability and flexibility, making it difficult to flexibly handle actions beyond the set. Therefore, this article proposes a new human-robot collaboration framework based on action recognition and multi-scale control, designs 27 basic gesture actions for motion control, and constructs a robot control instruction library containing 70 different semantics based on these actions. By integrating static gesture recognition, dynamic action process recognition, and You-Only-Look-Once V5 object recognition and positioning technology, accurate recognition of various control actions has been achieved. The recognition accuracy of 27 types of static control actions has reached 100%, and the dynamic action recognition accuracy of the gearbox assembly process based on lightweight MF-AE-NNOBJ has reached 90%. This provides new ideas for simplifying the complexity of human-robot collaboration problems, improving system accuracy, efficiency, and stability.

Navigating The Cloud: Enhancing Human-Robot Collaboration In Industry 4.0 Through Digital Twins And Cloud-Based Systems

Industry 4.0 Is Geared Towards Establishing Sophisticated Industrial Ecosystems That Necessitate Secure And Efficient Collaboration Between Humans And Robots, Termed Human-Robot Collaboration (HRC). Within This Framework, HRC Leverages Cutting-Edge Collision Avoidance Technology Capable Of Detecting Objects And Foreseeing Possible Collisions, Thereby Calculating Alternative Pathways To Avert Any Direct Contact. Furthermore, The Concept Of Digital Twins Emerges As An Innovative Solution, Offering A Platform To Simultaneously Evaluate The Consequences Of Various Control Strategies Within A Digitally Simulated Environment. While Cloud Computing Infrastructure Can Furnish The Requisite Computational Prowess, It May Simultaneously Introduce Challenges Such As Increased Latency And Variability In Communication, Known As Jitter, Which Could Potentially Hinder System Performance. Cloud Technologies Are Renowned For Their Inventive Approaches To Ease The Workload For Software Developers And System Operators, Raising Questions About Their Integration With Digital Twin Methodologies And The Resilience Of Robotic Systems Against The Potential Delays Incurred From Cloud-Based Services. This Research Endeavors To Navigate These Complexities By Integrating A Blend Of Both Public And Private Cloud Services, Which Are Distinguished By Their Unique Parallel Processing Capabilities. This Study's Contributions Are Threefold. Firstly, It Introduces A Novel Method Designed To Gauge The Efficacy Of Diverse Strategies By Focusing On Their Associated Latency Impacts. Secondly, It Delineates A Tangible Application Of HRC, Illuminating Its Practical Benefits And Real-World Applicability. Lastly, It Defines A Crucial Performance Metric Intended To Assess The Efficiency Of These Systems Thoroughly. By Delving Into These Aspects, The Research Aims To Conduct A Comprehensive Analysis Of The Strengths And Weaknesses Inherent In Various Technological Methodologies And Their Consequential Effects On The Performance Of HRC Systems Within The Ambit Of Industry 4.0. Through This Detailed Examination, The Study Seeks To Provide Valuable Insights Into Optimizing Human-Robot Collaboration In Industrial Settings, Ensuring Both Security And Efficiency Are Upheld

Human Factors in AR-Enabled Human-Robot Collaboration for Fabrication-Centric Architectural Design Process: A Co-design Workshop Approach.

The emergence of collaborative robotics presents an opportunity for architectural designers to safely engage in design and fabrication through human-robot collaboration (HRC). By leveraging the adaptability, creativity, and design judgement of designers with the strength, repeatability, and design precision of robotic assistance, HRC has the potential to create a unified design-fabrication workflow. Recent advancements in augmented reality (AR) technology further enhance these prospects by enabling users to superimpose context-sensitive, computer-generated information in the real world. AR technology also provides situational awareness, which proves beneficial in the context of HRC. The maturation of AR technologies offers new possibilities for developing HRC systems tailored to architectural design-fabrication needs. Recognizing the pivotal role of human factors in HRC development process, this paper aims to explore the architectural designers’ needs to develop an AR-enabled HRC system that better supports the fabrication-centric design process, such as exploratory collaborative assembly tasks. Key findings highlight the necessity for a unified design-fabrication workflow, a clearer allocation of tasks between designers and robotic arms, an intuitive user interface, a streamlined interaction process, a better understanding of robot intentions and movements, intuitive procedures for error avoidance and correction, and enhanced user safety in HRC scenarios.

Designing interaction interface for supportive human-robot collaboration: A co-creation study involving factory employees

Considering social-technical factors during the design and implementation of collaborative robots (cobots) is important to ensure their successful integration into industrial workspaces and the well-being of the operators such as ergonomics. In this work, we present a co-creation study in developing an interaction interface for a human–robot collaboration (HRC) system involving SME factory employees in the Netherlands. Employing a qualitative research method, the co-creation activities in this study sought employees’ input on preferred use cases and collaboration methods with robots. The gathered qualitative data was used to design the HRC interaction interface, aligning it with operators’ needs and preferences. The developed system was fully functional, underwent technical validation, and received feedback from the factory operators. Our study emphasizes the importance of involving employees in the design of HRC interaction interfaces, which can result in HRC systems that meet their needs and preferences. Such customized systems have the potential to enhance the acceptance of robots in industrial settings. The study contributes to the field by demonstrating a participatory approach to designing an HRC interaction interface for a robot in a real industrial setting, where no use cases or interaction modalities were pre-defined.

Managing safety of the human on the factory floor: a computer vision fusion approach

PurposeMaintaining the safety of the human is a major concern in factories where humans co-exist with robots and other physical tools. Typically, the area around the robots is monitored using lasers. However, lasers cannot distinguish between human and non-human objects in the robot’s path. Stopping or slowing down the robot when non-human objects approach is unproductive. This research contribution addresses that inefficiency by showing how computer-vision techniques can be used instead of lasers which improve up-time of the robot.Design/methodology/approachA computer-vision safety system is presented. Image segmentation, 3D point clouds, face recognition, hand gesture recognition, speed and trajectory tracking and a digital twin are used. Using speed and separation, the robot’s speed is controlled based on the nearest location of humans accurate to their body shape. The computer-vision safety system is compared to a traditional laser measure. The system is evaluated in a controlled test, and in the field.FindingsComputer-vision and lasers are shown to be equivalent by a measure of relationship and measure of agreement. R2 is given as 0.999983. The two methods are systematically producing similar results, as the bias is close to zero, at 0.060 mm. Using Bland–Altman analysis, 95% of the differences lie within the limits of maximum acceptable differences.Originality/valueIn this paper an original model for future computer-vision safety systems is described which is equivalent to existing laser systems, identifies and adapts to particular humans and reduces the need to slow and stop systems thereby improving efficiency. The implication is that computer-vision can be used to substitute lasers and permit adaptive robotic control in human–robot collaboration systems.

Protective and Collision‐Sensitive Gel‐Skin: Visco‐Elastomeric Polyvinyl Chloride Gel Rapidly Detects Robot Collision by Breaking Electrical Charge Accumulation Stability

Human–robot collaboration (HRC) is effective to improve productivity in industrial fields, based on the robot's fast and precise work and the human's flexible skill. To facilitate the HRC system, the first priority is to ensure safety in the event of accidents, such as collisions between robots and humans. Therefore, a protective and collision‐sensitive robot skin, named Gel‐Skin is proposed to guarantee the safety in HRC. The Gel‐Skin is composed of polyvinyl chloride (PVC) gel, which is a functional material with piezoresistive characteristics and impact absorption capability. In particular, the PVC gel has a distinctive piezoresistive property that the relation between mechanical pressure and electrical resistance is tunable depending on an applied voltage. When a voltage is applied to the PVC gel, the electrical charges are accumulated around the anode and it shows increased piezoresistive sensitivity. In this study, it is verified for the PVC gel to exhibit the 4.78 times higher sensitivity by simply applying a voltage. This novel physical phenomenon enables the Gel‐Skin to detect the collision rapidly. Finally, the Gel‐Skin is applicated to a real robot system and it is verified that the Gel‐Skin can detect a collision and absorb impact to ensure safety.

A Distortion Correction Method Based on Actual Camera Imaging Principles.

In the human-robot collaboration system, the high-precision distortion correction of the camera as an important sensor is a crucial prerequisite for accomplishing the task. The traditional correction process is to calculate the lens distortion with the camera model parameters or separately from the camera model. However, in the optimization process calculate with the camera model parameters, the mutual compensation between the parameters may lead to numerical instability, and the existing distortion correction methods separated from the camera model are difficult to ensure the accuracy of the correction. To address this problem, this study proposes a model-independent lens distortion correction method based on the image center area from the perspective of the actual camera lens distortion principle. The proposed method is based on the idea that the structured image preserves its ratios through perspective transformation, and uses the local image information in the central area of the image to correct the overall image. The experiments are verified from two cases of low distortion and high distortion under simulation and actual experiments. The experimental results show that the accuracy and stability of this method are better than other methods in training and testing results.

Navigating The Cloud: Enhancing Human-Robot Collaboration In Industry 4.0 Through Digital Twins And Cloud-Based Systems

Industry 4.0 Is Geared Towards Establishing Sophisticated Industrial Ecosystems That Necessitate Secure And Efficient Collaboration Between Humans And Robots, Termed Human-Robot Collaboration (HRC). Within This Framework, HRC Leverages Cutting-Edge Collision Avoidance Technology Capable Of Detecting Objects And Foreseeing Possible Collisions, Thereby Calculating Alternative Pathways To Avert Any Direct Contact. Furthermore, The Concept Of Digital Twins Emerges As An Innovative Solution, Offering A Platform To Simultaneously Evaluate The Consequences Of Various Control Strategies Within A Digitally Simulated Environment. While Cloud Computing Infrastructure Can Furnish The Requisite Computational Prowess, It May Simultaneously Introduce Challenges Such As Increased Latency And Variability In Communication, Known As Jitter, Which Could Potentially Hinder System Performance. Cloud Technologies Are Renowned For Their Inventive Approaches To Ease The Workload For Software Developers And System Operators, Raising Questions About Their Integration With Digital Twin Methodologies And The Resilience Of Robotic Systems Against The Potential Delays Incurred From Cloud-Based Services. This Research Endeavors To Navigate These Complexities By Integrating A Blend Of Both Public And Private Cloud Services, Which Are Distinguished By Their Unique Parallel Processing Capabilities. This Study's Contributions Are Threefold. Firstly, It Introduces A Novel Method Designed To Gauge The Efficacy Of Diverse Strategies By Focusing On Their Associated Latency Impacts. Secondly, It Delineates A Tangible Application Of HRC, Illuminating Its Practical Benefits And Real-World Applicability. Lastly, It Defines A Crucial Performance Metric Intended To Assess The Efficiency Of These Systems Thoroughly. By Delving Into These Aspects, The Research Aims To Conduct A Comprehensive Analysis Of The Strengths And Weaknesses Inherent In Various Technological Methodologies And Their Consequential Effects On The Performance Of HRC Systems Within The Ambit Of Industry 4.0. Through This Detailed Examination, The Study Seeks To Provide Valuable Insights Into Optimizing Human-Robot Collaboration In Industrial Settings, Ensuring Both Security And Efficiency Are Upheld

The Impact of Changing Collaborative Workplace Parameters on Assembly Operation Efficiency

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