Simulation Platform to Investigate Safe Operation of Human-Robot Collaboration Systems

Trust has been identified as a key element for the successful cooperation between humans and robots. However, little research has been directed at understanding trust development in industrial human-robot collaboration (HRC). With industrial robots becoming increasingly integrated into production lines as a means for enhancing productivity and quality, it will not be long before close proximity industrial HRC becomes a viable concept. Since trust is a multidimensional construct and heavily dependent on the context, it is vital to understand how trust develops when shop floor workers interact with industrial robots. To this end, in this study a trust measurement scale suitable for industrial HRC was developed in two phases. In phase one, an exploratory study was conducted to collect participants’ opinions qualitatively. This led to the identification of trust related themes relevant to the industrial context and a related pool of questionnaire items was generated. In the second phase, three human-robot trials were carried out in which the questionnaire items were applied to participants using three different types of industrial robots. The results were statistically analysed to identify the key factors impacting trust and from these generate a trust measurement scale for industrial HRC.

In the industrial human-robot collaboration environment, workers and robots often need to collaborate to complete complex tasks in a shared space. Accurate and robust motion recognition is the key to improving the productivity and safety of human-robot collaboration. Accurately tracking and recognizing workers' motions can provide clues about tasks to be performed, thus ensuring the safety of collaborative workspaces. Aiming at the differences in postures and characteristics of workers' motions in the working environment, the human body's self-occlusion and partial occlusion, this paper uses a skeleton recognition algorithm to estimate the 3D skeleton information in monocular video. A novel skeleton descriptor that describes static and dynamic features of the 3D skeletons in the short time around the current frame is proposed. Besides, we train a deep neural network to recognize the human motion. Experimental results show that the proposed skeleton descriptor can improve the accuracy of motion recognition by about 4%, and the neural network we use outperforms the other three classical classification methods.

In complex industrial human-robot collaboration (HRC) environment, obstacles in the shared working space will occlude the operator, and the industrial robot will threaten the safety of the operator if it is unable to get the complete human spatial point cloud. This paper proposes a real-time human point cloud inpainting method based on the deep generative model. The method can recover the human point cloud occluded by obstacles in the shared working space to ensure the safety of the operator. The method proposed in this paper can be mainly divided into three parts: (i) real-time obstacles detection. This process can detect obstacle locations in real time and generate the image of obstacles. (ii) the application of the deep generative model algorithm. It is a complete convolutional neural network (CNN) structure and introduces advanced generative adversarial loss. The model can generate the missing depth data of operators at arbitrary position in the human depth image. (iii) spatial mapping of the depth image. The depth image will be mapped to point cloud by coordinate system conversion. The effectiveness of the method is verified by filling hole of the human point cloud occluded by obstacles in industrial HRC environment. The experiment results show that the proposed method can accurately generate the occluded human point cloud in real time and ensure the safety of the operator.

The inherent variability in human performance introduces stochastic perturbations into manufacturing environments, undermining the seamless coordination required for effective human-robot collaboration (HRC) systems. While human cognitive flexibility enhances adaptability, it simultaneously acts as a source of operational uncertainty, complicating the modeling and optimization of integrated robotic systems. Given these challenges, there is an urgent need to substantially expand the adaptability of robotic systems through real-time detection, algorithmic analysis and dynamic behavioral adjustments in response to human performance fluctuations. The systematic development of such systems capable of precisely detecting task-specific variations, analyzing them via advanced AI algorithms and adapting their behavior accordingly remains a critical focus of contemporary research. To evaluate progress in this domain, this study conducts a systematic literature review, synthesizing advancements across 124 publications and identifying underexplored research frontiers. The findings reveal a persistent misalignment between current technical capabilities and the requirements of adaptive collaboration in dynamic industrial environments. Key gaps include the absence of explainable AI frameworks for transparent decision-making, limited generalizability of adaptive control architectures and a lack of proactive strategies that anticipate rather than merely react to performance deviations.

The development and evaluation of Robot Light Skin: A novel robot signalling system to improve communication in industrial human–robot collaboration

Trends on Human Factors in Industrial Human-Robot Collaboration: from Design to Implementation

In the Industry 5.0 era, the focus shifts from basic automation to fostering collaboration between humans and robots. Trust is crucial in this new paradigm, enabling smooth interaction, especially for users with limited robotics knowledge. This study presents a novel framework that uses human hand gestures and voice commands to control robot movements, aiming to enhance trust, reduce cognitive workload, and minimize task execution time-key for efficient manufacturing. In automated systems, swift completion of micromanagement tasks is essential to prevent process disruption. To evaluate this framework, we devised a testbed scenario within an automated carbon fiber transportation and draping process, focusing on a maintenance task as the micromanagement challenge. Participants inspected the gripper, guided the robot along a defined path, and performed maintenance, such as attaching cables. Two conditions were tested: gestures and voice commands versus a smartPAD. The results showed that gestures and voice commands increased trust, lowered cognitive load, and shortened execution times, improving overall manufacturing efficiency.

Web-based human-robot collaboration digital twin management and control system

Human-robot collaboration (HRC) on assembly lines enhances productivity and human operators' well-being by enabling humans and robots to perform tasks independently or collaboratively in shared workspaces. Aligning with the human-centric vision of Industry 5.0, this study addresses the human-centered assembly line balancing problem with human-robot collaboration (HCALBP-HRC). A multi-objective mixed-integer linear programming (MILP) model is proposed to minimize cycle time, cognitive load, and ergonomic risk through effective task allocation and resource management. Experiment results of three groups of instances with different production scenarios verified the feasibility of the MILP model. The analysis of trade-offs among the three objectives under varying weight configurations highlights the conflicts between production efficiency and human-centered considerations, underscoring the importance of balanced decision-making in HRC-based assembly systems.

The human-robot collaboration (HRC) solutions presented so far have the disadvantage that the interaction between humans and robots is based on the human's state or on specific gestures purposely performed by the human, thus increasing the time required to perform a task and slowing down the pace of human labor, making such solutions uninteresting. In this study, a different concept of the HRC system is introduced, consisting of an HRC framework for managing assembly processes that are executed simultaneously or individually by humans and robots. This HRC framework based on deep learning models uses only one type of data, RGB camera data, to make predictions about the collaborative workspace and human action, and consequently manage the assembly process. To validate the HRC framework, an industrial HRC demonstrator was built to assemble a mechanical component. Four different HRC frameworks were created based on the convolutional neural network (CNN) model structures: Faster R-CNN ResNet-50 and ResNet-101, YOLOv2 and YOLOv3. The HRC framework with YOLOv3 structure showed the best performance, showing a mean average performance of 72.26% and allowed the HRC industrial demonstrator to successfully complete all assembly tasks within a desired time window. The HRC framework has proven effective for industrial assembly applications.

Human-Robot Collaboration (HRC) in industry is a promising research direction that has potential to expand robotics to previously unthinkable application areas. Orchestration of large hybrid human-robot teams carrying out many tasks in parallel within a shop floor faces new challenges due to unique aspects introduced by HRC. This paper presents a new approach to topological and temporal orchestration of hybrid human-robot workforce, considering the capabilities of robot agents as well as the potentially new roles that human operators may acquire in an HRC setting. We propose a two-stage approach to orchestrating large HRC teams: First, an abstract topological and task assignment problem is solved, which does not consider the precise sequence of tasks. Second, the result of the first step is used to initialize a constrained search for an efficient HRC schedule. Initial application of the proposed approach in problems of varying complexity demonstrates encouraging results.

Deep Learning-based Multimodal Control Interface for Human-Robot Collaboration

This paper presents a mini-review of human-robot collaboration (HRC) in the construction industry, synthesizing key findings from the literature.The paper outlines the development of construction robotics, the current applications of HRC, the issues identified, and the factors affecting the adoption of HRC in construction.Furthermore, it discusses the future directions and opportunities for HRC in construction.This mini-review aims to provide some knowledge to advance the integration of HRC in construction.

Robots present an innovative solution to the construction industry’s challenges, including safety concerns, skilled worker shortages, and productivity issues. Successfully collaborating with robots requires new competencies to ensure safety, smooth interaction, and accelerated adoption of robotic technologies. However, limited research exists on the specific competencies needed for human—robot collaboration in construction. Moreover, the perspectives of construction industry professionals on these competencies remain underexplored. This study examines the perceptions of construction industry professionals regarding the knowledge, skills, and abilities necessary for the effective implementation of human—robot collaboration in construction. A two-round Delphi survey was conducted with expert panel members from the construction industry to assess their views on the competencies for human—robot collaboration. The results reveal that the most critical competencies include knowledge areas such as human—robot interface, construction robot applications, human—robot collaboration safety and standards, task planning and robot control system; skills such as task planning, safety management, technical expertise, human—robot interface, and communication; and abilities such as safety awareness, continuous learning, problemsolving, critical thinking, and spatial awareness. This study contributes to knowledge by identifying the most significant competencies for human—robot collaboration in construction and highlighting their relative importance. These competencies could inform the design of educational and training programs and facilitate the integration of robotic technologies in construction. The findings also provide a foundation for future research to further explore and enhance these competencies, ultimately supporting safer, more efficient, and more productive construction practices.

The emergence of Industry 5.0 brings a paradigm shift towards collaborative environments where humans and intelligent robots work side-by-side, enabling personalized, flexible, and resilient manufacturing. However, integrating humans and robots introduces new operational and safety risks that require proactive and adaptive control strategies. This study proposes an intelligent multi-criteria decision-making framework for smart risk assessment and the selection of optimal adaptive control strategies in human–robot collaborative manufacturing settings. The proposed framework integrates advanced risk analytics, real-time data processing, and expert knowledge to evaluate alternative control strategies, such as real-time wearable sensor integration, vision-based dynamic safety zones, AI-driven behavior prediction models, haptic feedback, and self-learning adaptive robot algorithms. A cross-disciplinary panel of ten experts structures six main and eighteen sub-criteria spanning safety, adaptability, ergonomics, reliability, performance, and cost, with response time and implementation/maintenance costs modeled as cost types. Safety receives the most significant weight; the most influential sub-criteria are collision avoidance efficiency, return on investment (ROI), and emergency response capability. The framework preserves linguistic semantics from elicitation to aggregation and provides a transparent, uncertainty-aware tool for selecting and phasing adaptive control strategies in Industry 5.0 collaborative cells.