Human-Robot Collaboration in the Construction Industry: A Mini-review

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.

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.

How human-robot collaboration impacts construction productivity: An agent-based multi-fidelity modeling approach

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.

The steep rise of interest in collaborative robots applied to industrial tasks is fallout from the emergence of Industry 4.0 in most industrialized countries. Workcells with both human workers and robots employed together extend the field of use of robotics to medium and small series productions. In a collaborative workplace, robots and humans can exploit their mutual skills and strengths to the best effect. With the goal of supporting collaborative robotics, this chapter points out several issues that may arise by introducing robots in manual production. This chapter’s statement is that the success of collaborative workcells in manufacturing relies on dedicated and accurate design activities and should not be approached by the mere addition of a robot to the existing workplace.

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.

Exploring three pillars of construction robotics via dual-track quantitative analysis

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.

In recent years the application of transformative technologies has, across a number of industries, represented a challenge to current industry paradigms, resulting in significant improvements in both technical capabilities and output efficiency, whilst disrupting established business models. Many industries are facing an uncertain future in which today’s technological limitations cannot be assumed to apply. The construction industry is likely to be significantly affected by potentially transformative technologies such as additive manufacturing (3D printing), artificial intelligence, and virtual/augmented reality. The application of such technologies presents both significant opportunities and challenges. There are a lot of bottlenecks that need to be resolved before we can maximize the positive impacts of transformative technologies on the construction industry. It is often claimed that additive manufacturing (AM) will transform the way we construct structures and buildings. Although, still in its infancy, this technology is likely to become an important part of the construction industry in relatively near future. AM as one of the most highlighted key enabling technologies has the potential to create disruptive solutions along with automation and robotics, that are at the forefront of building innovation. Material formulations and the science behind the interactions of different components undoubtedly counts as a vital workforce that determines the successful implementation of AM technology in the construction industry. There are fundamental interdependencies between the materials, the printing technologies, and both the scale and geometrical complexity of any printed structure. The construction industry can potentially take utmost advantage of the benefits of AM, e.g. improve safety, reduce labor and time, and advance customization. The key for successful development and implementation of AM is industry stakeholder collaboration involving materials science, architecture/design, computation, and robotics. Automation in construction will happen, there is no question about it and AM will have its role to a certain extent, as it cannot be the sole solution to major problems in the sector.

Starting in the 1970s with robots that were physically isolated from contact with their human co-workers, robots now collaborate with human workers towards a common task goal in a shared workspace. This type of robotic device represents a new era of workplace automation. Industrial robotics is rapidly evolving due to advances in sensor technology, artificial intelligence (AI), wireless communications, mechanical engineering, and materials science. While these new robotic devices are used mainly in manufacturing and warehousing, human-robot collaboration is now seen across multiple goods-producing and service-delivery industry sectors. Assessing and controlling the risks of human-robot collaboration is a critical challenge for occupational safety and health research and practice as industrial robotics becomes a pervasive feature of the future of work. Understanding the physical, psychosocial, work organization, and cybersecurity risks associated with the increasing use of robotic technologies is critical to ensuring the safe development and implementation of industrial robotics. This commentary provides a brief review of the uses of robotic technologies across selected industry sectors; the risks of current and future industrial robotic applications for worker and employer alike; strategies for integrating human-robot collaboration into a health and safety management system; and the role of robotic safety standards in the future of work.

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.

Human-robot collaborative control of lightweight manipulator is an effective means for the development of human friendly robot. First, in view of the commonality and key requirements of human-robot collaboration function, the control system architecture and design method of cooperative robot with four-layer functional architecture are put forward, and analysis the kinematics and dynamics control method of lightweight manipulator. Then, in order to solve the high-precision multi-joint synchronous motion control of the low-rigidity lightweight manipulator, a method of synchronizing network distributed clock based on EtherCAT real-time Ethernet is proposed, and then puts a method of building experiment of half-physical joint simulation platform. In order to solve the problem of kinematic man-machine cooperative control of lightweight robotic manipulators, a simplified method of manipulator dynamics adapting to the mass characteristics of 6-DOF lightweight robotic manipulators is proposed. Based on this, a study on the joint moment estimation method is proposed, A Simplified Calculation Method of External Torque in Joint Space of Lightweight manipulator. In the end, a dynamic drag control experiment of a lightweight robotic arm was designed to verify the correctness of the system method.

Driving forces and status of automation and robotics in construction in Europe