Collaborative Robotics in Construction: A Test Case on Screwing Gypsum Boards on Ceiling

Current paper deals with a new tendency in robotics, consisting in broader and broader exploiting of socalled collaborative robots, sharing their workspace with humans. An overview of research in this area has been performed. Some aspects of robot programming method, called programming by demonstration, have been discussed. In particular, the authors try to answer the question, whether the idea of direct human-robot cooperation can be applied also in welding. Examples of research in this area have been presented. The paper discusses some as- pects of human-robot communication. It presents a conception of a novel method of calibration of human-robot voice communication system, now under development at the Warsaw University of Technology. This method is focused on specific requirements resulting from the use of collaborative robots.

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.

Driven by digitalization in supply chains, the use of Collaborative Robots (COBOTs) has become increasingly widespread in recent years. They significantly contribute to process efficiency by working in place of, or in collaboration with, humans in a variety of operations, including welding, painting, assembly and disassembly, transportation, packaging, and palletizing. However, when uncertainty and different criteria are taken into account, decision support systems that compare practical robots based on their suitability for specific needs are inadequate. This study presents a comprehensive multi-criteria decision-making (MCDM) framework for prioritizing COBOTs with different features used in digital supply chain processes. Based on in-depth research in the literature and the opinions of experts working in companies that use relevant robots in the industry, the criteria to be evaluated when selecting COBOT types are identified. The importance of these criteria was determined using the Picture Fuzzy Step-wise Weight Assessment Ratio Analysis (PiF-SWARA) method, which effectively captures the uncertainty in experts’ decisionmaking processes. Subsequently, alternative COBOT types were ranked using the Picture Fuzzy Combinative Distance-Based Assessment (PiFCODAS) approach. This case study, which evaluates the PiF-SWARACODAS concept, reveals that according to expert assessments, cost is the most important criterion in COBOT selection, followed by process quality and space utilization. The findings about the selection of types emphasize that high-efficiency articulated robots operating at high speeds under mass production conditions are the primary priority. These robots are followed by humanoid robots. The third most important are power and force-limiting robots. The fourth and fifth types of COBOTs are hand-guided and safety-monitored stop robots. Validation and sensitivity analyses confirmed the robustness of the results. Overall, the proposed framework not only clarifies the key priorities for manufacturing facilities but also provides a validated decision support tool to align digitalization strategies with the most appropriate COBOT investments.OPEN ACCESS Received: 25/08/2025 Accepted: 15/10/2025 Published: 15/12/2025 An extensive literature review was conducted to examine previous research on COBOT applications in the supply chain, to identify evaluation criteria and alternatives for this study, and to highlight the originality of this study. The concept of COBOT was included in books, journals and conference proceedings accessible through the SCOPUS database. However, the literature search with the related keyword found too many studies. Therefore, the scope was further customized to include different types of COBOT models that can be used throughout the supply chain. To make the literature review process more systematic, the PRISMA methodology, which stands for “Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)”, was used. The PRISMA approach, designed by Moher et al. [10], has become widely used among academics in recent years. Sources are analyzed and evaluated according to the determined eligibility criteria. Then, appropriate studies are selected. Table 1lists the keywords used to perform a PRISMA method literature review, along with the number of studies found using these Table 1: Literature search with A total of 1179 studies were found in SCOPUS after searching with the were found. The results were duplicated because some

As collaborative robots begin to appear on factory floors, there is a need to consider how these robots can best help their human partners. In this paper, we propose an optimization framework that generates task assignments and schedules for a human-robot team with the goal of improving both time and ergonomics and demonstrate its use in six real-world manufacturing processes that are currently performed manually. Using the strain index method to quantify human physical stress, we create a set of solutions with assigned priorities on each goal. The resulting schedules provide engineers with insight into selecting the appropriate level of compromise and integrating the robot in a way that best fits the needs of an individual process.

The use of collaborative robots in the industrial domain has significantly grown in the last years, allowing humans to operate in the same workspace occupied by robots without any physical barriers. Understandably, the safety of the human operator has been a major concern both for researchers and regulatory bodies. The power and force limited modality of robots is of particular interest in that sense, being used in order to bound the energy of eventual collisions when a close physical interaction with humans is necessary. Such an interaction modality allows the robotic system to operate without the use of barriers, but a measurement of the force and pressure occurring due to a contact must be provided as part of the risk assessment. However, the precise procedure to follow in order to reliably provide such measures is still unclear for users and system integrators willing to self-assess the safety of their own collaborative robotic system. In this work, the repeatability and reliability of such testing procedures and measures are analyzed with an interlaboratory comparison approach, with the aim to establish the degree of variability possibly encountered when performing the same test under slightly different conditions.

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

As the development of robotics technologies for collaborative robots (COBOTs), the applications of human–robot collaboration (HRC) have been growing in the past decade. Despite the tremendous efforts from both academia and industry, the overall usage and acceptance of COBOTs are still not so high as expected. One of the major affecting factors is the comfort of humans in HRC, which is usually less emphasized in COBOT development; however, it is critical to the user acceptance during HRC. Therefore, this paper gives a review of human comfort in HRC including the influential factors of human comfort, measurement of human comfort in terms of subjective and objective manners, and human comfort improvement approaches in the context of HRC. Discussions on each topic are also conducted based on the review and analysis.

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.

Integrating robotics in construction works has the potential to address persistent challenges such as labor shortages, health risks, and low productivity. This paper proposes a systematic task planning approach for human-robot collaboration (HRC) in structure assembly, comprising a robotic potential scoring-based task classification system and a constraint programming-based task allocation and sequencing optimization model. Based on key factors such as component properties, connection methods, robotic payload capacities, material storage layouts, and workspace safety, the task classification system provides a systematic approach to classifying construction tasks. To obtain optimal task allocation and sequencing, an extended flexible job shop scheduling problem (FJSSP) based optimization model integrates human fatigue into the objective function to balance operational efficiency and worker well-being. A timber frame assembly case study with different HRC configurations and storage arrangements was conducted to evaluate the performance in enhancing makespan and reducing human fatigue. This work establishes a robust foundation for optimizing HRC in structure assembly, paving the way for more effective and human-centric practices in the industry. • Proposed a task classification system based on robotic potential evaluation • Developed a constraint programming model optimizing efficiency and human fatigue • Found high-capability robots enhance flexibility in collaboration with human worker • Guided design and application of robot deployment in structural assembly • Provided task sequencing and allocation combinations with varying emphases

Human in the loop online estimation of robotic speed limits for safe human robot collaboration

Human-robot collaboration (HRC), as a part of Industry 4.0 strategy, requires a completely new type of robots able to co-work with humans, called collaborative robots or cobots. This kind of collaboration is especially needed in assembly systems, which are known for having a low level of automation. For some assembly tasks human is still an irreplaceable factor. On the other hand, some assembly tasks are monotonous and tiring for humans. Therefore, the different approaches to cope with the challenge of identification and selection of proper task allocation between human worker and cobots are reported by many researchers. It is not an easy task since multiple and often conflicting criteria need to be taken into account. Some kind of artificial decision support is needed, to successfully solve this problem. In this research, task allocation procedure is presented for identification of different improvement options that utilize cobots into the assembly line for different tasks to be performed. The decision support system based on the HUMANT algorithm has been used for selection of the option which represents the best compromise solution. The procedure is experimentally tested on the assembly line with car gearboxes as a real product.

Intuitive Robot Programming Using Augmented Reality

Construction robots employ cutting-edge technology to perform tasks more accurately than traditional construction workers, producing higher-quality results and fewer mistakes. Moreover, although construction robotics is a demanding topic in construction sector research, more review studies that track and anticipate adoption trends are required in the construction sector. This study aims to bridge this gap by identifying the adoption challenges and limitations of construction robots and the opportunities offered to the construction sector. To achieve this aim, the study adopts a systematic literature review approach using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol. Additionally, the systematic literature review focuses on the framework for categorizing technological advances and potential trends in development over the past decade. The review results reveal that: (a) current robotic technology covered four critical perspectives including perception, mobility, manipulation, and collaboration; (b) promoting the sector requires attention to safety and ethical issues because of the risks associated.

Scaling up and automating business processes makes one of the greatest contributions to the development of the business. The growth of sales of the goods or services may lead to the sophistication of the enterprise's business processes, increased number of staff, and the necessity to create new departments and divisions. Unfortunately, increased number of employees does not always correlate with the increase in the revenue of the enterprise. Instead, forced business process automation is regarded to be more effective. The integration of robotics solutions is one of the main ways to automate the production. This kind of modernization is one of the key stages to ensure further development within the Fourth Industrial revolution. The main result of the business processes automation and the whole Fourth Industrial Revolution (Industry 4.0) will be a fully “unmanned” production facility, where robotic solutions have entirely replaced the employees at their workspaces. In this way, robots will be able to perform those kinds of work that are dangerous for the humans. The robots-based production processes can be continuous bringing a positive impact on the efficiency and the productivity of the enterprise. This paper describes the automation of the business process on production facilities with the use of collaborative robotics.

This scientific paper examines the use of collaborative robots in the context of Industry 5.0 and its impact on practical engineering education. The aim of this paper is to highlight the importance as well as the challenges of using robots in Industry 5.0 and to analyze how this can be integrated into engineering education. The paper discusses various aspects such as the technological advances, the need for interaction between humans and robots, and the competencies that engineers need in an increasingly digitalized working world. Furthermore, practical methods and approaches in engineering education at the Zittau/Görlitz University of Applied Sciences are presented to prepare students for the requirements of Industry 5.0. Additionally, a draft of a future interdisciplinary learning factory 5.0 will be presented, in which networked technologies with the focus on robotics run through various scenarios in the context of Industry 5.0 at different locations.