Robust safety zones for manipulators with uncertain dynamics in collaborative robotics

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.

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

Performance guaranteed human-robot collaboration with POMDP supervisory control

In recent years, Human-Robot Collaboration (HRC) has been widely studied and the demands for human-robot collaboration are expected to increase due to the lack of working population. Regarding to the safety on human-robot collaboration, not only physical safety but also psychological safety has to be considered. However, current collaborative robots only have some limitations for physical safety. In addition, productivity is another problem of collaborative robot because of the speed and payload limitations. To solve these problems, this paper aims at developing a system that can improve the efficiency and perceptions of collaborative work. We designed auditory cues which express the destination of robot motion. Finally, experiment was conducted to evaluate the efficiency of collaborative work, mental stress of collaborators, and perception of collaborative work using both objective and subjective measures. Results revealed that auditory cues significantly improved the efficiency and fluency of collaborative work without negative effects on the mental stress of collaborator.

A proposal of an assembly workstation for car panel fitting aided by an augmented reality device

In recent years, collaborative robots have been introduced into production systems as a new generation of industrial robots, sharing work with workers. Unlike industrial robots and workers in the past to work in isolation, collaborative robots and workers can often share work tasks and space. The introduction of these collaborative robots makes human-robot collaboration (HRC) more and more important, which is an emerging technology in the field of production systems. According to the characteristics of workers and collaborative robots, a measurement model of assembly complexity is first proposed. The assembly tasks are analyzed from the three aspects of process depth, operation process, and cognitive process, and the complexity of each assembly task is evaluated. The indicator of the machine-machine collaboration attribute measures each task to determine whether the task can be completed by a collaborative robot; finally, an assembly task allocation evaluation model is established, to ensure the assembly complexity of each worker’s task is balanced, so that the cognitive load assigned to the robot task is lower than the worker’s task, to maximize production efficiency and reduce the assembly system cost.

The evolution of industrial robotics has advanced from isolated, caged systems through basic human–robot interaction (HRI) to sophisticated human–robot collaboration (HRC). However, conventional vision systems based on red, green, blue (RGB) cameras remain a significant limiting factor in realizing the full potential of collaborative automation. This comprehensive review examines the transformative role of event cameras in advancing HRC capabilities and addressing current limitations in industrial settings. Event cameras, with their microsecond-level temporal resolution and robust performance under challenging lighting conditions, offer substantial advantages over traditional RGB cameras that are constrained by fixed frame rates and ambient lighting dependencies. We present a systematic framework for leveraging event cameras to enhance the human state understanding in collaborative robotics, encompassing real-time detection of poses, gestures, facial expressions, and emotional states. This framework addresses fundamental challenges in workplace safety and collaborative efficiency while enabling more sophisticated and responsive HRC systems. Our review synthesizes recent research developments in event camera applications specific to HRC, providing a detailed comparative analysis of their advantages over conventional vision systems. We identify emerging opportunities and potential research directions for advancing event-based vision in industrial robotics. In addition, we examine integration challenges and propose strategies for implementing event camera technology in existing industrial infrastructure. This work contributes valuable insights into the future trajectory of adaptive and intuitive HRC systems, offering a roadmap for researchers and practitioners in the field of industrial automation.

In many current assembly scenarios, efficient collaboration between humans and robots can improve collaborative efficiency and quality. However, the efficient arrangement of human–robot collaborative (HRC) tasks constitutes a significant challenge. In a collaborative workspace where humans and robots collaborate on assembling a shared product, the determination of task allocation between them is of crucial importance. To address this issue, offline feasible HRC paths are established based on assembly task constraint information. Subsequently, the HRC process is simulated within a virtual environment leveraging these feasible paths. Human assembly intentions are explicitly expressed through human assembly trajectories, and implicitly expressed through simulation results such as assembly time and human–robot resource allocation. Furthermore, a two-stage MLP-LSTM network is employed to train and optimize the assembly simulation database. In the first stage, a sequence generation model is trained using high-quality HRC processes. Then, the network learns human evaluation patterns to score the generated sequences. Ultimately, task allocation for HRC is performed based on the high-scoring generated sequences. The effectiveness of the proposed method is demonstrated through assembly scenarios of two products. Compared with traditional optimization methods like DFS and Greedy, the human collaboration ratio has been optimized by 10%, while the collaborative quality evaluation has been improved by 3%.

Human–robot collaboration can enhance productivity of production lines and reduce human ergonomic risk. The numbers and types of robots and stations in which robots are allocated need to be determined. Operations should be scheduled carefully when a human and robot work on a part in a station to obtain a feasible operation allocation with the highest efficiency and lowest ergonomic risk. A mixed-integer linear programming model, constraint programming model, and Benders decomposition algorithm were developed to analyse advantages of collaborative robots in assembly lines. An energy expenditure method was used to evaluate ergonomic risk. By scheduling and balancing collaborative human–robot assembly lines, operational advantages and scheduling constraints from human–robot collaboration were studied when immobile and mobile robots are used. Regression lines were developed that can help managers determine how many and what types of robots are best for a line and what the impact of robot mobility on robot and line performance can be. The best configuration for equipping a line with collaborative robots is when (number of robots)/(number of stations) is near .7 and about 37% of robots are mobile. Robots can be efficiently used in lines with both a small and large number of passive resources and in simple and mixed-model lines.

The recent automation technology advances have introduced the shared human robot (HR) workspaces concept for the future production lines. This chapter presents the design and application of safety systems that enable the dynamic monitoring of zones inside a human robot collaborative cell. The safety concepts suggested have as a main enabler a 3D safety certified camera that can detect in real time any moving human or other object in the defined area. The discussion starts from a concept of replacing physical fences with virtual safety zones while monitoring human robot distance. As a next step, a more dynamic scheme is proposed by enhancing 3D safety zones with robot side safety functions while enabling the dynamic switching of zones to minimize the allowed HR distance. The control architecture design and the implementation of the systems in two industrial examples from the automotive industry are presented in this chapter.

Introduction of robotics into agriculture has the potential to lower production costs, reduce the drudgery of tedious manual labor, increase the level of accuracy of mechanized operations, and improve environmental control. Unlike industrial applications that often deal with relatively simple, repetitive, well-defined, and known a priori tasks, agriculture robots usually require advanced technologies to deal with the relatively more complex and highly unstructured and dynamic nature of both biological produce and the environment. This chapter reviews agricultural robotic systems with a focus on recent advances in human–robot collaboration.

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

Human Pose Estimation (HPE) is an essential computer vision task that plays a significant role in ergonomics, human-centered design of workplaces, and collaborative robotics, particularly in providing a safe, adaptive, and valuable human-robot collaboration (HRC). Nevertheless, the need for a concise and practical approach to setting up a multicamera and multimodal system for HPE dataset generation remains an open issue in the literature.The main goal of this work is to describe in concise steps a protocol that serves as a guide in the exploratory stage of the approach to construct an extensive multicamera and multimodal dataset used to enhance HRC. In this paper, the proposed protocol specifically addresses the challenge of designing a biomechanical model that can consistently reproduce complex and variable human motion analyses in an assembly task while considering ergonomic factors. Furthermore, the resulting work led to the definition of a marker set for one single-person future pipeline involving the placement of thirty-two reflective markers for 3D motion analysis, specifically emphasizing the upper segments of the human body, including the hands. The future generation of this dataset will hold significant promise for advancing the study of HRC. It will introduce reliable and precise multimodal data collection, such as human kinematics and video data, including depth data, which will then be used for posture metrics analysis. Finally, the dataset will be a valuable resource for the research community, enabling the training of machine learning models. These models will empower collaborative robots (cobots) to learn from human demonstrations, enhancing their efficiency and ergonomic performance in assembly tasks.

Over the years industrial robots had been used for numerous of repeatable tasks in different fields of industry, especially in automotive. Due to the safety reasons, robotized working stations had to be isolated by safety cell. Nowadays we can observe a strong trend of automation and robotization of production processes, which is considered as the fourth industrial revolution. Industry 4.0 has brought new opportunities and challenges. One of the key directions of Industry 4.0 is Human-Robot Collaboration (HRC). According to this concept, collaborative robot, which is equipped with appropriate safety and collision avoidance systems, can assist the human in the most effortable and monotonic tasks in the same workspace without additional safety guard. In this paper the characteristic of Human-Robot Collaboration (HRC), safety regulations, as well as application examples of collaborative robots are presented.

The concept of Human-Robot Collaboration (HRC) describes innovative industrial work procedures, in which human staff works in close vicinity with robots on a shared task. Current HRC scenarios often deploy hand-guided robots or remote controls operated by the human collaboration partner. As HRC envisions active collaboration between both parties, ongoing research efforts aim to enhance the capabilities of industrial robots not only in the technical dimension but also in the robot’s socio-interactive features. Apart from enabling the robot to autonomously complete the respective shared task in conjunction with a human partner, one essential aspect lifted from the group collaboration among humans is the communication between both entities. State-of-the-art research has identified communication as a significant contributor to successful collaboration between humans and industrial robots. Non-verbal gestures have been shown to be contributing aspect in conveying the respective state of the robot during the collaboration procedure. Research indicates that, depending on the viewing perspective, the usage of non-verbal gestures in humans can impact the interpersonal attribution of certain characteristics. Applied to collaborative robots such as the Yumi IRB 14000, which is equipped with two arms, specifically to mimic human actions, the perception of the robots’ non-verbal behavior can affect the collaboration. Most important in this context are dominance emitting gestures by the robot that can reinforce negative attitudes towards robots, thus hampering the users’ willingness and effectiveness to collaborate with the robot. By using a 3 × 3 within-subjects design online study, we investigated the effect of dominance gestures (Akimbo, crossing arms, and large arm spread) working in a standing position with an average male height, working in a standing position with an average female height, and working in a seated position on the perception of dominance of the robot. Overall 115 participants (58 female and 57 male) with an average age of 23 years evaluated nine videos of the robot. Results indicated that all presented gestures affect a person’s perception of the robot in regards to its perceived characteristics and willingness to cooperate with the robot. The data also showed participants’ increased attribution of dominance based on the presented viewing perspective.