Collaborative robots (cobots) are robots that are designed to collaborate with humans in an open workspace. In contrast to industrial robots in an enclosed environment, cobots need additional mechanisms to assure humans’ safety in collaborations. It is especially true when a cobot is used in manufacturing environment; since the workload or moving mass is usually large enough to hurt human when a contact occurs. In this article, we are interested in understanding the existing studies on cobots, and especially, the safety requirements, and the methods and challenges of safety assurance. The state of the art of safety assurance of cobots is discussed at the aspects of key functional requirements (FRs), collaboration variants, standardizations, and safety mechanisms. The identified technological bottlenecks are (1) acquiring, processing, and fusing diversified data for risk classification, (2) effectively updating the control to avoid any interference in a real-time mode, (3) developing new technologies for the improvement of HMI performances, especially, workloads and speeds, and (4) reducing the overall cost of safety assurance features. To promote cobots in manufacturing applications, the future researches are expected for (1) the systematic theory and methods to design and build cobots with the integration of ergonomic structures, sensing, real-time controls, and human-robot interfaces, (2) intuitive programming, task-driven programming, and skill-based programming which incorporate the risk management and the evaluations of biomechanical load and stopping distance, and (3) advanced instrumentations and algorithms for effective sensing, processing, and fusing of diversified data, and machine learning for high-level complexity and uncertainty. The needs of the safety assurance of integrated robotic systems are specially discussed with two development examples.

Safety assurance mechanisms of collaborative robotic systems in manufacturing

The advances in Industry 4.0 provide both challenges and opportunities for digital manufacturing and assembly systems. This paper first addresses the state-of-the-art readiness for Industry 4.0 concerning assembly and manufacturing systems through a literature review of the relevant papers recently published. Then it assesses the challenges faced nowadays by assembly and manufacturing systems. Third, it focuses on the most promising future developments and evolution of such production systems as well as their digitalisation. Finally, this manuscript illustrates the content of the papers selected for this special issue. Through the study presented in this special issue, valuable contributions to both theory and application in this area have been achieved, and a useful reference for future research is given.

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Design and management of digital manufacturing and assembly systems in the Industry 4.0 era

Increased global competition has placed a premium on customer satisfaction, and there is a greater demand for manufacturers to be flexible with their products and services. This challenge is usually addressed with the introduction of human operators for precise tasks that require dexterity, flexibility and cognitive decision making. On the other hand, robots, through automation, are very effective in carrying out repetitive, non-ergonomic tasks. Owing to the complementary nature of robots’ and humans’ capabilities, there is an increased interest towards a shared workspace for humans and robots to work together collaboratively, forming the motivation behind the field of human-robot collaboration (HRC). Research in HRC in industry is concerned with the safety of the humans and robots, extent, and modes of collaboration among them, and the level of autonomy and adaptability of robots that can be trained for different tasks. This paper introduces a novel taxonomy of levels of interaction between humans and robots along the lines of SAEs guidelines for autonomous vehicles in response to a need for standard definitions and evolving nature of the field. Research into modes of communication for HRC driven by machine learning are reviewed followed by broad definitions of the types of machine learning. The authors also present a comprehensive review of the machine learning (ML) methodologies and industrial applications of the same in the context of adaptable collaborative robots.

A Survey of Robot Learning Strategies for Human-Robot Collaboration in Industrial Settings

Systematic review on machine learning (ML) methods for manufacturing processes – Identifying artificial intelligence (AI) methods for field application

Increased global competition has placed a premium on customer satisfaction, and there is a greater demand for manufacturers to be flexible with their products and services. This challenge is usually addressed with the introduction of human operators for precise tasks that require dexterity, flexibility and cognitive decision making. On the other hand, robots, through automation, are very effective in carrying out repetitive, non-ergonomic tasks. Owing to the complementary nature of robots’ and humans’ capabilities, there is an increased interest towards a shared workspace for humans and robots to work together collaboratively, forming the motivation behind the field of human-robot collaboration (HRC). Research in HRC in industry is concerned with the safety of the humans and robots, extent, and modes of collaboration among them, and the level of autonomy and adaptability of robots that can be trained for different tasks. This paper introduces a novel taxonomy of levels of interaction between humans and robots along the lines of SAEs guidelines for autonomous vehicles in response to a need for standard definitions and evolving nature of the field. Research into modes of communication for HRC driven by machine learning are reviewed followed by broad definitions of the types of machine learning. The authors also present a comprehensive review of the machine learning (ML) methodologies and industrial applications of the same in the context of adaptable collaborative robots. 

Development of a sociotechnical planning system for human-robot interaction in assembly systems focusing on small and medium-sized enterprises

Human Robot Interaction – learning how to integrate collaborative robots into manual assembly lines

Speed and separation monitoring (SSM) as well as power and force limiting (PFL) are two of the four permissible collaborative operations in human-robot interaction (HRI). Current standards and guidelines provide users and system integrators with a simple basis to calculate permissible separation distances between human workers and robots. However, problems occur in practical implementations, as the safety zones are oversized due to various simplifications and corresponding path velocities have to be significantly reduced. This leads, for example, to cycle time losses and wasted space within the respective HRI application. The present paper describes an approach to combine the SSM and PFL collaborative operations and to exploit the optimization potential for robot motion planning and the design of collaborative workstations. To localize the necessary human body regions, we integrate a method from the field of machine learning. Using an exemplary HRI scenario with the PILZ lightweight robot PRBT, we validate the approach presented here and discuss initial results.

A Hybrid Collaborative Operation for Human-Robot Interaction Supported by Machine Learning

The human-robot collaboration (HRC) is an integral part of numerous research activities. The combination of the positive capabilities of human workers and robots ensures a high resource efficiency and productivity. However, especially the planning and implementation of HRC applications requires the consideration of several issues like the economic feasibility, acceptance of employees and in particular technical practicability. Based on the study of existing approaches which consider different technical or economic issues, we present a new method to identify suitable areas of HRC applications within assembly systems and to estimate the optimal allocation of human-related and automated subtasks. Finally, our approach is verified in an assembly line for terminal strips which in turn arises from an industrial use case.

Task-based Potential Analysis for Human-Robot Collaboration within Assembly Systems

The human-robot collaboration unites positive skills of humans (flexibility, intuition, creativity) and robots (strength, stamina, velocity, precision) in order to ensure a high level of resource efficiency and productivity also with smaller batch sizes and a high variety of versions in production. However, the design of a collaborative assembly system is a complex engineering challenge due to the different goal criteria that have to be considered. A simulation tool for designing and securing human-robot collaboration is yet missing which prevents the widespread use of such technologies. This paper proposes a concept of a simulation tool for collaborative workplaces. In the future, this will ensure that also companies with little experience and limited resources are empowered to implement human-robot collaboration systems prosperously.

Paul Glogowski

Papers

Development of a sociotechnical planning system for human-robot interaction in assembly systems focusing on small and medium-sized enterprises

Human Robot Interaction – learning how to integrate collaborative robots into manual assembly lines

A Hybrid Collaborative Operation for Human-Robot Interaction Supported by Machine Learning

Task-based Potential Analysis for Human-Robot Collaboration within Assembly Systems

Task-based Simulation Tool for Human-Robot Collaboration within Assembly Systems