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

Industry 4.0 smart reconfigurable manufacturing machines

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

This survey paper discusses opportunities and threats of using artificial intelligence (AI) technology in the manufacturing sector with consideration for offensive and defensive uses of such technology. It starts with an introduction of Industry 4.0 concept and an understanding of AI use in this context. Then provides elements of security principles and detection techniques applied to operational technology (OT) which forms the main attack surface of manufacturing systems. As some intrusion detection systems (IDS) already involve some AI-based techniques, we focus on existing machine-learning and data-mining based techniques in use for intrusion detection. This article presents the major strengths and weaknesses of the main techniques in use. We also discuss an assessment of their relevance for application to OT, from the manufacturer point of view. Another part of the paper introduces the essential drivers and principles of Industry 4.0, providing insights on the advent of AI in manufacturing systems as well as an understanding of the new set of challenges it implies. AI-based techniques for production monitoring, optimisation and control are proposed with insights on several application cases. The related technical, operational and security challenges are discussed and an understanding of the impact of such transition on current security practices is then provided in more details. The final part of the report further develops a vision of security challenges for Industry 4.0. It addresses aspects of orchestration of distributed detection techniques, introduces an approach to adversarial/robust AI development and concludes with human–machine behaviour monitoring requirements.

Artificial intelligence, cyber-threats and Industry 4.0: challenges and opportunities

\n Collaborative robotics is an umbrella term that conveys the general idea of proximity between machines and humans for some useful tasks in a shared space, with a range of options for timing (continuously, synchronously, alternately, etc.). While this type of human robot interaction is fairly common in many fields of robotics, this survey is dedicated to the technology and situations that are typical of industrial applications. The current state-of-the-art in collaborative robotics for industrial applications is the result of a long legacy of research and development in actuation principles and mechanisms from 2000s onward, together with an abundant literature about improving control performances, intuitive interaction modes, and perception with sensors. Safety technology and standardization is discussed according to the principles of risk estimation and the typical strategies for risk reduction that are suitable for different classes of robot systems are used in collaborative applications. The organization of applications (planning, role assignment, optimization of resources) has a major impact on both the quality and the safety of collaborative robotics solutions, with most of the literature focused on investigating those performance metrics that combine human factors and production figures. The survey is intended to delineate an interpretation key for the design of collaborative robotics solution that explains the relationship among all relevant factors: actuation, control, safety, physical interaction, usability, and productivity.

Collaborative Robotics: A Survey

Cobot programming for collaborative industrial tasks: An overview

Effective Prognostics and Health Management (PHM) for cutting tools during Computerized Numerical Control (CNC) processes can significantly reduce downtime and decrease losses throughout manufacturing processes In recent years, deep learning algorithms have demonstrated great potentials for PHM However, the algorithms are still hindered by the challenge of the limited amount data available in practical manufacturing situations for effective algorithm training To address this issue, in this research, a transfer learning enabled Convolutional Neural Networks (CNNs) approach is developed to predict the health state of cutting tools With the integration of a transfer learning strategy, CNNs can effectively perform tool health state prediction based on a modest number of the relevant images of cutting tools Quantitative benchmarks and analyses on the performance of the developed approach based on six typical CNNs models using several optimization techniques were conducted The results indicated the suitability of the developed approach, particularly using ResNet-18, for estimating the wear width of cutting tools Therefore, by exploiting the integrated design of CNNs and transfer learning, viable PHM strategies for cutting tools can be established to support practical CNC machining applications

Transfer learning enabled convolutional neural networks for estimating health state of cutting tools

An approach of Task-Parameterised Learning from Demonstration (TP-LfD) aims at automatically adapting the movements of collaborative robots (cobots) to new settings using knowledge learnt from demonstrated paths. The approach is suitable for encoding complex relations between a cobot and its surrounding, i.e., task-relevant objects. However, further efforts are still required to enhance the intelligence and adaptability of TP-LfD for dynamic tasks. With this aim, this paper presents an improved TP-LfD (iTP-LfD) approach to program cobots adaptively for a variety of industrial tasks. iTP-LfD comprises of three main improvements over other developed TP-LfD approaches: 1) detecting generic visual features for frames of reference (frames) in demonstrations for path reproduction in new settings without using complex computer vision algorithms, 2) minimising redundant frames that belong to the same object in demonstrations using a statistical algorithm, and 3) designing a reinforcement learning algorithm to eliminate irrelevant frames. The distinguishing characteristic of the iTP-LfD approach is that optimal frames are identified from demonstrations by simplifying computational complexity, overcoming occlusions in new settings, and boosting the overall performance. Case studies for a variety of industrial tasks involving different objects and scenarios highlight the adaptability and robustness of the iTP-LfD approach.

iTP-LfD: Improved task parametrised learning from demonstration for adaptive path generation of cobot

Task-Parameterized Learning from Demonstrations (TP-LfD) is an intelligent intuitive approach to support collaborative robots (cobots) for various industrial applications. Using TP-LfD, human’s demonstrated paths can be learnt by a cobot for reproducing new paths for the cobot to move along in dynamic situations intelligently. One of the challenges to applying TP-LfD in industrial scenarios is how to identify and optimize critical task parameters of TP-LfD, i.e., frames in demonstrations. To overcome the challenge and enhance the performance of TP-LfD in complex manufacturing applications, in this paper, an improved TP-LfD approach is presented. In the approach, frames in demonstrations are autonomously chosen from a pool of generic visual features. To strengthen computational convergence, a statistical algorithm and a reinforcement learning algorithm are designed to eliminate redundant frames and irrelevant frames respectively. Meanwhile, a B-Spline cut-in algorithm is integrated in the improved TP-LfD approach to enhance the path reproducing process in dynamic manufacturing situations. Case studies were conducted to validate the improved TP-LfD approach and to showcase the advantage of the approach. Owing to the robust and generic capabilities, the improved TP-LfD approach enables teaching a cobot to behavior in a more intuitive and intelligent means to support dynamic manufacturing applications.

Shirine El Zaatari

Papers

Cobot programming for collaborative industrial tasks: An overview

Transfer learning enabled convolutional neural networks for estimating health state of cutting tools

iTP-LfD: Improved task parametrised learning from demonstration for adaptive path generation of cobot

An improved approach of task-parameterized learning from demonstrations for cobots in dynamic manufacturing

Ring Gaussian Mixture Modelling and Regression for Collaborative Robots