A ROS2 based communication architecture for control in collaborative and intelligent automation systems

Collaborative robots are becoming part of intelligent automation systems in modern industry. Development and control of such systems differs from traditional automation methods and consequently leads to new challenges. Thankfully, Robot Operating System (ROS) provides a communication platform and a vast variety of tools and utilities that can aid that development. However, it is hard to use ROS in large-scale automation systems due to communication issues in a distributed setup, hence the development of ROS2. In this paper, a ROS2 based communication architecture is presented together with an industrial use-case of a collaborative and intelligent automation system.Comment: 9 pages, 4 figures, 3 tables, to be published in the proceedings of 29th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM2019), June 201

Towards safe human robot collaboration - Risk assessment of intelligent automation

Automation and robotics are two enablers for developing the Smart Factory of the Future, which is based on intelligent machines and collaboration between robots and humans. Especially in final assembly and its material handling, where traditional automation is challenging to use, collaborative robot (cobot) systems may increase the flexibility needed infuture production systems. A major obstacle to deploy a truly collaborative application is to design and implement a safe and efficient interaction between humans and robot systems while maintaining industrial requirements such as cost and productivity. Advanced and intelligent control strategies is the enabler when creating this safe, yet efficient, system, but is often hard to design and build.This paper highlights and discusses the challenges in meeting safety requirements according to current safety standards, starting with the mandatory risk assessment and then applying risk reduction measures, when transforming a typical manual final assembly station into an intelligent collaborative station. An important conclusion is that current safety standards and requirements must be updated and improved and the current collaborative modes defined by the standards community should be extended with a new mode, which in this paper is refereed tothedeliberative planning and acting mode

An algorithm for data-driven shifting bottleneck detection

Manufacturing companies continuously capture shop floor information using sensors technologies, Manufacturing Execution Systems (MES), Enterprise Resource Planning systems. The volumes of data collected by these technologies are growing and the pace of that growth is accelerating. Manufacturing data is constantly changing but immediately relevant. Collecting and analysing them on a real-time basis can lead to increased productivity. Particularly, prioritising improvement activities such as cycle time improvement, setup time reduction and maintenance activities on bottleneck machines is an important part of the operations management process on the shop floor to improve productivity. The first step in that process is the identification of bottlenecks. This paper introduces a purely data-driven shifting bottleneck detection algorithm to identify the bottlenecks from the real-time data of the machines as captured by MES. The developed algorithm detects the current bottleneck at any given time, the average and the non-bottlenecks over a time interval. The algorithm has been tested over real-world MES data sets of two manufacturing companies, identifying the potentials and the prerequisites of the data-driven method. The main prerequisite of the proposed data-driven method is that all the states of the machine should be monitored by MES during the production run

Control components for Collaborative and Intelligent Automation Systems

Collaborative and intelligent automation systems need intelligent control systems. Some of this intelligence exist on a per-component basis in the form of vision, sensing, motion, and path planning algorithms. To fully take advantage of this intelligence, also the coordination of subsystems need to exhibit intelligence. While there exist middleware solutions that eases communication, development, and reuse of such subsystems, for example the Robot Operating System (ROS), good coordination also requires knowledge about how control is supposed to be performed, as well as expected behavior of the subsystems. This paper introduces lightweight components that wraps ROS2 nodes into composable control components from which an intelligent control system can be built. The ideas are implemented on a use case involving collaborative robots with on-line path planning, intelligent tools, and human operators

Data quality problems in discrete event simulation of manufacturing operations

High-quality input data are a necessity for successful discrete event simulation (DES) applications, and there are available methodologies for data collection in DES projects. However, in contrast to standalone projects, using DES as a daily manufacturing engineering tool requires high-quality production data to be constantly available. In fact, there has been a major shift in the application of DES in manufacturing from production system design to daily operations, accompanied by a stream of research on automation of input data management and interoperability between data sources and simulation models. Unfortunately, this research stream rests on the assumption that the collected data are already of high quality,and there is a lack of in-depth understanding of simulation data quality problems from a practitioners’ perspective.Therefore, a multiple-case study within the automotive industry was used to provide empirical descriptions of simulation data quality problems, data production processes, and relations between these processes and simulation data quality problems. These empirical descriptions are necessary to extend the present knowledge on data quality in DES in a practical real-world manufacturing context, which is a prerequisite for developing practical solutions for solving data quality problems such as limited accessibility, lack of data on minor stoppages, and data sources not being designed for simulation. Further, the empirical and theoretical knowledge gained throughout the study was used to propose a set of practical guidelines that can support manufacturing companies in improving data quality in DES

Risk Assessment and Safety Measures for Intelligent and Collaborative Automation

In the truck industry, manual final assembly and material handling processes can be complex and crowded, making their automation difficult using traditional industrial robots. Collaborative robot systems, on the other hand, offer a flexible and user-friendly alternative that can free up human workers from repetitive and non-ergonomic tasks, allowing them to focus on more value-adding operations. Despite the considerable efforts made by researchers and within the industry to promote collaborative robots, they are often underused and their use is limited to handling simple automation tasks without perimeter fences. The aim of this thesis is to enhance our understanding of human-robot collaboration and the challenges faced by complex industries when implementing intelligent and collaborative automation. The goal is to create a sustainable workplace where robots and humans can work together safely and efficiently in a flexible environment. Through several industrial use cases, two demonstration setups were developed to identify a set of industrial challenges and requirements. These requirements include safe, efficient, and intuitive interactions, as well as deliberative and robust control, reliable communication, variant handling, and an efficient engineering process. However, the most critical requirement is ensuring the safety of both machines and humans. It was found that current safety standards trade safety for efficiency, flexibility, and cost, which limits the implementation of intelligent and adaptive collaborative systems in complex applications. To address these issues, a new safety approach called deliberative safety is proposed, which allows for switching between different safety measures depending on whether flexibility or efficiency is required to attain production goals. A taxonomy is proposed to better support the design of deliberative safety, along with five safety measures ranging from currently existing measures like perimeter safety to planned and active safety. These measures can enable intelligent human-robot collaboration. However, incorporating intelligence and using the deliberative safety concept may introduce new types of risks, which necessitates the development of new risk assessment and risk reduction methods. To address this, a risk assessment method based on reliability theory is combined with a novel method based on system theory to identify system requirements in the early stages of development and to identify risky scenarios and related risk reduction methods. The findings of this research will be beneficial to manufacturing industries seeking to use intelligent and collaborative automation to increase flexibility when automating. Additionally, they will provide valuable inputs for the development of related safety standards and risk assessment procedures

Risk Assessment and Safety Measures for Intelligent and Collaborative Automation

In the truck industry, manual final assembly and material handling processes can be complex and crowded, making their automation difficult using traditional industrial robots. Collaborative robot systems, on the other hand, offer a flexible and user-friendly alternative that can free up human workers from repetitive and non-ergonomic tasks, allowing them to focus on more value-adding operations. Despite the considerable efforts made by researchers and within the industry to promote collaborative robots, they are often underused and their use is limited to handling simple automation tasks without perimeter fences. The aim of this thesis is to enhance our understanding of human-robot collaboration and the challenges faced by complex industries when implementing intelligent and collaborative automation. The goal is to create a sustainable workplace where robots and humans can work together safely and efficiently in a flexible environment. Through several industrial use cases, two demonstration setups were developed to identify a set of industrial challenges and requirements. These requirements include safe, efficient, and intuitive interactions, as well as deliberative and robust control, reliable communication, variant handling, and an efficient engineering process. However, the most critical requirement is ensuring the safety of both machines and humans. It was found that current safety standards trade safety for efficiency, flexibility, and cost, which limits the implementation of intelligent and adaptive collaborative systems in complex applications. To address these issues, a new safety approach called deliberative safety is proposed, which allows for switching between different safety measures depending on whether flexibility or efficiency is required to attain production goals. A taxonomy is proposed to better support the design of deliberative safety, along with five safety measures ranging from currently existing measures like perimeter safety to planned and active safety. These measures can enable intelligent human-robot collaboration. However, incorporating intelligence and using the deliberative safety concept may introduce new types of risks, which necessitates the development of new risk assessment and risk reduction methods. To address this, a risk assessment method based on reliability theory is combined with a novel method based on system theory to identify system requirements in the early stages of development and to identify risky scenarios and related risk reduction methods. The findings of this research will be beneficial to manufacturing industries seeking to use intelligent and collaborative automation to increase flexibility when automating. Additionally, they will provide valuable inputs for the development of related safety standards and risk assessment procedures