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

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

Interactive formal specification for efficient preparation of intelligent automation systems

The automation system of the future will consist of an increasing amount of complex resources, such as collaborative robots and/or autonomously roaming robots for material handling. To control these devices in an environment shared with human operators require state of the art computer perception and motion planning algorithms to be used as part of the automation system. This new type of intelligent automation system, where intelligent machines and learning algorithms are replacing more traditional automation solutions, requires new methods and workflows to keep up with the increase in complexity. This paper presents an interactive and iterative framework for solving some of these new challenges. The framework supports model-based control system preparation performed simultaneously to preparation of 3D geometries, positioning of robots, and tool design. The workflow enables an interactive preparation process, where new resources and constraints can be added to a live (real or simulated) automation system and control system failures can be analyzed in familiar tools for virtual preparation. Additionally, the paper describes how the integrated preparation process was applied to reconfiguring an industrial use case that includes a collaborative robot working side by side with a human operator, smart tools, and a vision system for localizing both work objects and tools

Application of the sequence planner control framework to an intelligent automation system with a focus on error handling

Future automation systems are likely to include devices with a varying degree of autonomy, as well as advanced algorithms for perception and control. Human operators will be expected to work side by side with both collaborative robots performing assembly tasks and roaming robots that handle material transport. To maintain the flexibility provided by human operators when introducing such robots, these autonomous robots need to be intelligently coordinated, i.e., they need to be supported by an intelligent automation system. One challenge in developing intelligent automation systems is handling the large amount of possible error situations that can arise due to the volatile and sometimes unpredictable nature of the environment. Sequence Planner is a control framework that supports the development of intelligent automation systems. This paper describes Sequence Planner and tests its ability to handle errors that arise during execution of an intelligent automation system. An automation system, developed using Sequence Planner, is subjected to a number of scenarios where errors occur. The error scenarios and experimental results are presented along with a discussion of the experience gained in trying to achieve robust intelligent automation

Development of an Industry 4.0 Demonstrator Using Sequence Planner and ROS2

In many modern automation solutions, manual off-line programming is being replaced by online algorithms that dynamically perform tasks based on the state of the environment. Complexities of such systems are pushed even further with collaboration among robots and humans, where intelligent machines and learning algorithms are replacing more traditional automation solutions. This chapter describes the development of an industrial demonstrator using a control infrastructure called Sequence Planner (SP), and presents some lessons learned during development. SP is based on ROS2 and it is designed to aid in handling the increased complexity of these new systems using formal models and online planning algorithms to coordinate the actions of robots and other devices. During development, SP can auto generate ROS nodes and message types as well as support continuous validation and testing. SP is also designed with the aim to handle traditional challenges of automation software development such as safety, reliability and efficiency. In this chapter, it is argued that ROS2 together with SP could be an enabler of intelligent automation for the next industrial revolution