Towards an infrastructure for preparation and control of intelligent automation systems

In an attempt to handle some of the challenges of modern production, intelligent automation systems offer solutions that are flexible, adaptive, and collaborative. Contrary to traditional solutions, intelligent automation systems emerged just recently and thus lack the supporting tools and infrastructure that traditional systems nowadays take for granted. To support efficient development, commissioning, and control of such systems, this thesis summarizes various lessons learned during years of implementation. Based on what was learned, this thesis investigates key features of infrastructure for modern and flexible intelligent automation systems, as well as a number of important design solutions. For example, an important question is raised whether to decentralize the global state or to give complete access to the main controller.Moreover, in order to develop such systems, a framework for virtual preparation and commissioning is presented, with the main goal to offer support for engineers. As traditional virtual commissioning solutions are not intended for preparing highly flexible, collaborative, and dynamic systems, this framework aims to provide some of the groundwork and point to a direction for fast and integrated preparation and virtual commissioning of such systems.Finally, this thesis summarizes some of the investigations made on planning as satisfiability, in order to evaluate how different methods improve planning performance. Throughout the thesis, an industrial material kitting use case exemplifies presented perspectives, lessons learned, and frameworks

Towards compositional automated planning

The development of efficient propositional satisfiability problem solving algorithms (SAT solvers) in the past two decades has made automated planning using SAT-solvers\ua0an established AI planning approach. Modern SAT solvers can\ua0accommodate a wide variety of planning problems with a large number of variables. However, fast computing of reasonably long\ua0plans proves challenging for planning as satisfiability. In order to address this challenge, we present a compositional approach based on abstraction refinement that iteratively generates, solves and composes partial solutions from a parameterized planning problem. We show that this approach decomposes the monolithic planning problem into smaller problems and thus significantly speeds up plan calculation, at least for a class of tested planning problems

Evaluation of high level methods for efficient planning as satisfiability

Fast planning algorithms play a key role in intelligent automation systems where control sequences are constantly calculated. In order to determine which algorithms increase planning performance, we evaluate and compare several high level planning methods on a set of standard benchmarks. We focus on planning as satisfiability as the leading approach for solving difficult planning problems

Sequence Planner: A Framework for Control of Intelligent Automation Systems

This paper presents a framework that tackles the challenges met in the development of automation systems featuring collaborative robotics and other machines that have some degree of autonomy. These machines rely on online algorithms for both sensing and acting in order to achieve a very high level of flexibility. To take advantage of these new machines and algorithms, control systems must also be increasingly flexible. In this paper, we present a framework for control of this new class of intelligent automation systems called Sequence Planner (SP), which helps with control of both traditional automation equipment and machines with autonomy. To aid the complex task of developing automation control solutions, SP relies on supporting algorithms for control logic synthesis and online planning. SP has been implemented with plug-in support for the Robot Operating System (ROS) and applied to an industrial demonstrator. We present our findings on how SP performed as a control system for this demonstrator, where we show that it is an adequate approach to implement automation for a highly flexible single station system. As a standardized way of automating such systems is missing, we hope that our contribution will provide a foundation for how to develop intelligent automation systems

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

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

Comparison of Exact and Approximate methods for the Vehicle Routing Problem with Time Windows

This paper presents a comparison of two approaches for solving the vehicle routing problem with time windows (VRPTW). Scheduling of vehicles for pickup and delivery is a common problem in logistics and may be expressed as VRPTW, for which both exact and approximate techniques are available. It is therefore interesting to compare such techniques to evaluate their performance and figure what is the best option based on the instance features and size. In this work, we compared Mixed Integer Linear Programming (MILP) with Set-Based Particle Swarm optimization (S-PSO). Both algorithms are tested on the full 56 instances of the Solomon dataset. The results show that the two algorithms perform similarly for lower number of customers while there are significant differences for the cases with higher number of customers. For higher number of customers MILP consistently performs as good as or better than S-PSO for the clustered data, both with short and long scheduling horizons, while the S-PSO outperforms MILP in most cases with random and mixed random clustered data with long scheduling horizons. Furthermore when the algorithms perform the same with regards to the main objective (number of vehicles), MILP generally achieves a better result in the second objective (distance traveled)

Industrial Challenges when Planning and Preparing Collaborative and Intelligent Automation Systems for Final Assembly Stations

During the last five decades, automation and robotics have transformed the automotive industry by increasing efficiency and improving the product quality. However, future trucks that will be autonomous, electrical and connected will require a completely new type of flexibility and intelligence in the production systems, especially in the final assembly. To handle the increased complexity of the products, production processes and logistic systems, final assembly must be transformed into collaborative and intelligent automation systems. These systems will include collaborative and deliberative robots (cobots), advanced vision-based control, adaptive safety systems, online optimization and learning algorithms and connected and well-informed human operators. But it will be a huge undertaking to transform current trucks industry such that they can design, implement and maintain large scale collaborative and intelligent automation systems. This paper presents the challenges with current planning and preparation processes for final assembly as well as the requirement and possible solutions for the future processes. An industrial use case at Volvo Trucks based on Sequence Planner and ROS2 is used to evaluate the proposed planning and preparation processes