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

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

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

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