Cyber-physical production systems increasingly involve collaborative robotic missions, which come with a higher demand for robustness and safety. Practitioners rely on risk assessments to identify potential failures and implement measures to mitigate their risks. Ensuring that mitigation strategies derived from risk assessments are adequately considered in the software implementation can be challenging, especially when stakeholders involved in the assessment process lack a programming background. This leads to a disconnection between the outputs of risk assessments and the actual implementation of robotic missions. To address this issue, there is a need to integrate software engineering practices into the risk assessment process to ensure consistency and traceability between the outputs of risk assessments and their corresponding software implementation.This paper presents a design science study that conceived a model-based approach for early risk assessment in a development-centric way. Our approach supports risk assessment activities by using behavior-tree models. We evaluated the approach together with five practitioners from four companies. This approach is the first attempt to use behavior-tree models to support risk assessment. Our findings highlight the potential of behavior-tree models in supporting early identification, visualization, and bridging the gap between code implementation and the outputs of risk assessments. Our findings suggest research directions for further development of the approach to increase its applicability and usefulness in practice.

Conventional programming of explicit control code is unsuitable for flexible and collaborative production systems. A model-based approach, which focuses on defining capabilities of a system, instead of specifying how to achieve them, provides an alternative for creating complex, scalable, and reliable systems. This is accomplished through the use of behavior models, and tools such as planning, synthesis, verification, and testing. However, developing such models is not without challenges, as it is possible to overlook or incorrectly specify potential behavior and constraints. This can result in unsolvable planning problems or plans that are invalid for other reasons. When plans are unobtainable, developers receive no feedback, which makes model adjustments a difficult and time-intensive task. This paper recognizes these challenges as crucial barriers for adopting model-based development of intelligent automation systems. To facilitate the development of such systems, an approach for detecting and localizing faults in behavior models is presented. Drawing inspiration from software fault localization techniques, the proposed method involves identifying suspicious resources, variables, and operations. The effectiveness of this approach is illustrated with an example use case.

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.

The development of efficient propositional satisfiability problem solving algorithms (SAT solvers) in the past two decades has made automated planning using SAT-solvers an established AI planning approach. Modern SAT solvers can accommodate a wide variety of planning problems with a large number of variables. However, fast computing of reasonably long plans 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.

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.

In order to adapt to stricter system delivery and integration requirements, virtual commissioning (VC) has become a well adopted practice in industry. VC is getting increasingly integrated into the overall engineering process, where the control software is continuously tested with the virtual plant model. At the same time, collaborative and intelligent automation systems are becoming an important part of modern industries. In these complex systems, humans perform operations together with collaborative robots, intelligent machines and smart tools. However, performing VC of such complex, distributed and heterogeneous systems demands new ways of interfacing different hardware and software components. This paper discusses the requirements, process and results of integrated virtual commissioning of an industrial collaborative and intelligent automation system use-case. Moreover, this industrial use-case illustrates challenges and exemplifies the need to use the next generation Robot Operating System (ROS2) due to its robust communication layer as well as easy integration with smart devices and algorithms.

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.