The ScenarioTools Play-Out of Modal Sequence Diagram Specifications with Environment Assumptions

Many software-intensive systems consist of multiple components that provide complex functionality by their interaction. The scenario-based languages LSCs and MSDs are intuitive, but precise means to specify interactions; the engineers can specify how a system can, must, or must not react to events in its environment. A key benefit of LSCs/MSDs is that they can be executed via the play-out algorithm, which allows engineers to perform an early automated analysis of the specification. However, LSCs/MSDs lack support for expressing also what can or cannot happen in the environment. This is crucial especially in embedded systems: very often, the software will only be able to satisfy its requirements if certain assumptions are made about the behavior of mechanical parts or the physical environment. We extend MSD specifications to formally express such environment assumptions, and propose a corresponding extension of the play-out algorithm. The concepts are implemented in a novel, Eclipse-based tool

Trajectory Optimization Methods for Robotic Cells Considering Energy Efficiency and Collisions

In production robots are moved at maximum speed whenever possible in order to achieve the shortest overall cycle time. This can lead to individual waiting times, especially in interlinked production processes. These waiting times offer opportunities for optimization. Due to high energy prices and political efforts, energy efficiency has become the focus of trajectory optimization in recent years. Robot cells with a common intermediate circuit offer the possibility of energy exchange across individual axes or robots. By adapting the robot trajectories, the total power consumption of a robotic cell on the grid side can be significantly reduced. This paper focuses on trajectory optimization, whereby a detailed collision detection of individual robots is included within the analysis. It is shown that with collision detection energy optimization for cramped robot cells becomes possible and the losses in efficiency compared to the optimization without it are minute

Specifying and Synthesizing Energy-Efficient Production System Controllers that Exploit Braking Energy Recuperation

Reducing the energy consumption is a major concern in industrial production systems. One approach is recuperating the braking energy of robot axes. Ideally, their acceleration and deceleration phases should be synchronized so that the braking energy of one axis can be reused directly to accelerate another. This requires a detailed alignment of the axes' trajectories, but also a careful design of the overall discrete control. Finding an optimal control strategy manually, however, is difficult, as also many functional and safety requirements must be considered. We therefore propose an automated methodology that consists of three parts: (1) A scenario-based language to flexibly specify the discrete production system behavior, (2) an automated procedure to synthesize optimal control strategies from such specifications, including PLC code generation, and (3) a procedure for the detailed trajectory optimization. We describe the methodology, focusing on parts (1) and (2) in this paper, and present tool support and evaluation results

A Comparison of Incremental Triple Graph Grammar Tools

Triple Graph Grammars (TGGs) are a graph-based and visual technique for specifying bidirectional model transformation. TGGs can be used to transform models from scratch (in the batch mode), but the real potential of TGGs lies in propagating updates incrementally. Existing TGG tools differ considerably in their incremental mode concerning underlying algorithms, user-oriented aspects, incremental update capabilities, and formal properties. Indeed, the different foci, strengths, and weaknesses of current TGG tools in the incremental mode are difficult to discern, especially for non-developers. In this paper, we close this gap by (i) identifying a set of criteria for a qualitative comparison of TGG tools in the incremental mode, (ii) comparing three prominent incremental TGG tools with regard to these criteria, and (iii) conducting a quantitative comparison by means of runtime measurements

ScenarioTools Real-Time Play-Out for Test Sequence Validation in an Automotive Case Study

In many areas, such as automotive, healthcare, or production, we find software-intensive systems with complex real-time requirements. To efficiently ensure the quality of these systems, engineers require automated tools for the validation of the requirements throughout the development. This, however, requires that the requirements are specified in an analyzable way. We propose modeling the specification using Modal Sequence Diagrams (MSDs), which express what a system may, must, or must not do in certain situations. MSDs can be executed via the play-out algorithm to investigate the behavior emerging from the interplay of multiple scenarios; we can also test if traces of the final product satisfy all scenarios. In this paper, we present the first tool supporting the play-out of MSDs with real-time constraints. As a case study, we modeled the requirements on gear shifts in an upcoming standard on vehicle testing and use our tool to validate externally generated gear shift sequences

A tool for the automation of efficient multi-robot choreography planning and execution

In the automotive industry, the design, modeling, and planning of multi-robot cells are manual error-prone, and time-expensive tasks. A recent work investigated, using reactive synthesis, approaches to automate robot task planning, and execution. In this paper, we present a tool that realizes a model-At-runtime approach. The tool is integrated with a robot simulation tool, to automate efficient multi-robot choreography planning, and execution. We illustrate the tool using a multi-robot spot welding cell, inspired from an industrial case. Given a virtual model of the production cell, and user constraints definition, the tool can derive a specification for the reactive synthesis. The tool integrates the synthesized controller with the production cell execution, and in real time, optimizes the strategies by considering the uncertainties. The system can select among several correct, and safe actions, the optimal action using AI-based planning techniques, such as the Monte Carlo Tree Search (MCTS) algorithm. We showcase our tool, illustrate its implementation architecture, including how it can support robot experts for automated planning and execution of production cells