Synthesizing Executable PLC Code for Robots from Scenario-Based GR(1) Specifications

Gritzner, Daniel; Greenyer, Joel

doi:10.1007/978-3-319-74730-9_23

Cited by 15 publications

(9 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SBM has been used in modeling complex systems, such as robotic controllers (Elyasaf et al, 2019;Gritzner and Greenyer, 2018), webservers , smart buildings (Elyasaf et al, 2018), a nanosatellite (Bar-Sinai et al, 2019), and cache coherence protocols (Harel et al, 2016a).…”

Section: Vanilla Scenario-based Modelingmentioning

confidence: 99%

Towards Repairing Scenario-Based Models with Rich Events

Katz¹

2021

Preprint

View full text Add to dashboard Cite

Repairing legacy systems is a difficult and error-prone task: often, limited knowledge of the intricacies of these systems could make an attempted repair result in new errors. Consequently, it is desirable to repair such systems in an automated and sound way. Here, we discuss our ongoing work on the automated repair of scenario-based models: fully executable models that describe a system using scenario objects that model its individual behaviors. We show how rich, scenario-based models can be model-checked, and then repaired to prevent various safety violations. The actual repair is performed by adding new scenario objects to the model, and without altering existing ones -in a way that is well aligned with the principles of scenario-based modeling. In order to automate our repair approach, we leverage off-the-shelf SMT solvers. We describe the main principles of our approach, and discuss our plans for future work.

show abstract

Section: Vanilla Scenario-based Modelingmentioning

confidence: 99%

Towards Repairing Scenario-Based Models with Rich Events

Katz¹

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…GR(1) synthesis was introduced in [32]. It has since been used and investigated by many, including, e.g., Kress-Gazit et al [20], who used GR(1) in robotics; Maoz and Ringert [25], who showed GR(1) synthesis for specification patterns; D'Ippolito et al [9], [10], who used GR(1) to deal with fallible domains and non-anomalous event-based behavior models; and Gritzner and Greenyer [15], who used scenariobased GR(1) specifications to synthesize executable PLC code. Several tools support GR(1) synthesis [2], [12], [35].…”

Section: B Repair Of Gr(1) Specificationsmentioning

confidence: 99%

Symbolic Repairs for GR(1) Specifications

Maoz

Ringert

Shalom

2019

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)

View full text Add to dashboard Cite

Unrealizability is a major challenge for GR(1), an expressive assume-guarantee fragment of LTL that enables efficient synthesis. Some works attempt to help engineers deal with unrealizability by generating counter-strategies or computing an unrealizable core. Other works propose to repair the unrealizable specification by suggesting repairs in the form of automatically generated assumptions.In this work we present two novel symbolic algorithms for repairing unrealizable GR(1) specifications. The first algorithm infers new assumptions based on the recently introduced JVTS. The second algorithm infers new assumptions directly from the specification. Both algorithms are sound. The first is incomplete but can be used to suggest many different repairs. The second is complete but suggests a single repair. Both are symbolic and therefore efficient.We implemented our work, validated its correctness, and evaluated it on benchmarks from the literature. The evaluation shows the strength of our algorithms, in their ability to suggest repairs and in their performance and scalability compared to previous solutions. 1 env boolean r; // request 2 env boolean c; // clear 3 4 sys boolean g; // grant 5 sys boolean v; // valid 6 7 // sys responds with grant for every request 8 gar respond: pRespondsToS(r, next(g)); 9 10 // if cleared or granted no immediate next grant 11 gar ungrant: G ((c | g) -> next(!g)); 12 13 // cleared request means not valid 14 gar exclude: G (c -> !v); 15 16 // infinitely often give valid grants 17 gar just: GF (g & v);

show abstract

“…From a resulting strategy, PCL code can be generated [12]. We extended the synthesis algorithm to extract a strategy where the energy cost per item processed is minimal [11]. This extension is based on a function to determine the overall energy cost of an execution sequence, which is the energy consumed by all the components minus the recuperated braking energy.…”

Section: Scenario-based Specification and Synthesismentioning

confidence: 99%

“…In order to compute a strategy for this objective, we applied an algorithm for computing strategies in infinite games [10], and extended it to determine the most energyefficient strategy [11]. This extension is based on a function to determine the overall energy cost of path fragments where energy-consuming processes take place and possibly overlap with energy-producing processes, for example, a path fragment between starting and stopping the acceleration of robot A that may overlap with starting and stopping the deceleration of robot B.…”

Section: Introductionmentioning

confidence: 99%

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

Gritzner

Knochelmann

Greenyer

et al. 2018

2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Synthesizing Executable PLC Code for Robots from Scenario-Based GR(1) Specifications

Cited by 15 publications

References 23 publications

Towards Repairing Scenario-Based Models with Rich Events

Towards Repairing Scenario-Based Models with Rich Events

Symbolic Repairs for GR(1) Specifications

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

Contact Info

Product

Resources

About