Improved Domain Modeling for Realistic Automated Planning and Scheduling in Discrete Manufacturing

Rogalla, Antje; Fay, Alexander; Niggemann, Oliver

doi:10.1109/etfa.2018.8502631

Cited by 16 publications

(11 citation statements)

References 10 publications

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“…An application of PDDL for industrial problems including their formalization is addressed in [43]. It provides a collection of simple prototypical cases.…”

Section: Production Planning For Industrial Production Systems Enable...mentioning

confidence: 99%

Digitalized Automation Engineering of Industry 4.0 Production Systems and Their Tight Cooperation with Digital Twins

Novák

Vyskočil

2022

Processes

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Smart production systems conforming the Industry 4.0 vision are based on subsystems that are integrated in a way that supports high flexibility and re-configurability. Specific components and devices, such as industrial and mobile robots or transport systems, now pose full-blown systems, and the entire Industry 4.0 production system constitutes a system-of-systems. Testing, fine-tuning, and production planning are important tasks in the entire engineering production system life-cycle. All these steps can be significantly supported and improved by digital twins, which are digitalized replicas of physical systems that are synchronized with the real systems at runtime. However, the design and implementation of digital twins for such integrated, yet partly stand-alone, industrial sub-systems can represent challenging and significantly time-consuming engineering tasks. In this article, the problem of the digital twin design for discrete-event production systems is addressed. The article also proposes to utilize a formal description of production resources and related production operations that the resources can perform. An executable version of such formalization can be automatically derived into a form of a digital twin. Such a derived digital twin can be enhanced with operation duration times that are obtained with process mining methods, leading to more realistic simulations for the entire production system. The proposed solution was successfully tested and validated in the Industry 4.0 Testbed, equipped with four robots and a transport system, which is utilized as a use-case in this article.

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“…An application of PDDL for industrial problems including their formalization is addressed in [43]. It provides a collection of simple prototypical cases.…”

Section: Production Planning For Industrial Production Systems Enable...mentioning

confidence: 99%

Digitalized Automation Engineering of Industry 4.0 Production Systems and Their Tight Cooperation with Digital Twins

Novák

Vyskočil

2022

Processes

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“…Re‐planning has been traditionally been applied to restricted problems such as path or motion planning [24, 25]. Initial work on integrating planning in automated manufacturing environments is provided in [26].…”

Section: Related Workmentioning

confidence: 99%

RoboPlanner: a pragmatic task planning framework for autonomous robots

Kattepur

Purushotaman

2020

Cognitive Computation and Systems

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Robotic automation has proliferated various industrial deployments including manufacturing, retail warehousing and logistics supply chains. In order for robots to advance to the next stage of cognitive autonomy, a robust framework for planning, execution and adaptation is needed. While there have been advances in abstract automated planning systems, they are still illsuited to be applied within runtime robotic executions, which take place in uncertain environments. In this study, the authors provide a deliberative robotic planning and simulated execution framework called RoboPlanner that provides a pragmatic integration of automated planning, orchestration and adaptive deployments. This is coupled with an execution monitor and plan repair module, that allows reconfiguration to various template actions with runtime changes. Structured rules for re-planning in the case of state changes, unforeseen obstacles or execution failures are provided. They demonstrate their simulation framework on a realistic example of mobile pick & delivery robots in Industry 4.0 warehouses, that plan, execute, adapt and replan in sync with a knowledge base.

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Section: B Automated Planningmentioning

confidence: 99%

“…A study on usage of PDDL for a collection of typical basic industrial problems is presented in [3]. Compared to [3], the approach proposed in this letter (i) utilizes PDDL as an intermediate format rather than a tool for direct modeling by experts, (ii) we use a real system of industrial scale, and (iii) we are focused on classical (i.e., non-temporary) planners due to the efficiency. While a "bridge between automation and AI planning" is described in [3], we are enhancing this concept by incorporating a standardized specification and its translation into automation and AI planning.…”

Section: B Automated Planningmentioning

confidence: 99%

“…We have implemented the workflow previously described based on metamodels of ISA-95 and PDDL that have been formalized using Ecore/EMF. 3 However, our approach could have been realized using any capable technology, including, e.g., ontologies. The transformation of the initial production system information into a planning domain model has been realized threefold, as described in the following two sub-sections for generic information and in Section IV-B for domain-specific concepts.…”

Section: B Implementationmentioning

confidence: 99%

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Flexible Production Systems: Automated Generation of Operations Plans Based on ISA-95 and PDDL

Wally

Vyskočil

Novák

et al. 2019

IEEE Robot. Autom. Lett.

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Model-driven engineering (MDE) provides tools and methods for the manipulation of formal models. In this letter, we leverage MDE for the transformation of production system models into flat files that are understood by general purpose planning tools and that enable the computation of "plans", i.e., sequences of production steps that are required to reach certain production goals. These plans are then merged back into the production system model, thus enriching the formalized production system knowledge.Index Terms-AI-based methods, factory automation, intelligent and flexible manufacturing.

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Improved Domain Modeling for Realistic Automated Planning and Scheduling in Discrete Manufacturing

Cited by 16 publications

References 10 publications

Digitalized Automation Engineering of Industry 4.0 Production Systems and Their Tight Cooperation with Digital Twins

Digitalized Automation Engineering of Industry 4.0 Production Systems and Their Tight Cooperation with Digital Twins

RoboPlanner: a pragmatic task planning framework for autonomous robots

Flexible Production Systems: Automated Generation of Operations Plans Based on ISA-95 and PDDL

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