Flexible Robotic Assembly Based on Ontological Representation of Tasks, Skills, and Resources

Abstract

Technology has sufficiently matured to enable, in principle, flexible and autonomous robotic assembly systems. However, in practice, it requires making all the relevant (implicit) knowledge that system engineers and workers have – about products to be assembled, tasks to be performed, as well as robots and their skills – available to the system explicitly. Only then can the planning and execution components of a robotic assembly pipeline communicate with each other in the same language and solve tasks autonomously without human intervention. This is why we have developed the Factory of the Future (FoF) ontology. At its core, this ontology models the tasks that are necessary to assemble a product and the robotic skills that can be employed to complete said tasks. The FoF ontology is based on existing standards. We started with theoretical considerations and iteratively adapted it based on practical experience gained from incorporating more and more components required for automated planning and assembly. Furthermore, we propose tools to extend the ontology for specific scenarios with knowledge about parts, robots, tools, and skills from various sources. The resulting scenario ontology serves us as world model for the robotic systems and other components of the assembly process. A central runtime interface to this world model provides fast and easy access to the knowledge during execution. In this work, we also show the integration of a graphical user front-end, an assembly planner, a workspace reconfigurator, and more components of the assembly pipeline that all communicate with the help of the FoF ontology. Overall, our integration of the FoF ontology with the other components of a robotic assembly pipeline shows that using an ontology is a practical method to establish a common language and understanding between the involved components.

FAIR for digital twins

“…Beyond mere search, machine-actionable descriptions also allow to further automate tasks heavily relying on expert knowledge. An example is matching complex requirements with an existing supply: In the project Factory of the Future 17 , we described the capabilities of individual robots to make allocations for a sequence of tasks to be performed [30]. In another, still ongoing project, we want to suggest suitable ways of manufacturing for requested parts.…”

Capability-based Frameworks for Industrial Robot Skills: a Survey

“…However, such mapping would require an universal information representation among all employed skills [44]. To lay a foundation for that, skills and their primitives could be defined in standards such as AutomationML or other standards, such as the Factory of the Future ontology [87]. Within this aspect, it is worth noting that industrial applications initially preferred Automation ML (AML) as information representation and this is also visible in Fig.…”

Capability-based Frameworks for Industrial Robot Skills: a Survey

“…On one hand, from the manufacturing domain where the concept of Plug-and-Work based on PPR [8] describes capabilities at the shop floor. On the other hand, from the robotics domain, where ontologies have been defined to represent robots' capabilities necessary to solve complex steps like the assembly of a chainsaw as described in the FoF ontology [9]. This resulted in the architecture shown in Fig.…”

“…However, such mapping would require an universal information representation among all employed skills [51]. To lay a foundation for that, skills and their primitives could be defined in industrial standards such as AML like in [5] or definitions like the FoF ontology [9]. Within this aspect, it is worth noting that industrial applications initially preferred AML as information representation and this is also visible in Fig.…”