Knowledge and Skill Representations for Robotized Production

Abstract

Abstract:Model-based systems in control are a means to utilize efficiently human knowledge and achieve high performance. While models consisting of formalized knowledge are used during the engineering step, running systems usually do not contain a high-level, symbolic representation of the control and most of its properties, typically named numerical parameters. On a system level and beyond the plant data, there is also a need to represent the meaning of the data such that deployment and fault analysis could be augmented with partly automated inference based on the semantics of the data. To that end, we extended the formalized knowledge traditionally used in control to include the control purpose, engineering assumption, quality, involved state machines, and so on. We then represented the control semantics in a format that allows an easier extraction of information using querying and reasoning. It aims at making knowledge in control engineering reusable so that it can be shipped together with the control systems. We implemented prototypes that include automatic conversion of plant data from AutomationML into RDF triples, as well as the automated extraction of control properties, the conversion of parameters, and their storage in the same triple store. Although these techniques are standard within the semantic web community, we believe that our robotic prototypes for semantic control represent a novel approach.

Cut & recombine: reuse of robot action components based on simple language instructions

“…Alternative studies do exist that emphasize data collection from robotic experiments in both industry-oriented (Björkelund et al, 2011; Persson et al, 2010) and service robotics domains (Beetz et al, 2016; Ovchinnikova et al, 2015; Riazuelo et al, 2015; Tenorth and Beetz, 2013; Tenorth et al, 2013; Winkler et al, 2014). In the following, we will discuss how the mentioned approaches relate to our study.…”

“…In the following, we will discuss how the mentioned approaches relate to our study. Persson et al (2010) and Björkelund et al (2011) mainly address the question of how to convert code adhering to the emerging industrial standard AutomationML (Drath et al, 2008) into RDF representations allowing reasoning (Miller, 1998) and accumulate data adhering to Semantic Web standards (Shadbolt et al, 2006). These initial efforts are followed by limited reuse attempts (Stenmark and Nugues, 2013; Stenmark et al, 2015), but not in a Semantic Web context.…”

“…These initial efforts are followed by limited reuse attempts (Stenmark and Nugues, 2013; Stenmark et al, 2015), but not in a Semantic Web context. Persson et al (2010) and Björkelund et al (2011) had already found that the desired conversion into RDF structures has obstacles; in industry the code is currently mainly accumulated in AutomationML formats. AutomationML, however, does not target the specificity of defining reusable robotic actions or action fragments, as in our study, but rather targets standardized coding conventions, such that code is easily reusable by another programmer.…”

Distributed multi-robot coordination combining semantics and real-time scheduling

“…In [5], Naumann et al describe an integrated approach for the generation of executable programs for assembly processes using Knowledge Integration Framework (KIF). The goal of KIF is to represent, store, adapt and distribute knowledge across engineering platforms [6].…”

Skill-Based Asset Management: A PLM-Approach for Reconfigurable Production Systems

“…They propose a dual approach for skill definition -top-down definition of production goals and bottom-up description of machine skills, called capabilities. A follow-up research activity is conducted in [24], [26][27] extending the skill concept of [25] focusing on the knowledge representation of the skill-enhanced PPR model (see [28]) via semantic web technologies. The presented approaches mainly concentrate on skill execution in various application fields.…”