An expert knowledge representation based on real world experiences in proof-of-concept robot system design

Abstract

The development of proof-of-concept (PoC) robotic systems requires a high level of domain-oriented and expert knowledge in an interdisciplinary field to provide individual solutions. The direct interdisciplinary use of existing solutions for such systems can be limited due to domain, environment and task differences. Therefore, it may be necessary to build an appropriate solution from scratch. In addition, the solution parts require knowledge of the system and precise prior knowledge of the robot’s task. In practice, applications that can be used as automated development tools support the user and help to take an important step towards customer centric design and manufacturing. It can move closer to the reality of outcome-based product definition, while reducing development costs and increasing product variety. The key challenge of this thesis was to identify existing robot design approach and provide a widely applicable knowledge base that allowed to simplify one streamline robot design. This covers prior knowledge about the subsystem, facts and relationships, as well as information about hardware design, components and dependencies. Knowledge representation based on ontology for robotic system design can be used as a unified and standardized method for knowledge generation, using and sharing. Concepts defined in the ontology and the extraction of valuable additional information from explicit knowledge using reasoning would counteract this phenomenon. Considering the life cycles of robotic systems, the ontology would be a suitable way to collect and represent the required knowledge about all the phases that this system goes through. This thesis focuses on the idea of solving the challenge of developing PoC system solutions for terrestrial and extraterrestrial robotics as an ontology-based knowledge representation of prior knowledge from different domains.This empirical approach is based on the experience gained in different projects and is written as a cumulative dissertation.