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ORIGINAL RESEARCH article

Front. Robot. AI
Sec. Computational Intelligence in Robotics
Volume 11 - 2024 | doi: 10.3389/frobt.2024.1363150
This article is part of the Research Topic Robotics Software Engineering View all 7 articles

Semantic Composition of Robotic Solver Algorithms on Graph Structures

Provisionally accepted
Sven Schneider Sven Schneider 1,2*Nico Hochgeschwender Nico Hochgeschwender 3*Herman Bruyninckx Herman Bruyninckx 2,4,5*
  • 1 Institute for AI and Autonomous Systems, Department of Computer Science, Hochschule Bonn-Rhein-Sieg (H-BRS), Sankt Augustin, North Rhine-Westphalia, Germany
  • 2 Department of Mechanical Engineering, KU Leuven, Leuven, Belgium
  • 3 Department of Mathematics and Computer Science, University of Bremen, Bremen, Bremen, Germany
  • 4 Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
  • 5 Flanders Make (Belgium), Lommel, Belgium

The final, formatted version of the article will be published soon.

    This article introduces a model-based design, implementation, deployment, and execution methodology, with tools supporting the systematic composition of algorithms, from generic and domain-specific computational building blocks, that prevent code duplication, and enable robots to adapt their software themselves. The envisaged algorithms are numerical solvers based on graph structures. In this article we focus on kinematics and dynamics algorithms, but examples such as message passing on probabilistic networks and factor graphs, or cascade control diagrams fall under the same pattern. The tools rely on mature standards from the Semantic Web. They first synthesize algorithms symbolically, from which they then generate efficient code. The use case is an overactuated mobile robot with two redundant arms.

    Keywords: solvers based on graph traversal, Model-based engineering, Algorithm synthesis, Code generation, composability and compositionality, Kinematics and Dynamics

    Received: 29 Dec 2023; Accepted: 27 Nov 2024.

    Copyright: © 2024 Schneider, Hochgeschwender and Bruyninckx. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Sven Schneider, Institute for AI and Autonomous Systems, Department of Computer Science, Hochschule Bonn-Rhein-Sieg (H-BRS), Sankt Augustin, 53757, North Rhine-Westphalia, Germany
    Nico Hochgeschwender, Department of Mathematics and Computer Science, University of Bremen, Bremen, 28359, Bremen, Germany
    Herman Bruyninckx, Department of Mechanical Engineering, KU Leuven, Leuven, Belgium

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.