Concept for the automated adaption of abstract planning domains for specific application cases in skills-based industrial robotics

Lisa Heuss (), Daniel Gebauer and Gunther Reinhart
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Lisa Heuss: Technical University of Munich
Daniel Gebauer: Technical University of Munich
Gunther Reinhart: Technical University of Munich

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 8, No 32, 4233-4258

Abstract: Abstract High product diversity, dynamic market conditions, and a lack of skilled workers are current challenges in manufacturing. Industrial robots autonomously planning and completing upcoming production tasks can help companies address these challenges. In this publication, we focus on autonomous task planning within industrial robotics and investigate how to facilitate the use of automated planning techniques from the field of artificial intelligence for this purpose. First, we present a novel methodology to automatically adapt abstractly modeled planning domains to the characteristics of individual application cases a user intends to implement. A planning domain is a formalized representation of the robot’s working environment that builds the basis for automated planning. Second, we integrate this approach into the procedure for developing skills-based industrial robotic applications to enable them to perform autonomous task planning. Finally, we demonstrate the use of the methodology within the application field kitting in two reference scenarios with a mobile robot and a stationary robot cell. Using our methodology, persons without expertise in automated planning can enable a robot for autonomous task planning without much extra effort.

Keywords: Industrial robot; Task planning; Artificial intelligence; Automated planning; Planning domain definition language (PDDL); Skills (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10845-023-02211-3

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