In this paper, we present the service robot MARLIN and its integration with
the K4R platform, a cloud system for complex AI applications in retail. At its
core, this platform contains so-called semantic digital twins, a semantically
annotated representation of the retail store. MARLIN continuously exchanges
data with the K4R platform, improving the robot's capabilities in perception,
autonomous navigation, and task planning. We exploit these capabilities in a
retail intralogistics scenario, specifically by assisting store employees in
stocking shelves. We demonstrate that MARLIN is able to update the digital
representation of the retail store by detecting and classifying obstacles,
autonomously planning and executing replenishment missions, adapting to
unforeseen changes in the environment, and interacting with store employees.
Experiments are conducted in simulation, in a laboratory environment, and in a
real store. We also describe and evaluate a novel algorithm for autonomous
navigation of articulated tractor-trailer systems. The algorithm outperforms
the manufacturer's proprietary navigation approach and improves MARLIN's
navigation capabilities in confined spaces