Human-Machine Differentiation in Speed and Separation Monitoring for Improved Efficiency in Human-Robot Collaboration.

Journal: Sensors (Basel, Switzerland)
Published Date:

Abstract

Human-robot collaborative applications have been receiving increasing attention in industrial applications. The efficiency of the applications is often quite low compared to traditional robotic applications without human interaction. Especially for applications that use speed and separation monitoring, there is potential to increase the efficiency with a cost-effective and easy to implement method. In this paper, we proposed to add human-machine differentiation to the speed and separation monitoring in human-robot collaborative applications. The formula for the protective separation distance was extended with a variable for the kind of object that approaches the robot. Different sensors for differentiation of human and non-human objects are presented. Thermal cameras are used to take measurements in a proof of concept. Through differentiation of human and non-human objects, it is possible to decrease the protective separation distance between the robot and the object and therefore increase the overall efficiency of the collaborative application.

Authors

  • Urban B Himmelsbach
    Work-Life Robotics Laboratory, Department of Business and Industrial Engineering, Offenburg University of Applied Sciences, 77723 Gengenbach, Germany.
  • Thomas M Wendt
    Work-Life Robotics Laboratory, Department of Business and Industrial Engineering, Offenburg University of Applied Sciences, 77723 Gengenbach, Germany.
  • Nikolai Hangst
    Work-Life Robotics Laboratory, Department of Business and Industrial Engineering, Offenburg University of Applied Sciences, 77723 Gengenbach, Germany.
  • Philipp Gawron
    Work-Life Robotics Laboratory, Department of Business and Industrial Engineering, Offenburg University of Applied Sciences, 77723 Gengenbach, Germany.
  • Lukas Stiglmeier
    Work-Life Robotics Laboratory, Department of Business and Industrial Engineering, Offenburg University of Applied Sciences, 77723 Gengenbach, Germany.