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A Resilient and Effective Task Scheduling Approach for Industrial Human-Robot Collaboration.

Andrea Pupa1,2, Wietse Van Dijk1, Christiaan Brekelmans1

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This study introduces an online framework for resilient human-robot collaboration (HRC) task scheduling. It effectively manages uncertainties and deviations in industrial settings, promoting efficient parallel work.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Industrial Automation

Background:

  • Effective task scheduling is a major challenge in human-robot collaboration (HRC) due to uncertainties in shared industrial workspaces.
  • Traditional offline scheduling strategies are insufficient for dynamic operational environments.
  • The need for resilient and adaptive task scheduling in collaborative robotics is critical.

Purpose of the Study:

  • To present a novel online framework for resilient and reliable task scheduling in HRC.
  • To address operational uncertainties, human/robot errors, varying operator skills, and actor substitutions.
  • To maintain efficient schedules by promoting parallel human-robot work.

Main Methods:

  • Utilizing AND/OR graphs to represent collaborative jobs and potential deviations.
  • Developing an adaptive architecture that selects optimal collaboration paths.
  • Implementing local AND/OR graph adaptation to manage failures during operation.

Main Results:

  • The proposed framework demonstrates resilience against operational deviations and errors.
  • Efficient parallel human-robot work is promoted, enhancing collaboration.
  • Successful validation in an industrial assembly scenario using a Franka Emika Panda robot.

Conclusions:

  • The novel online framework provides a robust solution for dynamic HRC task scheduling.
  • The approach enhances reliability and efficiency in industrial collaborative robotics.
  • AND/OR graph adaptation is key to completing tasks despite unforeseen failures.