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Prescribed Performance Control of a Human-Following Surveillance Robot with Incomplete Observation.

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Summary

This study introduces new algorithms for rehabilitation robots to accurately recognize user intentions and maintain a consistent following pose, even with incomplete sensor data. This enhances robotic assistance for individuals with lower limb muscle weakness.

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Area of Science:

  • Robotics
  • Rehabilitation Engineering
  • Human-Robot Interaction

Background:

  • Effective rehabilitation is crucial for individuals with lower limb muscle weakness to regain mobility.
  • Existing human-following rehabilitation robots face challenges with sensory signal loss and maintaining relative pose.
  • Uninterrupted robotic assistance requires robust intention recognition and stable following control.

Purpose of the Study:

  • To develop and validate novel algorithms for human intention recognition and robot following control.
  • To address the challenges of incomplete sensory observations in human-following robotic systems.
  • To enhance the reliability and effectiveness of rehabilitation robots for gait assistance.

Main Methods:

  • Proposed a human walking intention estimation algorithm utilizing set-membership filtering with incomplete observation.
  • Developed a human-following control algorithm based on prescribed performance for stable robot navigation.
  • Integrated these algorithms into a human-following surveillance robot for experimental validation.

Main Results:

  • The intention estimation algorithm demonstrated continuous and accurate recognition even with incomplete motion signals.
  • The control algorithm successfully achieved constrained robot following, maintaining the desired relative pose.
  • Experimental results confirmed the effectiveness of both proposed algorithms in simulated real-world scenarios.

Conclusions:

  • The developed set-membership filtering approach enables robust intention recognition despite signal interruptions.
  • Prescribed performance control ensures stable and effective robot following for rehabilitation applications.
  • These advancements contribute to more reliable and user-centered robotic assistance for mobility recovery.