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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Human-Robot Interaction

Background:

  • Traditional mobile robot navigation primarily focuses on 2D geometric environment features.
  • Situations requiring robots to coexist with humans necessitate incorporating social distancing and human comfort.
  • Existing methods often lack the dynamic adaptability needed for real-time human interaction.

Purpose of the Study:

  • To develop and present a social navigation approach for mobile robots.
  • To enable robots to navigate environments while maintaining social distancing from people.
  • To enhance human comfort and safety during human-robot interactions in shared spaces.

Main Methods:

  • A multi-layer environmental model integrating geometric and topological data from sensor fusion.
  • Combining the Fast Marching Square algorithm for path planning with Gaussian models for representing people.
  • Utilizing a continuous environmental representation for dynamic path adjustment based on real-time data.

Main Results:

  • Demonstrated practical application on an assistive robot for indoor navigation.
  • Successfully incorporated a specific behavior for navigating narrow passages with people.
  • Efficient detection and modeling of individuals and groups to ensure their comfort.

Conclusions:

  • The proposed social navigation method is effective for real-world assistive robot applications.
  • The fusion of geometric, topological, and human-centric data enables safer and more comfortable robot navigation.
  • This approach significantly advances the capability of mobile robots to operate harmoniously in human-populated environments.