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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Vision

Background:

  • Indoor environments often feature ramps and sloped areas, enabling mobile robot navigation.
  • Multi-level indoor areas pose navigation challenges due to sensor reference changes.
  • Cooperative multi-robot systems enhance exploration speed and localization redundancy.

Purpose of the Study:

  • To propose a multi-robot localization strategy for fast and robust exploration of multi-level indoor environments.
  • To enable mobile robots to navigate complex indoor spaces with varying elevations.
  • To improve the reliability of robot navigation through cooperative sensing and mapping.

Main Methods:

  • A leader-follower robot system was implemented for cooperative exploration.
  • The leader robot used 3D LIDAR for particle localization and a camera for multi-level area detection via convolutional neural networks.
  • The follower robot used 2D LIDAR and an iterative closest point algorithm for mapping and re-localization.

Main Results:

  • The leader robot successfully detected multi-level areas and generated navigation paths.
  • The follower robot created 2D maps of accessible areas, serving as a backup localization resource.
  • The proposed system demonstrated robust environment exploration in challenging multi-level settings.

Conclusions:

  • The leader-follower robot system provides an effective solution for navigating and mapping multi-level indoor environments.
  • Cooperative robot strategies enhance localization accuracy and exploration efficiency.
  • The integration of vision-based detection and LIDAR-based mapping ensures robust navigation.