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This study introduces a topology-based navigation system for underwater robots exploring flooded mines. The system effectively guides robots through unknown 3D tunnels using a graph-based map, even with uncertainties.

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Area of Science:

  • Robotics
  • Geoscience
  • Autonomous Systems

Background:

  • Flooded underground mines present complex, unknown 3D environments for exploration.
  • Spherical underwater vehicles, like the UX-series robots, are suited for such challenging terrains.
  • Accurate navigation is crucial for gathering geoscientific data in these environments.

Purpose of the Study:

  • To design, implement, and simulate a topology-based navigation system for UX-series robots.
  • To enable autonomous navigation in semi-structured, unknown 3D tunnel networks.
  • To address uncertainties and errors in topological maps generated by perception and SLAM modules.

Main Methods:

  • A topological map represented as a labeled graph is assumed as input.
  • A novel distance metric is defined for node-matching operations.
  • The metric is utilized for robot localization and path planning within the topological map.

Main Results:

  • Extensive simulations demonstrate the system's effectiveness in navigating complex topologies.
  • The navigation system successfully addresses map uncertainties and reconstruction errors.
  • Performance was validated across various randomly generated tunnel network topologies and noise levels.

Conclusions:

  • The proposed topology-based navigation system is effective for autonomous underwater exploration in flooded mines.
  • The defined distance metric robustly handles map inaccuracies, enabling reliable robot localization.
  • This approach facilitates efficient geoscientific data collection in challenging subterranean environments.