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This study introduces a new robot navigation system that uses probing to assess terrain collapsibility, ensuring safer travel over unpredictable surfaces like soft soil or water. This advanced traversability analysis enhances robot safety in outdoor environments.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Vision

Background:

  • Safe navigation in unknown outdoor environments is challenging due to unpredictable terrain.
  • Existing traversability analysis methods relying solely on visual and geometric data are insufficient for non-rigid terrains.

Purpose of the Study:

  • To develop a robust traversability analysis framework for safe robot navigation on unknown rough terrain.
  • To integrate terrain collapsibility assessment into robot navigation strategies.

Main Methods:

  • Utilized an RGB-D camera for terrain geometric and semantic property identification.
  • Employed a force sensor for probing questionable terrains and quantifying ground collapsibility.
  • Developed a collapsibility metric to estimate terrain collapse risk.

Main Results:

  • Generated global and local traversability grid maps by combining collapsibility, geometric, and semantic data.
  • Successfully enabled a quadrupedal robot to generate optimal paths for safe navigation.
  • Validated the approach through both simulation and real-world experiments.

Conclusions:

  • The proposed framework provides a comprehensive assessment of unpredictable terrain, crucial for robot safety.
  • Integrating collapsibility information significantly improves traversability analysis for outdoor robot navigation.