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Terrain-aware semantic mapping for cooperative subterranean exploration.

Michael J Miles1, Harel Biggie2, Christoffer Heckman2

  • 1Department of Mechanical Engineering, University of Colorado Boulder, Boulder, CO, United States.

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|October 19, 2023
PubMed
Summary

This study presents a semantic grid mapping framework for robots navigating challenging subterranean environments. The system enables efficient, multi-robot exploration and hazard sharing, crucial for search and rescue operations.

Keywords:
DARPA subterranean challengecooperative explorationfield robotssemantic grid mappingsubterranean environmentsterrain mapping

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Geospatial Mapping

Background:

  • Subterranean environments pose significant navigation challenges for field robots due to undefined hazards.
  • Sharing hazard information among multiple robots is complex, especially under bandwidth constraints.
  • Efficient mapping and navigation are critical for semi-autonomous search and rescue operations.

Purpose of the Study:

  • To develop an efficient and modular framework for semantic grid mapping of subterranean environments.
  • To encode occupancy, traversability, and stairway information into a distributed grid map.
  • To enable safe and enduring exploration by robot fleets.

Main Methods:

  • Proposed an efficient, modular framework for semantic grid mapping.
  • Encoded occupancy and traversability data, including stairways, into a distributed grid map.
  • Facilitated map distribution among a robot fleet, overcoming bandwidth limitations.

Main Results:

  • Demonstrated safe and enduring exploration of subterranean environments.
  • Showcased system performance in high-fidelity simulations and physical experiments.
  • Successfully utilized the system in the DARPA Subterranean Challenge, achieving third place.

Conclusions:

  • The developed semantic grid mapping framework enhances robot navigation in complex subterranean terrains.
  • The system effectively supports multi-robot collaboration and information sharing for exploration.
  • This approach is vital for advancing semi-autonomous search and rescue capabilities in challenging environments.