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Autonomous robotic exploration with simultaneous environment and traversability models learning.

Miloš Prágr1, Jan Bayer1, Jan Faigl1

  • 1Computational Robotics Laboratory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czechia.

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Summary
This summary is machine-generated.

This study introduces a method for autonomous mobile robots to explore unknown terrains by learning traversability models. The approach enables robots to select optimal locomotion gaits for different surfaces, enhancing exploration efficiency.

Keywords:
active learninglocomotion gaitmobile robot explorationmulti-legged robottraversability

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Autonomous Systems

Background:

  • Autonomous mobile robots require robust methods for exploring unknown environments.
  • Effective terrain traversability assessment and spatial modeling are crucial for robot navigation.
  • Multi-legged robots can utilize different locomotion gaits for varied terrains.

Purpose of the Study:

  • To develop a method for generalized autonomous mobile robot exploration in unknown environments.
  • To enable robots to learn and utilize multiple traversability models linked to specific locomotion gaits.
  • To improve robot navigation by integrating online learning of spatial and traversability models.

Main Methods:

  • A decoupled approach for simultaneous learning of environment and traversability models.
  • Navigation waypoint generation based on current spatial and traversability models.
  • Utilizing the generalized traveling salesman problem for waypoint selection with a longer planning horizon.

Main Results:

  • Successful verification in simulated scenarios and experimental deployments with a hexapod robot.
  • Demonstrated exploitation of online-learned traversability models.
  • Effective selection of appropriate locomotion gaits for different terrain types.

Conclusions:

  • The proposed method enhances autonomous robot exploration by learning terrain characteristics.
  • Robots can dynamically adapt their locomotion gaits based on learned traversability information.
  • This approach improves navigation safety and efficiency in complex, unknown environments.