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A Framework for Optimal Navigation in Situations of Localization Uncertainty.

Charifou Orou Mousse1, Mohamed Benrabah1, François Marmoiton1

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Area of Science:

  • Robotics
  • Autonomous Systems
  • Navigation

Background:

  • Autonomous vehicles rely on path following and obstacle avoidance for navigation.
  • Localization uncertainty significantly impacts these functions, often necessitating a complete stop for safety.
  • Existing methods struggle to maintain performance under uncertain localization conditions.

Purpose of the Study:

  • To develop a framework that optimally combines path following and obstacle avoidance.
  • To ensure these two critical functions remain independent despite localization uncertainties.
  • To enable robust navigation for mobile robots even with inaccurate positioning.

Main Methods:

  • Proposed a novel framework for autonomous vehicle navigation.
  • Implemented independent path following and obstacle avoidance modules.
  • Tested the framework on an EZ10 shuttle in the PAVIN experimental platform.

Main Results:

  • The proposed framework successfully combines path following and obstacle avoidance.
  • Localization uncertainty only affected path following, not obstacle avoidance.
  • Obstacle avoidance was performed effectively in the robot's local reference frame, independent of absolute position accuracy.

Conclusions:

  • The developed approach ensures collision-free navigation for mobile robots.
  • It is a promising solution for autonomous systems operating with inaccurate localization.
  • The independence of obstacle avoidance from localization uncertainty enhances navigation safety and reliability.