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Development of an Improved Rapidly Exploring Random Trees Algorithm for Static Obstacle Avoidance in Autonomous

S M Yang1, Y A Lin1

  • 1Department of Aeronautics and Astronautics, National Cheng Kung University, Tainan City 70101, Taiwan.

Sensors (Basel, Switzerland)
|April 3, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an improved Rapidly Exploring Random Trees (RRT) algorithm for autonomous vehicle path planning. The enhanced method ensures safe obstacle avoidance and efficient navigation, achieving high path tracking accuracy.

Keywords:
Rapidly Exploring Random Treesautonomous vehicle obstacle avoidancepath planning

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Area of Science:

  • Robotics
  • Autonomous Systems
  • Artificial Intelligence

Background:

  • Autonomous vehicles require sophisticated path planning for safe obstacle avoidance.
  • Existing algorithms like Rapidly Exploring Random Trees (RRT) can be computationally intensive and may produce suboptimal paths.

Purpose of the Study:

  • To enhance the RRT algorithm for improved path planning efficiency and safety in autonomous vehicles.
  • To integrate path pruning, smoothing, and optimization with geometric collision detection for robust navigation.

Main Methods:

  • An improved RRT algorithm incorporating path pruning to remove redundant points.
  • Path smoothing to ensure continuous differentiability and vehicle implementable curvature.
  • Geometric collision detection and path optimization for selecting the shortest feasible path.

Main Results:

  • The improved RRT algorithm demonstrated enhanced planning efficiency and safety.
  • Experimental verification showed successful path tracking with an average deviation of 5.2% of vehicle width.
  • Lane change maneuvers were executed with an average deviation within 8.3% of vehicle width.

Conclusions:

  • The developed algorithm effectively plans safe and efficient paths for autonomous vehicles.
  • The integrated approach of pruning, smoothing, and optimization significantly improves navigation capabilities.
  • The system shows practical applicability for both general navigation and lane changes.