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Research on Autonomous Vehicle Path Planning Algorithm Based on Improved RRT* Algorithm and Artificial Potential

Xiang Li1, Gang Li1, Zijian Bian1

  • 1School of Automobile and Traffic Engineering, Liaoning University of Technology, Jinzhou 121001, China.

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|June 27, 2024
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Summary
This summary is machine-generated.

This study introduces a novel fusion algorithm for autonomous vehicle path planning, enhancing the RRT* algorithm and artificial potential field method. The new approach generates smoother, more accurate, and stable paths efficiently, overcoming limitations of existing methods.

Keywords:
RRT* algorithmartificial potential field methodautonomous vehiclecurvaturefusion algorithmpath planning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Autonomous Systems

Background:

  • Traditional RRT* algorithms for autonomous vehicles suffer from randomness, inefficiency, and poor local obstacle avoidance.
  • Artificial Potential Field (APF) methods often lead to local optima, unreachable targets, and are unsuitable for global path planning.
  • Existing path planning methods struggle with unknown obstacles and complex scenarios, necessitating improved algorithms.

Purpose of the Study:

  • To develop a fusion algorithm combining improved RRT* and APF for robust autonomous vehicle path planning.
  • To address limitations of RRT* including randomness, time consumption, and redundant nodes.
  • To overcome APF's drawbacks such as local optimality, unreachable targets, and global scenario inapplicability.

Main Methods:

  • Improved RRT* incorporates artificial potential field concepts, probability sampling optimization, and adaptive step sizes based on road curvature.
  • Path post-processing refines the global path, reducing redundant nodes and enhancing sampling efficiency.
  • Enhanced APF includes obstacle avoidance constraints, road boundary repulsion, optimized potential functions, and virtual gravity points for U-shaped obstacles.

Main Results:

  • The fusion algorithm successfully plans global paths using improved RRT* and navigates local obstacles with the enhanced APF.
  • Path smoothing ensures kinematic constraints are met, resulting in stable and accurate trajectories.
  • Simulations across diverse road scenes demonstrate the algorithm's ability to generate smooth, suitable paths quickly.

Conclusions:

  • The proposed fusion algorithm significantly improves path planning for autonomous vehicles.
  • It effectively overcomes the limitations of individual RRT* and APF methods, offering enhanced safety and efficiency.
  • The method provides a stable, accurate, and adaptable solution for real-world autonomous driving scenarios.