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An improved artificial potential field with RRT star algorithm for autonomous vehicle path planning.

Tian Guan¹, Yi Han², Michuang Kong¹

¹School of Automobile, Chang'an University, Xi'an, 710064, China.

Scientific Reports

|May 15, 2025

Summary

This study introduces a bidirectional APF-RRT* algorithm to improve path planning. The enhanced algorithm offers faster convergence and smoother paths compared to traditional methods.

Keywords:

BIAP-RRT* algorithm Dynamic target biasing Improved artificial potential field method Path planning

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Area of Science:

Robotics
Artificial Intelligence
Computer Science

Background:

Traditional RRT* algorithms face challenges with sampling randomness, slow convergence, and path smoothness.
Efficient path planning is crucial for autonomous systems.

Purpose of the Study:

To develop an improved path planning algorithm addressing limitations of traditional RRT*.
To enhance convergence speed, reduce sampling randomness, and improve path quality.

Main Methods:

Proposed a bidirectional APF-RRT* (Artificial Potential Field-RRT*) algorithm.
Incorporated dynamic goal bias, improved artificial potential field, and dual-tree growth strategies.
Utilized triangle inequality for path pruning and B-spline curves with linear interpolation for path smoothing.

Main Results:

The BIAP-RRT* algorithm demonstrated significantly faster convergence speeds.
Achieved a reduced number of iterations compared to traditional RRT* and other improved algorithms.
Resulted in demonstrably smoother and higher-quality paths.

Conclusions:

The BIAP-RRT* algorithm effectively overcomes the limitations of traditional RRT*.
Offers superior performance in terms of convergence, efficiency, and path smoothness for robotic path planning.