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Related Experiment Video

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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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Efficient path planning for autonomous vehicles based on RRT* with variable probability strategy and artificial

Fazhan Tao^1,2, Zhaowei Ding^1,3, Zhigao Fu⁴

¹School of Information Engineering, Henan University of Science and Technology, Luoyang, 471000, China.

Scientific Reports

|October 21, 2024

Summary

This study introduces an Improved A-RRT* algorithm for autonomous driving path planning. It enhances convergence speed and path quality by combining goal-bias sampling with an improved artificial potential field method.

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Area of Science:

Robotics
Artificial Intelligence
Computer Science

Background:

Autonomous driving technology relies heavily on path planning for enhanced safety.
The Rapidly-exploring Random Tree star (RRT*) algorithm is widely used but faces challenges like slow convergence and unpredictable search patterns.
Addressing these limitations is crucial for advancing autonomous navigation.

Purpose of the Study:

To propose an enhanced RRT* algorithm, termed Improved A-RRT*, for more efficient and reliable path planning.
To improve the convergence speed and path quality of the RRT* algorithm in autonomous driving applications.

Main Methods:

Introduced a variable probability goal-bias strategy to direct the random tree expansion towards the target.
Improved the artificial potential field (APF) method to avoid local optima during path generation.
Integrated the enhanced APF with RRT*, using target gravitational fields and obstacle repulsive forces to guide tree growth.

Main Results:

The Improved A-RRT* algorithm demonstrated significant optimizations in convergence speed compared to standard RRT*.
Experimental results showed superior path quality generated by the proposed algorithm.
The enhanced algorithm effectively guided the random tree towards the target region while avoiding obstacles.

Conclusions:

The Improved A-RRT* algorithm effectively addresses the limitations of the standard RRT* algorithm.
This enhanced approach offers a promising solution for safe and efficient path planning in autonomous driving.
The combination of goal-bias sampling and improved APF leads to better performance in complex environments.