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Related Experiment Video

Updated: Aug 30, 2025

Utilizing a Reconfigurable Maze System to Enhance the Reproducibility of Spatial Navigation Tests in Rodents

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Published on: December 2, 2022

Improved A* Path Planning Method Based on the Grid Map.

Yangqi Ou¹, Yuexin Fan², Xinglan Zhang²

¹College of Automation, Chongqing University, Chongqing 400044, China.

Sensors (Basel, Switzerland)

|August 26, 2022

Summary

This study enhances the A* (A-Star) algorithm for mobile robot path planning, reducing turning points and improving search speed. The improved algorithm ensures smoother, safer navigation, decreasing path search time by 13%.

Keywords:

Hdl_graph_slam mapping improved A* algorithm mobile robots path searching

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Area of Science:

Robotics
Artificial Intelligence
Computer Science

Background:

Traditional A* algorithm in spatial path planning suffers from excessive turning points and slow search efficiency.
Mobile robot navigation requires efficient and safe path planning to avoid obstacles and minimize operational wear.

Purpose of the Study:

To propose an improved A* algorithm for mobile robot path planning that addresses the limitations of the traditional approach.
To enhance path smoothness, safety, and search efficiency in obstacle-rich environments.

Main Methods:

Utilized lidar and IMU sensors for data acquisition on a mobile robot platform.
Employed the Hdl_graph_slam algorithm for constructing a 2D grid map.
Implemented an improved A* algorithm incorporating path smoothing, safety mechanisms (obstacle expansion), and adaptive cost functions.

Main Results:

The improved A* algorithm significantly reduced path turning points and enhanced path smoothness.
Experimental results demonstrated a 13% reduction in average path search time.
A 11% decrease in the average number of search extension nodes was observed, indicating improved efficiency.

Conclusions:

The proposed enhanced A* algorithm effectively overcomes the limitations of the traditional A* algorithm in mobile robot path planning.
The method provides a more efficient, smoother, and safer navigation solution for mobile robots in complex environments.