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Related Experiment Video

Updated: Sep 2, 2025

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

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

Published on: October 14, 2017

Combined improved A* and greedy algorithm for path planning of multi-objective mobile robot.

Dan Xiang^1,2, Hanxi Lin¹, Jian Ouyang³

¹School of Automation, Guangdong Polytechnic Normal University, Guangzhou, 510665, Guangdong, China.

Scientific Reports

|August 2, 2022

Summary

This study introduces an improved A* algorithm for Autonomous Mobile Robot (AMR) path planning. The enhanced algorithm reduces path length by approximately 5% and creates smoother routes in warehouse environments.

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Area of Science:

Robotics
Artificial Intelligence
Computer Science

Background:

Path planning for Autonomous Mobile Robots (AMRs) is a critical area in AI research.
Existing algorithms face challenges in efficiency and optimality for multi-objective scenarios.

Purpose of the Study:

To develop an improved path planning strategy for AMRs using a hybrid A* and greedy algorithm.
To enhance the efficiency and performance of path planning in complex environments.

Main Methods:

An improved A* algorithm with a modified evaluation function for faster convergence.
Removal of unnecessary nodes and retention of essential inflection points for optimized path generation.
Integration of the improved A* algorithm with a greedy algorithm for multi-objective path planning.

Main Results:

The proposed algorithm generates smoother paths compared to standard methods.
Path lengths were reduced by approximately 5% in a warehouse environment simulation.
The algorithm effectively planned paths for multiple target nodes.

Conclusions:

The hybrid A* and greedy algorithm offers a more efficient and effective solution for AMR path planning.
The method demonstrates significant improvements in path smoothness and length reduction.
This approach is suitable for multi-objective path planning in practical applications like warehouse logistics.