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

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Published on: October 14, 2017

Path planning for mobile robots in complex environments based on improved ant colony algorithm.

Yuzhuo Shi¹, Huijie Zhang², Zhisheng Li²

¹College of Information Technology, Tianjin College of Commerce, Tianjin 300350, China.

Mathematical Biosciences and Engineering : MBE

|November 3, 2023

Summary

This summary is machine-generated.

This study introduces an improved ant colony (CBIACO) algorithm for mobile robot path planning. It enhances convergence speed, global path quality, and obstacle avoidance in complex, dynamic, and unknown environments.

Keywords:

ant colony algorithm dynamic obstacle avoidance path exploration path planning unknown environment

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Area of Science:

Robotics
Artificial Intelligence
Computer Science

Background:

Basic ant colony algorithms struggle with convergence time, path quality, and adaptability to dynamic/unknown environments.
Mobile robot path planning requires efficient and robust algorithms for complex scenarios.

Purpose of the Study:

To propose an improved ant colony (CBIACO) algorithm for mobile robot path planning in complex environments.
To enhance convergence speed, global path quality, and obstacle avoidance capabilities.

Main Methods:

Developed a novel probability transfer function with adaptive weight adjustment for faster convergence.
Introduced an unknown environment path exploration strategy (UPES) for better exploration.
Implemented a collision classification model and dynamic obstacle avoidance strategy.

Main Results:

The CBIACO algorithm rapidly generates high-quality global paths in known environments.
Mobile robots successfully reach targets quickly and safely in unknown environments.
The dynamic obstacle avoidance strategy effectively reduces costs when navigating around obstacles.

Conclusions:

The CBIACO algorithm offers significant improvements over basic ant colony algorithms for mobile robot path planning.
The proposed method is effective in both static and dynamic complex environments, including unknown terrains.
This research contributes to safer and more efficient mobile robot navigation.