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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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Published on: October 14, 2017

Research on obstacle avoidance algorithm for unmanned ground vehicle based on multi-sensor information fusion.

Jiliang Lv^1,2, Chenxi Qu³, Shaofeng Du²

¹School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China.

Mathematical Biosciences and Engineering : MBE

|March 24, 2021

Summary

This study presents a novel fuzzy neural network obstacle avoidance algorithm for unmanned ground vehicles (UGVs) operating in complex environments. The developed algorithm demonstrates superior performance and reliability compared to traditional fuzzy controllers, ensuring safer navigation.

Keywords:

fuzzy neural network multi-sensor information fusion obstacle avoidance robotics control unmanned ground vehicle

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Area of Science:

Robotics
Artificial Intelligence
Control Systems

Background:

Unmanned ground vehicles (UGVs) are increasingly deployed in complex environments, necessitating robust obstacle avoidance systems.
Existing obstacle avoidance methods often struggle with the dynamic and unpredictable nature of these environments.

Purpose of the Study:

To design and validate an advanced obstacle avoidance algorithm for UGVs.
To enhance UGV navigation safety and efficiency in challenging terrains.

Main Methods:

Development of a sensor detection system and kinematic estimation model for UGVs.
Design of a fuzzy neural network (FNN) based obstacle avoidance algorithm integrating multi-sensor data.
Simulation using MATLAB to compare FNN with a traditional fuzzy controller.
Experimental validation on a UGV platform.

Main Results:

The FNN algorithm significantly improved obstacle avoidance performance compared to the fuzzy controller in simulations.
MATLAB simulations showed a superior navigation path under FNN control.
Experimental results confirmed the algorithm's reliability and effectiveness in real-world scenarios.

Conclusions:

The proposed fuzzy neural network obstacle avoidance algorithm offers a reliable and superior solution for UGVs in complex environments.
Multi-sensor information fusion within the FNN framework enhances obstacle detection and avoidance capabilities.
The study validates the practical applicability of the advanced algorithm for autonomous navigation systems.