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Related Experiment Video

Updated: Aug 5, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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Can Genetic Algorithms Be Used for Real-Time Obstacle Avoidance for LiDAR-Equipped Mobile Robots?

Zoltán Gyenes1,2, Ladislau Bölöni1, Emese Gincsainé Szádeczky-Kardoss2

  • 1Department of Computer Science, University of Central Florida, 4328 Scorpius St., Orlando, FL 32816, USA.

Sensors (Basel, Switzerland)
|March 30, 2023
PubMed
Summary

This study explores using genetic algorithms (GAs) for real-time obstacle avoidance in mobile robots. The GAVO algorithm demonstrates that GAs can achieve efficient, real-time performance in dynamic environments.

Keywords:
genetic algorithmobstacle avoidancevelocity obstacles

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computational Intelligence

Background:

  • Mobile robot deployment in public spaces is limited by real-time navigation challenges.
  • Robots require smooth trajectory planning to avoid static and dynamic obstacles.

Purpose of the Study:

  • To investigate the feasibility of employing genetic algorithms for real-time obstacle avoidance in mobile robots.
  • To develop and evaluate a novel algorithm combining genetic algorithms with the velocity obstacle model.

Main Methods:

  • A new family of algorithms, GAVO (Genetic Algorithms for Velocity Obstacles), was developed.
  • Genetic algorithms were adapted for online, real-time optimization, moving beyond their traditional offline use.
  • Experiments were conducted to test the performance of GAVO with specific chromosome representations and parameter settings.

Main Results:

  • The GAVO algorithm demonstrated the capability for real-time obstacle avoidance.
  • Carefully selected chromosome representations and parametrization are crucial for achieving real-time performance.
  • The study validates the potential of genetic algorithms in dynamic navigation tasks.

Conclusions:

  • Genetic algorithms can be effectively utilized for real-time obstacle avoidance in mobile robotics.
  • The GAVO approach offers a promising solution for enhancing robot navigation in complex, dynamic environments.
  • Further research into GA parametrization can unlock broader applications in autonomous systems.