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Data Fusion Applied to the Leader-Based Bat Algorithm to Improve the Localization of Mobile Robots.

Wolmar Araujo-Neto1, Leonardo Rocha Olivi2, Daniel Khede Dourado Villa1

  • 1Department of Electrical Engineering, Universidade Federal do Espírito Santo, Vitória 29075-910, ES, Brazil.

Sensors (Basel, Switzerland)
|January 25, 2025
PubMed
Summary
This summary is machine-generated.

The leader-based bat algorithm (LBBA) enhances mobile robot path planning by using robot orientation for better localization. This bio-inspired approach offers greater accuracy and faster convergence with fewer particles, outperforming other algorithms.

Keywords:
autonomous mobile robotsdata fusionoptimizationrobot localization

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Autonomous mobile robots require efficient path-planning in complex environments.
  • Bio-inspired algorithms offer solutions but face computational challenges with increasing particle numbers.
  • Existing methods struggle with local minima and computational overhead.

Purpose of the Study:

  • To introduce and evaluate the leader-based bat algorithm (LBBA) for enhanced mobile robot localization.
  • To improve path accuracy and reduce computational cost in autonomous navigation.
  • To address limitations of traditional bat algorithms (BA) in resource-constrained settings.

Main Methods:

  • The leader-based bat algorithm (LBBA) was developed, enhancing the traditional bat algorithm (BA).
  • Robot orientation, obtained via a digital compass, was dynamically integrated into swarm distribution.
  • Experiments involved a mobile robot navigating obstacle-filled environments, comparing LBBA with MRFO and BWO.

Main Results:

  • LBBA demonstrated superior path accuracy and faster convergence compared to MRFO and BWO.
  • The algorithm achieved these results using fewer particles, reducing computational overhead.
  • LBBA showed consistent robustness in avoiding local minima, a common issue in bio-inspired methods.

Conclusions:

  • LBBA is a promising solution for real-time localization in resource-constrained mobile robotics.
  • The algorithm enhances guidance accuracy and efficiency for autonomous mobile robots.
  • LBBA's performance suggests potential for broader adoption in mobile robot applications.