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Reaction diffusion Voronoi diagrams: from sensors data to computing.

Alejandro Vázquez-Otero1,2, Jan Faigl3, Raquel Dormido4

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This study introduces a novel reaction-diffusion (RD) system for computational problems, generating stable Voronoi diagrams (VD) from sensor data with enhanced noise resistance for robotics applications.

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FitzHugh–NagumoTuring instabilityVoronoi diagramexplorationlaser range sensornavigationpath planningreaction diffusionsonar sensor

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Area of Science:

  • Computational Science
  • Robotics
  • Artificial Intelligence

Background:

  • Reaction-diffusion (RD) systems offer a biologically inspired approach to computation.
  • Voronoi diagrams (VD) are crucial for spatial analysis and robotics tasks.
  • Existing RD models face challenges with noisy data and solution stability.

Purpose of the Study:

  • To present a new computational method using RD systems tailored for Voronoi diagram generation.
  • To apply this method to solve robotic problems, specifically topological place identification in grid maps.
  • To enhance the robustness and stability of RD-based computations.

Main Methods:

  • A novel RD system configuration is developed to naturally evolve Voronoi diagrams.
  • The framework integrates external information, such as grid maps from sensor measurements, directly into the RD model.
  • Generalized Voronoi diagrams are computed within the RD system's spatiotemporal dynamics.

Main Results:

  • The proposed RD-based method demonstrates significantly reduced sensitivity to noisy sensor data compared to standard VD algorithms.
  • The system overcomes previous limitations of RD models, such as volatile solutions from excitable waves, achieving stable final states.
  • Successful application in robotic tasks, including topological place identification in environment maps.

Conclusions:

  • The novel RD system provides a robust and stable computational framework for generating Voronoi diagrams.
  • This approach offers a promising solution for integrating sensor data in robotics and other computational problems.
  • The method enhances the reliability of VD computation in the presence of environmental noise.