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Un algoritmo mejorado de planificación de rutas Astar híbrido utilizando búsqueda guiada y restricciones de corredor

Na Che1,2,3, Xianwei Zeng1, Jian Zhao1,2,3

  • 1College of Computer Science and Technology, Changchun University, Changchun 130022, China.

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PubMed
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Este estudio presenta GCHybridA*, un algoritmo mejorado de planificación de rutas para la conducción autónoma. Mejora la seguridad y la eficiencia mediante el uso de diagramas de Voronoi y corredores seguros, reduciendo significativamente el espacio de búsqueda y garantizando rutas libres de colisiones.

Palabras clave:
vehículos autónomos híbridos A*búsqueda guiadaplanificación de rutascorredor seguro

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Área de la Ciencia:

  • Robótica y Sistemas Autónomos
  • Inteligencia Artificial
  • Geometría Computacional

Sus antecedentes:

  • Los algoritmos HybridA* tradicionales tienen dificultades con los grandes espacios de búsqueda, la ineficiencia computacional y las preocupaciones de seguridad en entornos complejos.
  • La planificación de rutas es fundamental para los sistemas de conducción autónoma, ya que requiere estrategias de navegación eficientes y seguras.

Objetivo del estudio:

  • Proponer un algoritmo HybridA* mejorado (GCHybridA*) que aborde las limitaciones de los métodos tradicionales.
  • Mejorar la eficiencia y la seguridad de la planificación de rutas globales en entornos estáticos para vehículos autónomos.

Principales métodos:

  • Construcción de una ruta de Voronoi alejada de los obstáculos y su suavizado para reducir la expansión ineficaz de nodos.
  • Generación de un corredor seguro para restringir la búsqueda de rutas, garantizando la seguridad de la ruta.
  • Implementación de un mecanismo de tamaño de paso adaptable para equilibrar la eficiencia de la búsqueda y la calidad de la ruta.

Principales resultados:

  • El algoritmo GCHybridA* logró una reducción promedio del 83,7% en las expansiones de nodos en comparación con el algoritmo HybridA* convencional.
  • Se mantuvieron cero puntos potenciales de colisión en todos los mapas probados, lo que demuestra una mayor seguridad.
  • Se observaron mejoras significativas en la eficiencia computacional y la calidad de la ruta.

Conclusiones:

  • GCHybridA* ofrece una solución innovadora y robusta para la planificación de rutas eficiente y segura en la conducción autónoma.
  • El algoritmo proporciona garantías de seguridad para la navegación en entornos estáticos.
  • Este enfoque supera eficazmente las limitaciones de los métodos de planificación de rutas tradicionales.