A Disaster Relief UAV Path Planning Based on APF-IRRT* Fusion Algorithm

  1. Diao, Qifeng 1
  2. Zhang, Jinfeng 1
  3. Liu, Min 2
  4. Yang, Jiaxuan 1
  5. González Aguilera, Diego 3
  1. 1 Research Center of Fluid Machinery Engineering and Technology of Jiangsu University, Zhenjiang 212013, China
  2. 2 Institute of Fluid Engineering Equipment, JITRI, Zhenjiang 212009, China
  3. 3 Universidad de Salamanca
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    Universidad de Salamanca

    Salamanca, España

    ROR https://ror.org/02f40zc51

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Revista:
Drones

ISSN: 2504-446X

Año de publicación: 2023

Volumen: 7

Número: 5

Páginas: 323

Tipo: Artículo

DOI: 10.3390/DRONES7050323 GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Drones

Resumen

Unmanned Aerial Vehicle (UAV) path planning has increasingly become the key research point for civilian drones to expand their use and enhance their work efficiency. Focusing on offline derivative algorithms, represented by Rapidly-exploring Random Trees (RRT), are widely utilized due to their high computational efficiency. However, deploying these offline algorithms in complex and changing disaster environments presents its own drawbacks, such as slow convergence speed, poor real-time performance, and uneven generation paths. In this paper, the Artificial Potential Field -Improved Rapidly-exploring Random Trees (APF-IRRT*) path-planning algorithm is proposed, which is applicable to disaster relief UAV cruises. The RRT* algorithm is adapted with adaptive step size and adaptive search range coupled with the APF algorithm for final path-cutting optimization. This algorithm guarantees computational efficiency while giving the target directivity of the extended nodes. Furthermore, this algorithm achieves remarkable progress in solving problems of slow convergence speed and unsmooth path in the UAV path planning and achieves good performance in both offline static and online dynamic environment path planning.

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Información de financiación

Financiadores

  • Key research and development project of Jiangsu Province
    • BE2019009-1

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