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Intelligent UAV Map Generation and Discrete Path Planning for Search and Rescue Operations

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  • Víctor San Juan
  • Matilde Santos
  • José Manuel Andújar

Abstract

Search and Rescue Operations (SAR) take place in any emergency situation where people are involved and their lives are at risk. These operations are nowadays carried out with the help of advanced technology, such as Unmanned Aerial Vehicles (UAVs). In this work, several methods are proposed to calculate the UAV discrete path planning. Previously, an intelligent characterization of the searching area is performed to estimate a potential risk/occupancy degree of the gridding map. This estimation is mainly based on fuzzy logic, considering different factors. Then, four methods are applied to calculate the path planning: an original proposal called attraction, fuzzy logic, ANFIS, and a PSO algorithm. All of them calculate the location of the waypoints to be followed by the UAVs to minimize the distance and the risk the people is exposed to. Then, these strategies are adapted to the possibility of having more than one UAV searching at the same time, and the swarm formation is discussed. Finally, these four solutions for path planning, including different number of UAVs, are tested in a real simulation scenario, and then the performance of each method is analyzed and compared with the others.

Suggested Citation

  • Víctor San Juan & Matilde Santos & José Manuel Andújar, 2018. "Intelligent UAV Map Generation and Discrete Path Planning for Search and Rescue Operations," Complexity, Hindawi, vol. 2018, pages 1-17, April.
    • Handle: RePEc:hin:complx:6879419
    DOI: 10.1155/2018/6879419
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    References listed on IDEAS

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    1. Zhenglong Wu & Zhenyu Guan & Chengwei Yang & Jie Li, 2017. "Terminal Guidance Law for UAV Based on Receding Horizon Control Strategy," Complexity, Hindawi, vol. 2017, pages 1-19, May.
    2. E. G. Hernandez-Martinez & E. D. Ferreira-Vazquez & G. Fernandez-Anaya & J. J. Flores-Godoy, 2017. "Formation Tracking of Heterogeneous Mobile Agents Using Distance and Area Constraints," Complexity, Hindawi, vol. 2017, pages 1-13, September.
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    Cited by:

    1. Cesar Guevara & Matilde Santos, 2022. "Smart Patrolling Based on Spatial-Temporal Information Using Machine Learning," Mathematics, MDPI, vol. 10(22), pages 1-27, November.
    2. Lufeng Luo & Hanjin Wen & Qinghua Lu & Haojie Huang & Weilin Chen & Xiangjun Zou & Chenglin Wang, 2018. "Collision-Free Path-Planning for Six-DOF Serial Harvesting Robot Based on Energy Optimal and Artificial Potential Field," Complexity, Hindawi, vol. 2018, pages 1-12, November.
    3. Moisés Lodeiro-Santiago & Pino Caballero-Gil & Ricardo Aguasca-Colomo & Cándido Caballero-Gil, 2019. "Secure UAV-Based System to Detect Small Boats Using Neural Networks," Complexity, Hindawi, vol. 2019, pages 1-11, January.
    4. M. R. Mahendrini Fernando Ariyachandra & Gayan Wedawatta, 2023. "Digital Twin Smart Cities for Disaster Risk Management: A Review of Evolving Concepts," Sustainability, MDPI, vol. 15(15), pages 1-25, August.

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