IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i10p2356-d1150207.html
   My bibliography  Save this article

Bio-Inspired Multi-UAV Path Planning Heuristics: A Review

Author

Listed:
  • Faten Aljalaud

    (Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia
    Computer Science Department, Imam Mohammad Ibn Saud Islamic University, Riyadh 11564, Saudi Arabia)

  • Heba Kurdi

    (Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia
    Mechanical Engineering Department, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA)

  • Kamal Youcef-Toumi

    (Mechanical Engineering Department, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA)

Abstract

Despite the rapid advances in autonomous guidance and navigation techniques for unmanned aerial vehicle (UAV) systems, there are still many challenges in finding an optimal path planning algorithm that allows outlining a collision-free navigation route from the vehicle’s current position to a goal point. The challenges grow as the number of UAVs involved in the mission increases. Therefore, this work provides a comprehensive systematic review of the literature on the path planning algorithms for multi-UAV systems. In particular, the review focuses on biologically inspired (bio-inspired) algorithms due to their potential in overcoming the challenges associated with multi-UAV path planning problems. It presents a taxonomy for classifying existing algorithms and describes their evolution in the literature. The work offers a structured and accessible presentation of bio-inspired path planning algorithms for researchers in this subject, especially as no previous review exists with a similar scope. This classification is significant as it facilitates studying bio-inspired multi-UAV path planning algorithms under one framework, shows the main design features of the algorithms clearly to assist in a detailed comparison between them, understanding current research trends, and anticipating future directions. Our review showed that bio-inspired algorithms have a high potential to approach the multi-UAV path planning problem and identified challenges and future research directions that could help improve this dynamic research area.

Suggested Citation

  • Faten Aljalaud & Heba Kurdi & Kamal Youcef-Toumi, 2023. "Bio-Inspired Multi-UAV Path Planning Heuristics: A Review," Mathematics, MDPI, vol. 11(10), pages 1-35, May.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:10:p:2356-:d:1150207
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/10/2356/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/11/10/2356/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Boukoberine, Mohamed Nadir & Zhou, Zhibin & Benbouzid, Mohamed, 2019. "A critical review on unmanned aerial vehicles power supply and energy management: Solutions, strategies, and prospects," Applied Energy, Elsevier, vol. 255(C).
    2. Yi Wang & Ensheng Liu, 2021. "Virtual Reality Technology of Multi UAVEarthquake Disaster Path Optimization," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-9, March.
    3. Saul Gonzalez-Bermejo & Guillermo Alonso-Linaje & Parfait Atchade-Adelomou, 2022. "GPS: A New TSP Formulation for Its Generalizations Type QUBO," Mathematics, MDPI, vol. 10(3), pages 1-20, January.
    4. Shen, Lixin & Wang, Yaodong & Liu, Kunpeng & Yang, Zaili & Shi, Xiaowen & Yang, Xu & Jing, Ke, 2020. "Synergistic path planning of multi-UAVs for air pollution detection of ships in ports," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 144(C).
    5. Luan, Jing & Yao, Zhong & Zhao, Futao & Song, Xin, 2019. "A novel method to solve supplier selection problem: Hybrid algorithm of genetic algorithm and ant colony optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 156(C), pages 294-309.
    6. Jianxun Li & Hao Liu & Kin Keung Lai & Bhagwat Ram, 2022. "Vehicle and UAV Collaborative Delivery Path Optimization Model," Mathematics, MDPI, vol. 10(20), pages 1-22, October.
    7. Hai Shen & Yunlong Zhu & Xiaodan Liang, 2014. "Lifecycle-Based Swarm Optimization Method for Numerical Optimization," Discrete Dynamics in Nature and Society, Hindawi, vol. 2014, pages 1-11, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Hong & Nguyen, Hoang & Bui, Xuan-Nam & Pradhan, Biswajeet & Mai, Ngoc-Luan & Vu, Diep-Anh, 2021. "Proposing two novel hybrid intelligence models for forecasting copper price based on extreme learning machine and meta-heuristic algorithms," Resources Policy, Elsevier, vol. 73(C).
    2. Somayeh Toghyani & Seyed Ali Atyabi & Xin Gao, 2021. "Enhancing the Specific Power of a PEM Fuel Cell Powered UAV with a Novel Bean-Shaped Flow Field," Energies, MDPI, vol. 14(9), pages 1-23, April.
    3. Zhang, Chaoyu & Zhang, Chengming & Li, Liyi & Guo, Qingbo, 2021. "Parameter analysis of power system for solar-powered unmanned aerial vehicle," Applied Energy, Elsevier, vol. 295(C).
    4. Chang, Huawei & Cai, Fengyang & Yu, Xianxian & Duan, Chen & Chan, Siew Hwa & Tu, Zhengkai, 2023. "Experimental study on the thermal management of an open-cathode air-cooled proton exchange membrane fuel cell stack with ultra-thin metal bipolar plates," Energy, Elsevier, vol. 263(PA).
    5. Li, Niansi & Liu, Xiaoyong & Yu, Bendong & Li, Liang & Xu, Jianqiang & Tan, Qiong, 2021. "Study on the environmental adaptability of lithium-ion battery powered UAV under extreme temperature conditions," Energy, Elsevier, vol. 219(C).
    6. He, Xinyu & He, Fang & Li, Lishuai & Zhang, Lei & Xiao, Gang, 2022. "A route network planning method for urban air delivery," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).
    7. Collins, Jeffrey M. & McLarty, Dustin, 2020. "All-electric commercial aviation with solid oxide fuel cell-gas turbine-battery hybrids," Applied Energy, Elsevier, vol. 265(C).
    8. Xiaxia Ma & Wenliang Bian & Wenchao Wei & Fei Wei, 2022. "Customer-Centric, Two-Product Split Delivery Vehicle Routing Problem under Consideration of Weighted Customer Waiting Time in Power Industry," Energies, MDPI, vol. 15(10), pages 1-23, May.
    9. Jadoon, Ihtesham & Raja, Muhammad Asif Zahoor & Junaid, Muhammad & Ahmed, Ashfaq & Rehman, Ata ur & Shoaib, Muhammad, 2021. "Design of evolutionary optimized finite difference based numerical computing for dust density model of nonlinear Van-der Pol Mathieu’s oscillatory systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 181(C), pages 444-470.
    10. Zhou, Kehan & Liu, Zhiwei & Zhang, Xin & Liu, Hang & Meng, Nan & Huang, Jianmei & Qi, Mingjing & Song, Xizhen & Yan, Xiaojun, 2022. "A kW-level integrated propulsion system for UAV powered by PEMFC with inclined cathode flow structure design," Applied Energy, Elsevier, vol. 328(C).
    11. Alicia Triviño & José M. González-González & José A. Aguado, 2021. "Wireless Power Transfer Technologies Applied to Electric Vehicles: A Review," Energies, MDPI, vol. 14(6), pages 1-21, March.
    12. Gurunadh Velidi & Chun Sang Yoo, 2023. "A Review on Flame Stabilization Technologies for UAV Engine Micro-Meso Scale Combustors: Progress and Challenges," Energies, MDPI, vol. 16(9), pages 1-44, May.
    13. Sedighizadeh, Davoud & Masehian, Ellips & Sedighizadeh, Mostafa & Akbaripour, Hossein, 2021. "GEPSO: A new generalized particle swarm optimization algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 179(C), pages 194-212.
    14. Jiang, Yi & Lv, Mingyun & Qu, Zhipeng & Zhang, Lanchuan, 2020. "Performance evaluation for scientific balloon station-keeping strategies considering energy management strategy," Renewable Energy, Elsevier, vol. 156(C), pages 290-302.
    15. Ilić, Damir & Milošević, Isidora & Ilić-Kosanović, Tatjana, 2022. "Application of Unmanned Aircraft Systems for smart city transformation: Case study Belgrade," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    16. Tian, Weiyong & Liu, Li & Zhang, Xiaohui & Shao, Jiaqi, 2024. "Flight trajectory and energy management coupled optimization for hybrid electric UAVs with adaptive sequential convex programming method," Applied Energy, Elsevier, vol. 364(C).
    17. Song, Zhuzhu & Tang, Wansheng & Zhao, Ruiqing & Zhang, Guoqing, 2022. "Implications of government subsidies on shipping companies’ shore power usage strategies in port," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 165(C).
    18. Tao Lei & Zhihao Min & Qinxiang Gao & Lina Song & Xingyu Zhang & Xiaobin Zhang, 2022. "The Architecture Optimization and Energy Management Technology of Aircraft Power Systems: A Review and Future Trends," Energies, MDPI, vol. 15(11), pages 1-37, June.
    19. Dinesh Karunanidy & Subramanian Ramalingam & Ankur Dumka & Rajesh Singh & Mamoon Rashid & Anita Gehlot & Sultan S. Alshamrani & Ahmed Saeed AlGhamdi, 2022. "JMA: Nature-Inspired Java Macaque Algorithm for Optimization Problem," Mathematics, MDPI, vol. 10(5), pages 1-28, February.
    20. Yuan Gao & Qian Zhang & Chun Kit Lau & Bhagwat Ram, 2022. "Robust Appointment Scheduling in Healthcare," Mathematics, MDPI, vol. 10(22), pages 1-15, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:10:p:2356-:d:1150207. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.