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Guidance and Flocking Algorithm for a Distributed FW‐UAV Swarm System Under Coordinated Surveillance Missions in an Occluded Environment.

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Title: Guidance and Flocking Algorithm for a Distributed FW‐UAV Swarm System Under Coordinated Surveillance Missions in an Occluded Environment.
Authors: Baig, Muhammad Imran1 (AUTHOR) bl2303502@nuaa.edu.cn, Ziyang, Zhen1 (AUTHOR) zhenziyang@nuaa.edu.cn, Javaid, Umair2 (AUTHOR) u.javaid@nbut.edu.cn
Source: Journal of Field Robotics. Jun2026, Vol. 43 Issue 4, p2769-2784. 16p.
Subjects: Obstacle avoidance (Robotics), Group formation, Airplanes, Military surveillance, Adaptive control systems, Swarming (Zoology)
Abstract: In this study, an adaptive guidance and flock control (AGFC) algorithm is proposed for real‐time navigation of a distributed fixed‐wing unmanned aerial vehicle (FW‐UAV) swarm system for coordinated surveillance missions (CSM) in an occluded environment. Under the concept of distributed guidance, a vector field‐based intelligent swarm guidance (VF‐ISG) model is designed, integrating attractive features for surveillance and repulsive features for obstacle avoidance upon detection. The repulsive VF feature enables intelligent path selection, dynamically adjusting trajectories based on the distance and angular relationships between the surveillance path and obstacle positions. In addition, we introduce gain functions to ensure a smooth transition between attractive and repulsive features, modulating the contribution of each VF component in real‐time. Geometric factors during flight, such as the relative distances and angles between the FW‐UAV, targets, and detected obstacles, parameterize these gain functions, enabling smooth and collision‐free navigation of each FW‐UAV. Later, leveraging the proposed VF‐ISG model, a flock control algorithm with active obstacle avoidance (FC‐AOA) and collision avoidance capabilities among swarm FW‐UAVs is formulated for flock formation in complex environments. The adaptive parameters are introduced in the proposed FC‐AOA algorithm, enabling flexible flock formation to maintain a safe distance from obstacles with minimal deviation from the CSM path. Mission‐specific normalized weights combine the VF‐ISG and FC‐AOA schemes, maintaining stable performance. We design a CSM scenario in numerical simulation that complements navigation over unstructured terrain with occluded paths. A comparative numerical simulation with a hybrid flocking baseline demonstrates that the proposed AGFC algorithm significantly improves path feasibility, reduces collision risk, and maintains formation during occluded path traversal. Finally, we perform a hardware‐in‐loop experiment to validate the effectiveness and applicability of the proposed AGFC algorithm in actual CSMs. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Field Robotics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
An: 194135380
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  Label: Title
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  Data: Guidance and Flocking Algorithm for a Distributed FW‐UAV Swarm System Under Coordinated Surveillance Missions in an Occluded Environment.
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  Data: <searchLink fieldCode="AR" term="%22Baig%2C+Muhammad+Imran%22">Baig, Muhammad Imran</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> bl2303502@nuaa.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Ziyang%2C+Zhen%22">Ziyang, Zhen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zhenziyang@nuaa.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Javaid%2C+Umair%22">Javaid, Umair</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> u.javaid@nbut.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Field+Robotics%22">Journal of Field Robotics</searchLink>. Jun2026, Vol. 43 Issue 4, p2769-2784. 16p.
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  Data: <searchLink fieldCode="DE" term="%22Obstacle+avoidance+%28Robotics%29%22">Obstacle avoidance (Robotics)</searchLink><br /><searchLink fieldCode="DE" term="%22Group+formation%22">Group formation</searchLink><br /><searchLink fieldCode="DE" term="%22Airplanes%22">Airplanes</searchLink><br /><searchLink fieldCode="DE" term="%22Military+surveillance%22">Military surveillance</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+control+systems%22">Adaptive control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Swarming+%28Zoology%29%22">Swarming (Zoology)</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: In this study, an adaptive guidance and flock control (AGFC) algorithm is proposed for real‐time navigation of a distributed fixed‐wing unmanned aerial vehicle (FW‐UAV) swarm system for coordinated surveillance missions (CSM) in an occluded environment. Under the concept of distributed guidance, a vector field‐based intelligent swarm guidance (VF‐ISG) model is designed, integrating attractive features for surveillance and repulsive features for obstacle avoidance upon detection. The repulsive VF feature enables intelligent path selection, dynamically adjusting trajectories based on the distance and angular relationships between the surveillance path and obstacle positions. In addition, we introduce gain functions to ensure a smooth transition between attractive and repulsive features, modulating the contribution of each VF component in real‐time. Geometric factors during flight, such as the relative distances and angles between the FW‐UAV, targets, and detected obstacles, parameterize these gain functions, enabling smooth and collision‐free navigation of each FW‐UAV. Later, leveraging the proposed VF‐ISG model, a flock control algorithm with active obstacle avoidance (FC‐AOA) and collision avoidance capabilities among swarm FW‐UAVs is formulated for flock formation in complex environments. The adaptive parameters are introduced in the proposed FC‐AOA algorithm, enabling flexible flock formation to maintain a safe distance from obstacles with minimal deviation from the CSM path. Mission‐specific normalized weights combine the VF‐ISG and FC‐AOA schemes, maintaining stable performance. We design a CSM scenario in numerical simulation that complements navigation over unstructured terrain with occluded paths. A comparative numerical simulation with a hybrid flocking baseline demonstrates that the proposed AGFC algorithm significantly improves path feasibility, reduces collision risk, and maintains formation during occluded path traversal. Finally, we perform a hardware‐in‐loop experiment to validate the effectiveness and applicability of the proposed AGFC algorithm in actual CSMs. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Field Robotics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1002/rob.70165
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 16
        StartPage: 2769
    Subjects:
      – SubjectFull: Obstacle avoidance (Robotics)
        Type: general
      – SubjectFull: Group formation
        Type: general
      – SubjectFull: Airplanes
        Type: general
      – SubjectFull: Military surveillance
        Type: general
      – SubjectFull: Adaptive control systems
        Type: general
      – SubjectFull: Swarming (Zoology)
        Type: general
    Titles:
      – TitleFull: Guidance and Flocking Algorithm for a Distributed FW‐UAV Swarm System Under Coordinated Surveillance Missions in an Occluded Environment.
        Type: main
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      – PersonEntity:
          Name:
            NameFull: Baig, Muhammad Imran
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          Name:
            NameFull: Ziyang, Zhen
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            NameFull: Javaid, Umair
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          Dates:
            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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              Value: 43
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              Value: 4
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            – TitleFull: Journal of Field Robotics
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