Guidance and Flocking Algorithm for a Distributed FW‐UAV Swarm System Under Coordinated Surveillance Missions in an Occluded Environment
Muhammad Imran Baig, Zhen Ziyang, Umair Javaid · Journal of Field Robotics · 2026
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.