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Article

A Hierarchical Cooperative Interception Framework for Multi-UAV Defense Against Large-Scale Swarm Intrusions

School of Electronic Engineering, Naval University of Engineering, Wuhan 430033, China
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Author to whom correspondence should be addressed.
Drones 2026, 10(6), 418; https://doi.org/10.3390/drones10060418
Submission received: 24 April 2026 / Revised: 24 May 2026 / Accepted: 27 May 2026 / Published: 28 May 2026

Abstract

To address the challenges of unbalanced task allocation, high inter-UAV collision risks, and lagging interception guidance in multi-UAV cooperative missions within complex urban low-altitude environments, a cooperative interception strategy integrating load-balanced allocation, k-nearest neighbor (k-NN) cooperative obstacle avoidance, and adaptive predictive guidance is proposed. First, a load-balanced Hungarian algorithm is developed at the task allocation layer. The integration of a multi-dimensional distance-angle threat assessment model and a nonlinear load penalty mechanism resolves the issues of resource idling and target overloading inherent in traditional one-to-one allocation, thereby achieving optimal resource configuration for saturated cooperative interception. Second, at the path planning layer, a cooperative obstacle avoidance algorithm based on k-NN nonlinear repulsion is introduced. By exclusively considering the dynamic repulsive fields of local nearest neighbors alongside scale-adaptive parameter regulation, this approach maintains safe formation spacing while reducing the computational complexity from O(n2) to O(k)(kn), significantly enhancing flight robustness in dense airspaces. Finally, at the terminal guidance layer, an adaptive look-ahead guidance model incorporating motion prediction is constructed to mitigate the overshoot and lag defects associated with classical pure pursuit algorithms during the interception of highly maneuverable targets. The implementation of linear extrapolation and dynamic gain regulation facilitates a paradigm shift from “passive pursuit” to “active interception.” Simulation results demonstrate that the proposed algorithm yields substantial improvements in task allocation efficiency, collision risk mitigation, and overall success rates across red-blue UAV swarm confrontation scenarios of varying scales. These findings provide a viable cooperative defense framework against large-scale, highly maneuverable unmanned aerial vehicle (UAV) swarm intrusions.
Keywords: pure pursuit algorithm; task allocation; inter-UAV collision avoidance; UAV interception pure pursuit algorithm; task allocation; inter-UAV collision avoidance; UAV interception

Share and Cite

MDPI and ACS Style

Zuo, L.; Wang, Y.; Liu, J.; Lu, Y.; Gu, R. A Hierarchical Cooperative Interception Framework for Multi-UAV Defense Against Large-Scale Swarm Intrusions. Drones 2026, 10, 418. https://doi.org/10.3390/drones10060418

AMA Style

Zuo L, Wang Y, Liu J, Lu Y, Gu R. A Hierarchical Cooperative Interception Framework for Multi-UAV Defense Against Large-Scale Swarm Intrusions. Drones. 2026; 10(6):418. https://doi.org/10.3390/drones10060418

Chicago/Turabian Style

Zuo, Lei, Ying Wang, Jialu Liu, Yu Lu, and Ruiwen Gu. 2026. "A Hierarchical Cooperative Interception Framework for Multi-UAV Defense Against Large-Scale Swarm Intrusions" Drones 10, no. 6: 418. https://doi.org/10.3390/drones10060418

APA Style

Zuo, L., Wang, Y., Liu, J., Lu, Y., & Gu, R. (2026). A Hierarchical Cooperative Interception Framework for Multi-UAV Defense Against Large-Scale Swarm Intrusions. Drones, 10(6), 418. https://doi.org/10.3390/drones10060418

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