Year: 2025
Type: article
Abstract: To address the challenges of dispersing aerial targets such as bird flocks at civilian airports and drones conducting low-altitude surveillance in critical areas, including ports and convention centers, this paper proposes a hybrid Artificial Potential Field-Particle Swarm Optimization (APF–PSO) algorithm. The proposed solution integrates the real-time collision-avoidance capability of the artificial potential field method with the global network-optimization characteristics of the particle swarm algorithm to maximize protective coverage. Simulation results demonstrate that the hybrid algorithm achieves optimal performance in dispersion of aerial targets based on protective coverage under safety constraints, confirming its superior performance. The key innovations lie in implementing a dynamic repulsion field with exponential gain for emergency maneuvers, introducing a vertical avoidance module to resolve deadlock issues, and establishing a novel decoupled cooperative paradigm for scalable aerial protection networks.
Source: Drones
Authors: Lei Zuo, Ying Wang, Yu Lu, Ruiwen Gu
Institution: Naval University of Engineering
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Award: G1622823741
OpenAlex ID: W4413915387