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Enhanced UAV Pursuit-Evasion Using Boids Modelling: A Synergistic Integration of Bird Swarm Intelligence and DRL

Weiqiang Jin1,#, Xingwu Tian1,#, Bohang Shi1, Biao Zhao1,*, Haibin Duan2, Hao Wu3

1 School of Information and Communications Engineering, Faculty of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710049, China
2 China Academy of Electronics and Information Technology, China Electronics Technology Group Corporation (CETC), Beijing, 100041, China
3 Department of Automatic Control, School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China

* Corresponding Author: Biao Zhao. Email: email
# Both authors, Weiqiang Jin and Xingwu Tian are co-first authors

Computers, Materials & Continua 2024, 80(3), 3523-3553. https://doi.org/10.32604/cmc.2024.055125

Abstract

The UAV pursuit-evasion problem focuses on the efficient tracking and capture of evading targets using unmanned aerial vehicles (UAVs), which is pivotal in public safety applications, particularly in scenarios involving intrusion monitoring and interception. To address the challenges of data acquisition, real-world deployment, and the limited intelligence of existing algorithms in UAV pursuit-evasion tasks, we propose an innovative swarm intelligence-based UAV pursuit-evasion control framework, namely “Boids Model-based DRL Approach for Pursuit and Escape” (Boids-PE), which synergizes the strengths of swarm intelligence from bio-inspired algorithms and deep reinforcement learning (DRL). The Boids model, which simulates collective behavior through three fundamental rules, separation, alignment, and cohesion, is adopted in our work. By integrating Boids model with the Apollonian Circles algorithm, significant improvements are achieved in capturing UAVs against simple evasion strategies. To further enhance decision-making precision, we incorporate a DRL algorithm to facilitate more accurate strategic planning. We also leverage self-play training to continuously optimize the performance of pursuit UAVs. During experimental evaluation, we meticulously designed both one-on-one and multi-to-one pursuit-evasion scenarios, customizing the state space, action space, and reward function models for each scenario. Extensive simulations, supported by the PyBullet physics engine, validate the effectiveness of our proposed method. The overall results demonstrate that Boids-PE significantly enhance the efficiency and reliability of UAV pursuit-evasion tasks, providing a practical and robust solution for the real-world application of UAV pursuit-evasion missions.

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Cite This Article

APA Style
Jin, W., Tian, X., Shi, B., Zhao, B., Duan, H. et al. (2024). Enhanced UAV pursuit-evasion using boids modelling: A synergistic integration of bird swarm intelligence and DRL. Computers, Materials & Continua, 80(3), 3523-3553. https://doi.org/10.32604/cmc.2024.055125
Vancouver Style
Jin W, Tian X, Shi B, Zhao B, Duan H, Wu H. Enhanced UAV pursuit-evasion using boids modelling: A synergistic integration of bird swarm intelligence and DRL. Comput Mater Contin. 2024;80(3):3523-3553 https://doi.org/10.32604/cmc.2024.055125
IEEE Style
W. Jin, X. Tian, B. Shi, B. Zhao, H. Duan, and H. Wu "Enhanced UAV Pursuit-Evasion Using Boids Modelling: A Synergistic Integration of Bird Swarm Intelligence and DRL," Comput. Mater. Contin., vol. 80, no. 3, pp. 3523-3553. 2024. https://doi.org/10.32604/cmc.2024.055125



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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