Open Access

ARTICLE

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

by Weiqiang Jin^1,#, Xingwu Tian^1,#, Bohang Shi¹, Biao Zhao^1,*, 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:
# 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

Received 18 June 2024; Accepted 16 August 2024; Issue published 12 September 2024

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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Keywords

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