A Cooperative Control Method for Multi-UAV Encirclement of a High-Speed Evader under Perception Constraints

YANG Hui; LI Bin; CHEN Hongbin

doi:10.11999/JEIT260956

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YANG Hui, LI Bin, CHEN Hongbin. A Cooperative Control Method for Multi-UAV Encirclement of a High-Speed Evader under Perception Constraints[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260956

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YANG Hui, LI Bin, CHEN Hongbin. A Cooperative Control Method for Multi-UAV Encirclement of a High-Speed Evader under Perception Constraints[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260956

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A Cooperative Control Method for Multi-UAV Encirclement of a High-Speed Evader under Perception Constraints

doi: 10.11999/JEIT260956 cstr: 32379.14.JEIT260956

YANG Hui^1
,,
LI Bin^{1
,
,},
CHEN Hongbin²

1.
School of Computer Science and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
2.
School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China

Funds: Guangxi Key Research and Development Program (AB25069510)

Received Date: 2026-07-13
Accepted Date: 2026-10-20
Rev Recd Date: 2026-09-07

Available Online: 2026-10-28

Abstract

Abstract

Objective This paper investigates the cooperative pursuit-evasion problem of multiple unmanned aerial vehicles (UAVs) against a highly maneuverable evader in low-altitude urban environments. With the rapid growth of low-altitude UAV applications, unauthorized UAVs pose increasing threats to airspace security. Compared with conventional counter-UAV measures, cooperative pursuit by multiple UAVs offers a more flexible solution; however, when the evader outperforms each pursuer in maneuverability, capture can only be achieved through coordinated decision-making that restricts the evader's escape space. The objective is therefore to develop an efficient multi-UAV cooperative pursuit strategy achieving robust capture under dynamics constraints, obstacle interference, and speed disadvantage. Methods To address this problem, a cooperative pursuit-evasion scenario is constructed in a bounded urban area with static rectangular obstacles, where the motion of all UAVs is described by a damped double-integrator model that captures the inertia and velocity-attenuation characteristics of UAV dynamics. The pursuit problem is then formulated as a Markov decision process in which each UAV acts on local observations. The pursuers are trained with the Multi-Agent Soft Actor-Critic (MASAC) algorithm under the centralized training and decentralized execution framework, while the evader is trained simultaneously with Soft Actor-Critic (SAC), forming an adversarial learning process. To improve exploration efficiency, a progressive adversarial imitation learning framework is introduced: expert demonstrations are generated offline via Apollonius-circle analysis under speed disadvantage, Generative Adversarial Imitation Learning provides intrinsic imitation rewards in early training, and an exponential decay mechanism gradually shifts the pursuer policy from imitation-driven to autonomous reinforcement learning. Furthermore, a velocity-ratio-based curriculum progressively increases the evader's maximum speed, enhancing policy adaptability against highly maneuverable targets. Results and Discussions Simulation experiments are conducted on a Python-based multi-UAV pursuit-evasion platform, where the urban airspace contains static rectangular buildings, three pursuer UAVs (maximum speed 3 m/s), and one evader UAV (3.9 m/s). Results show that the proposed framework outperforms baseline reinforcement learning methods and converges to a stable reward level after approximately 2000 episodes. Ablation studies demonstrate that Apollonius-circle based expert demonstrations significantly accelerate early-stage learning, while velocity-ratio curriculum learning enhances final pursuit performance; their combination yields a synergistic improvement in both convergence speed and policy quality. Experiments under speed ratios of 1:1, 1:1.3, and 1:1.6 further verify robustness: the capture success rate approaches 100% without a speed disadvantage and remains high as the evader's speed advantage increases. Conclusions This paper proposes a progressive adversarial imitation learning framework for multi-UAV cooperative pursuit of a highly maneuverable evader. By integrating MASAC-based cooperative decision-making, adversarial evader training, Apollonius-circle expert demonstrations with GAIL imitation rewards, and velocity-ratio curriculum learning, the framework significantly improves learning efficiency and capture performance. Simulation results verify faster convergence, higher capture success rates, and stronger adaptability across different evader speeds, providing an effective learning-based solution for low-altitude cooperative interception of highly maneuverable targets.
- UAV communication networks,
- Pursuit-evasion game,
- Deep reinforcement learning,
- Resource allocation

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References(15)

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