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Wolf-Pack-Inspired Distributed Encirclement for UAV-USV Systems via Visual Connectivity Preservation
Jinheng Xiao1, Shihan Kong2, Jinan Sun3
1School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China.
Biomimetics (Basel, Switzerland)
|July 27, 2026
Summary
This study introduces a novel wolf-pack inspired framework for coordinating unmanned aerial vehicle-unmanned surface vehicle teams. The growth-based visual connectivity encirclement (GB-VCE) method enhances cooperative encirclement with adaptive roles and efficient communication.
Area of Science:
- Robotics and Autonomous Systems
- Distributed Artificial Intelligence
- Multi-Agent Coordination
Background:
- Cooperative encirclement by heterogeneous teams (UAV-USV) faces challenges due to limited communication and sensing.
- Existing methods often rely on centralized control or fixed roles, limiting adaptability.
Purpose of the Study:
- To develop a distributed, biomimetic framework for heterogeneous UAV-USV teams to achieve robust encirclement.
- To enable adaptive role assignment and efficient information propagation under communication constraints.
Main Methods:
- Proposed the growth-based visual connectivity encirclement (GB-VCE) method, separating role evolution from motion execution.
- Agents dynamically adopt roles (leader, relay, searcher, encircler) based on local evidence.
- Treated visual links and relay relations as coordination resources, not sensing constraints.
Main Results:
- GB-VCE demonstrated improved encirclement accuracy and target-information coverage in simulations.
- Achieved better convergence consistency and heterogeneous role complementarity compared to baselines.
- Validated the effectiveness of biomimetic role growth and visual connectivity preservation.
Conclusions:
- The GB-VCE framework offers an interpretable and robust coordination principle for air-sea cooperative encirclement.
- Adaptive role assignment and resource-based coordination are key for heterogeneous multi-agent systems.
- Biomimicry provides effective strategies for complex autonomous team behaviors.
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