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Collaborative Optimization Framework for AAV Clusters: Enhancing Energy Efficiency, Reliability, and Stability
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AI
DOI：10.1109/JIOT.2026.3672307.png)
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Abstract
En 中文
Existing autonomous aerial vehicle (AAV) clusters lack a formalized model, fail to consider external interference factors, and overlook the need for dynamic cluster maintenance to ensure stability and coordination in open scenarios. To address these issues, we propose an AAV collaborative cluster formation method suitable for open scenarios, which can form an energy-efficient, reliable, and stable AAV cluster even in external interferences. First, we present a AAV node promotion method based on mobility similarity and connectivity. Then, we formalize a collaborative AAV cluster model based on the energy efficiency, reliability, and stability among AAV nodes. Next, we propose a formation method for AAV clusters based on Pareto optimality and provide a maintenance method for clusters. Extensive simulation results demonstrate that the proposed method significantly outperforms state-of-the-art (SOTA) approaches. Specifically, in open scenarios, our method improves average cluster efficiency (ACE) by up to 6.9%, enhances average cluster reliability (ACR) by 12.4%, enhances average cluster stability (ACS) by 14.2%, and extends the average cluster survival time (ANST) and the average node survival time (ANST) by 18.1% and 17.2% compared to the best-performing baseline, verifying the superior effectiveness and robustness of the proposed framework.
Keywords:
Autonomous aerial vehicles (AAVs)
collaborative model
multiobjective optimization
open scenarios
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8.9
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