多无人机系统安全感知协同决策优化算法OA

This paper addresses the dual challenge of security and robustness in collaborative decision-making for multi-UAV systems operating in dynamic and adversarial environments,where traditional approaches that decouple safety mechanisms from control policies often fail under anomalies.To this end,we propose adaptive security control with adversarial-resilient endogenous strategy(ASC-ARES),a novel framework grounded in"security by design"and"security left shift"principles that systematically embeds multi-layer constraints,including biconnected topology control,physical collision avoidance,and energy management,into deep reinforcement learning via structured state modeling and reward shaping.Methodologically,ASC-ARES extends the deep deterministic policy gradient(DDPG)algorithm to handle hybrid action spaces through a dual-head policy network for joint optimization of three-dimensional continuous attitude and discrete yaw actions.It further integrates a centroid-guided biconnectivity control algorithm to enable proactive network connectivity awareness and constructs a mean opinion score(MOS)-driven multi-objective adaptive reward mechanism to synergistically optimize quality of experience(QoE),network resilience,safety,and energy efficiency.Experimental results demonstrate that ASC-ARES achieves superior convergence and stability,maintaining an MOS fluctuation rate of only 0.36%and a biconnectivity success rate of 99.98%.Under fast gradient sign method(FGSM),projected gradient descent(PGD),and strong noise interference(ϵ=2.0),the system exhibits exceptional topology reconstruction and state recovery capabilities,with an average performance restoration rate exceeding 80%after interference removal.Ablation studies confirm that the topology control module improves service quality by 59%,while the repulsion mechanism reduces collision risk by 85%.These findings establish ASC-ARES as an effective paradigm for achieving integrated performance-security co-optimization in resource-constrained multi-agent systems.