发表机构
California State University, Los Angeles（加州州立大学洛杉矶分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对无人机群分布式视觉感知，提出一种感知感知通信中间件，将完整感知样本作为QoS对象，通过图像分片、并发传输、优先级调度和图像质量评估，在异构测试台上验证了低延迟、高吞吐和检测性能保障。
AI 中文摘要
无人机群日益支持依赖分布式视觉感知的安全关键型应用。满足这些应用的低延迟要求可能需要感知模型在具备推理能力的无人机上于群体内执行，从而产生了对高带宽感知数据进行高效无人机间传输的需求。然而，感知的服务质量（QoS）要求不同于传统的分组级QoS；单个分组的成功交付并不能确保完整、及时且可用的图像可供推理使用。我们提出了一种新颖的感知感知通信中间件，该中间件将完整的感知数据样本视为必须满足QoS的通信对象。该中间件扩展了基于轻量级UDP代理的发布-订阅架构，并增加了感知特定服务，包括图像分片与重组、并发分组传输、优先级感知调度和图像质量评估。该中间件在模拟无人机群的异构硬件测试台上使用YOLOv8n目标检测进行了评估。实验结果表明，端到端应用延迟低，吞吐量显著高于轻量级UDP代理，在不断增加的后台负载下能有效优先处理感知流量，并通过中间件级图像质量评估减轻了目标检测性能下降。这项工作为将AI特定数据处理集成到通信中间件中提供了初步框架，以支持多智能体移动信息物理系统中新兴的分布式AI应用。
英文摘要
Unmanned Aerial Vehicle (UAV) swarms increasingly support safety-critical applications that rely on distributed visual perception. Meeting the low-latency requirements of these applications can require perception models to execute within the swarm on inference-capable UAVs, creating a need for efficient UAV-to-UAV transport of high-bandwidth perception data. However, the Quality-of-Service (QoS) requirements of perception differ from conventional packet-level QoS; successful delivery of individual packets does not ensure that a complete, timely, and usable image is available for inference. We present a novel perception-aware communication middleware that treats complete perception-data samples as the communication objects for which QoS must be satisfied. The middleware extends a lightweight UDP broker-based publish-subscribe architecture with perception-specific services, including image fragmentation and reconstruction, concurrent packet transmission, priority-aware scheduling, and image quality assessment. The middleware is evaluated on a heterogeneous hardware testbed emulating a UAV swarm using YOLOv8n object detection. Experimental results demonstrate low end-to-end application latency, substantially higher throughput than a lightweight UDP broker, effective prioritization of perception traffic under increasing background load, and mitigation of object-detection degradation through middleware-level image quality assessment. This work provides an initial framework for integrating AI-specific data handling into communication middleware to support emerging distributed AI applications in multi-agent mobile cyber-physical systems.
