A3D：面向边缘辅助无人机的自适应、精准且自主导航系统
A3D: Adaptive, Accurate, and Autonomous Navigation for Edge-Assisted Drones
AI总结：
针对现有无人机DNN导航方案的资源与延迟矛盾，本文提出边缘辅助的A3D框架，通过深度强化学习调度与网络感知资源分配，实现低延迟、高精度的长距离自适应导航。
摘要：
精准导航对保障自主无人机的飞行安全与效率至关重要。鉴于深度神经网络（DNN）在视觉感知方面出色的预测能力，近期研究开始将其用于增强无人机导航性能。但现有方案要么在无人机本地执行DNN推理任务，受限于机载资源不足；要么将计算卸载至外部服务器，可能产生较高的网络延迟。鲜有研究同时优化卸载决策与图像传输配置，并实现动态自适应调整。本文提出A3D——一种边缘服务器辅助的无人机导航框架，可动态调整任务执行位置、输入分辨率与图像压缩比，以实现低推理延迟、高预测精度与长飞行距离的目标。具体而言，我们首先对用于无人机导航的前沿卷积神经网络进行增强，定义了名为“导航质量”的新型指标作为优化目标，可有效覆盖上述目标。随后在无人机端设计了基于深度强化学习的神经调度器，并设计信息编码器对状态特征进行重塑，以提升其学习能力。为进一步支持多无人机同时服务，我们扩展了边缘服务器设计，提出一种感知网络状态的资源分配算法，可按需为无人机分配容器化资源。最后我们用真实设备搭建了原理验证原型，在真实校园场景中验证了性能，并基于AirSim构建仿真环境开展全面评估。大量实验结果表明，与非自适应方案相比，A3D可将端到端延迟降低28.06%，飞行距离最多提升27.28%。
英文摘要：
Accurate navigation is of paramount importance to ensure flight safety and efficiency for autonomous drones. Recent research starts to use Deep Neural Networks to enhance drone navigation given their remarkable predictive capability for visual perception. However, existing solutions either run DNN inference tasks on drones in situ, impeded by the limited onboard resource, or offload the computation to external servers which may incur large network latency. Few works consider jointly optimizing the offloading decisions along with image transmission configurations and adapting them on the fly. In this paper, we propose A3D, an edge server assisted drone navigation framework that can dynamically adjust task execution location, input resolution, and image compression ratio in order to achieve low inference latency, high prediction accuracy, and long flight distances. Specifically, we first augment state-of-the-art convolutional neural networks for drone navigation and define a novel metric called Quality of Navigation as our optimization objective which can effectively capture the above goals. We then design a deep reinforcement learning based neural scheduler at the drone side for which an information encoder is devised to reshape the state features and thus improve its learning ability. To further support simultaneous multi-drone serving, we extend the edge server design by developing a network-aware resource allocation algorithm, which allows provisioning containerized resources aligned with drones' demand. We finally implement a proof-of-concept prototype with realistic devices and validate its performance in a real-world campus scene, as well as a simulation environment for thorough evaluation upon AirSim. Extensive experimental results show that A3D can reduce end-to-end latency by 28.06% and extend the flight distance by up to 27.28% compared with non-adaptive solutions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
