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.

翻译：精准导航对于确保自主无人机的飞行安全与效率至关重要。近期研究开始利用深度神经网络增强无人机导航，因其在视觉感知方面具有卓越的预测能力。然而，现有解决方案要么在无人机本地运行深度神经网络推理任务，受限于机载资源瓶颈；要么将计算卸载至外部服务器，可能引发较大的网络延迟。鲜有研究考虑联合优化卸载决策、图像传输配置及其动态自适应调整。本文提出A3D，一种边缘服务器辅助的无人机导航框架，能够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、高预测精度及长飞行距离。具体而言，我们首先改进了用于无人机导航的先进卷积神经网络，并定义了一个新的度量标准——导航质量作为优化目标，该标准能有效捕获上述目标。随后，我们在无人机端设计了一种基于深度强化学习的神经调度器，并为其开发了信息编码器以重塑状态特征，从而提升学习能力。为进一步支持多无人机并发服务，我们扩展了边缘服务器设计，开发了一种网络感知的资源分配算法，可实现容器化资源的按需供给。最终，我们在真实设备上实现了概念验证原型，并在真实校园场景及基于AirSim的仿真环境中验证其性能。大量实验结果表明，相比非自适应方案，A3D可将端到端延迟降低28.06%，并将飞行距离延长最多27.28%。