CoDrone：由边缘和云基础模型辅助的自主无人机导航
CoDrone: Autonomous Drone Navigation Assisted by Edge and Cloud Foundation Models
AI总结：
CoDrone通过整合云-边-端协作计算框架和基础模型，提升无人机自主导航性能，实现更高效和精确的环境感知与动态适应。
摘要：
无人飞行器的自主导航面临来自有限机载计算资源的关键挑战，这限制了部署的深度神经网络只能使用无法处理复杂环境的浅层架构。将任务卸载到远程边缘服务器会引入高延迟，这在系统设计中形成了固有的权衡。为了解决这些限制，我们提出了CoDrone——第一个将基础模型整合到自主无人机巡航场景中的云-边-端协作计算框架——有效地利用基础模型来增强资源受限的无人机平台的性能。为了减少机载计算和数据传输的开销，CoDrone采用灰度图像用于导航模型。当需要增强环境感知时，CoDrone利用边缘辅助的基础模型Depth Anything V2进行深度估计，并引入一种新的基于一维占用网格的导航方法——在实现细粒度场景理解的同时，提高了自主导航的效率和表示简洁性。CoDrone的关键组件是一个基于深度强化学习的神经调度器，能够无缝整合深度估计与自主导航决策，使系统能够实时适应动态环境。此外，该框架还引入了一个专为无人机设计的视觉语言交互模块，该模块结合了领域定制化的低级飞行原语，以实现云基础模型与无人机之间的有效交互。引入VLM增强了在复杂未见过的场景中的开集推理能力。实验结果表明，CoDrone在不同飞行速度和网络条件下均优于基线方法，实现了平均飞行距离增加40%和平均导航质量提高5%的成果。
英文摘要：
Autonomous navigation for Unmanned Aerial Vehicles faces key challenges from limited onboard computational resources, which restrict deployed deep neural networks to shallow architectures incapable of handling complex environments. Offloading tasks to remote edge servers introduces high latency, creating an inherent trade-off in system design. To address these limitations, we propose CoDrone - the first cloud-edge-end collaborative computing framework integrating foundation models into autonomous UAV cruising scenarios - effectively leveraging foundation models to enhance performance of resource-constrained unmanned aerial vehicle platforms. To reduce onboard computation and data transmission overhead, CoDrone employs grayscale imagery for the navigation model. When enhanced environmental perception is required, CoDrone leverages the edge-assisted foundation model Depth Anything V2 for depth estimation and introduces a novel one-dimensional occupancy grid-based navigation method - enabling fine-grained scene understanding while advancing efficiency and representational simplicity of autonomous navigation. A key component of CoDrone is a Deep Reinforcement Learning-based neural scheduler that seamlessly integrates depth estimation with autonomous navigation decisions, enabling real-time adaptation to dynamic environments. Furthermore, the framework introduces a UAV-specific vision language interaction module incorporating domain-tailored low-level flight primitives to enable effective interaction between the cloud foundation model and the UAV. The introduction of VLM enhances open-set reasoning capabilities in complex unseen scenarios. Experimental results show CoDrone outperforms baseline methods under varying flight speeds and network conditions, achieving a 40% increase in average flight distance and a 5% improvement in average Quality of Navigation.
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。
