CoDrone: Autonomous Drone Navigation Assisted by Edge and Cloud Foundation Models

Autonomous navigation for unmanned aerial vehicles (UAVs) presents significant challenges due to the limited onboard computational resources, which often restrict deployed deep neural networks (DNNs) to shallow architectures incapable of handling complex environments. In addition, offloading tasks to remote edge servers introduces high latency, creating an inherent tradeoff in system design. To address these limitations, we propose CoDrone—the first cloud–edge–end collaborative computing framework that integrates foundation models into autonomous UAV cruising scenarios—effectively leveraging foundation models to enhance the performance of resource-constrained UAV platforms. To reduce both 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, 1-D occupancy grid-based navigation method—enabling fine-grained scene understanding while significantly advancing the efficiency and representational simplicity of autonomous navigation. A key component of CoDrone is a deep reinforcement learning (DRL)-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, which incorporates domain-tailored low-level flight primitives to enable effective interaction between the cloud foundation model, the vision–language model (VLM), and the UAV. The introduction of VLM enhances open-set reasoning capabilities in complex and previously unseen scenarios. We implement a prototype of CoDrone and conduct extensive evaluations in the AirSim simulation environment. Experimental results demonstrate that CoDrone significantly 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.