CoDrone: Autonomous Drone Navigation Assisted by Edge and Cloud Foundation Models
Pengyu Chen, Tao Ouyang, Ke Luo, Weijie Hong, Xu Chen

TL;DR
CoDrone is a novel cloud-edge-end framework that enhances autonomous drone navigation by integrating foundation models, deep reinforcement learning, and efficient scene understanding to improve performance in resource-limited environments.
Contribution
It introduces a comprehensive collaborative framework combining foundation models, a neural scheduler, and UAV-specific interaction modules for improved autonomous navigation.
Findings
40% increase in average flight distance
5% improvement in navigation quality
Effective operation under varying network conditions
Abstract
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…
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Taxonomy
TopicsUAV Applications and Optimization · Robotics and Sensor-Based Localization · Advanced Neural Network Applications
