AdaDrone: Quality of Navigation Based Neural Adaptive Scheduling for Edge-Assisted Drones

Accurate navigation is of paramount importance to ensure flight safety and efficiency for autonomous drones. Recent research starts to use Deep Neural Networks (DNN) 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 AdaDrone, an edge computing 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 (DRL) based neural scheduler for which an information encoder is devised to reshape the state features and thus improve its learning ability. We finally implement a prototype of our framework wherein a drone board for navigation and scheduling control interacts with edge servers for task offloading and a simulator for performance evaluation. Extensive experimental results show that AdaDrone can reduce end-to-end latency by 28.06% and extend the flight distance by up to 27.28% compared with non-adaptive solutions.