Abstract
The Unmanned Aerial Vehicles (UAVs) delivery service is being increasingly used in logistics. However, it is challenging for a UAV to precisely identify the position for parcel delivering if it is only aided by the GPS, especially in some complex environments with weak signals and high interference. For this issue, we present a knowledge distillation empowered edge intelligence architecture, KeepEdge, to achieve visual information-assisted positioning for the UAV delivery services. Specifically, we integrate deep neural networks (DNN) into an edge computing framework to enable edge intelligence which empowers the UAVs to autonomously identify the expected delivery position. Deploying the DNN model and conducting model inference on UAVs however, requires high computing performance. To manage the trade-off between the limited resources onboard the UAVs and high-performance requirements, we employ knowledge distillation to produce a lightweight model with high accuracy based on the full model trained in the cloud. The lightweight model with significantly lower complexity and less inference latency is used onboard of the UAVs for accurate positioning. Comprehensive experiments show that the proposed architecture achieves satisfactory performance for assisted positioning. A real-world case study is presented to demonstrate the effectiveness of the proposed edge intelligence solution for UAV delivery services.
Highlights
UNMANNED Aerial Vehicles (UAVs), generally known as drones, have become a much-favored vehicle within many fields, such as transportation and surveilpackage, and returns to the dispatch center1
This paper presents a novel assisted positioning solution for the UAV delivery services based on the edge computing architecture
The Klayer convolutional neural network (CNN) in the student network can be treated as a miniature of the teacher network, where the ResNet-152 used in teacher network consists of 152-layers CNN with residual learning
Summary
UNMANNED Aerial Vehicles (UAVs), generally known as drones, have become a much-favored vehicle within many fields, such as transportation and surveilpackage, and returns to the dispatch center. This paper presents a novel assisted positioning solution for the UAV delivery services based on the edge computing architecture. (1) We propose a visual assisted positioning solution for the last mail UAV delivery services, which enables the UAV to automatically identify the expected delivery position in the scenarios where the GPS signal is weak or unstable. This solution acts as a complement element to the GPS-based positioning system. We use knowledge distillation between the cloud and the edge servers to strike a balance between the onboard resource constraints and the model performance requirements This enables the DNN model inference to be conducted on the UAV with satisfactory performance on both accuracy and inference delay.
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