Abstract
PurposeDeployment of deep neural networks on embedded devices is becoming increasingly popular because it can reduce latency and energy consumption for data communication. This paper aims to give out a method for deployment the deep neural networks on a quad-rotor aircraft for further expanding its application scope.Design/methodology/approachIn this paper, a design scheme is proposed to implement the flight mission of the quad-rotor aircraft based on multi-sensor fusion. It integrates attitude acquisition module, global positioning system position acquisition module, optical flow sensor, ultrasonic sensor and Bluetooth communication module, etc. A 32-bit microcontroller is adopted as the main controller for the quad-rotor aircraft. To make the quad-rotor aircraft be more intelligent, the study also proposes a method to deploy the pre-trained deep neural networks model on the microcontroller based on the software packages of the RT-Thread internet of things operating system.FindingsThis design provides a simple and efficient design scheme to further integrate artificial intelligence (AI) algorithm for the control system design of quad-rotor aircraft.Originality/valueThis method provides an application example and a design reference for the implementation of AI algorithms on unmanned aerial vehicle or terminal robots.
Highlights
With the new round of development on artificial intelligence (AI), which is typically represented by deep learning, the deep neural networks model has achieved amazing results in pattern recognition and computer vision
on-device machine learning (ODML) means that AI algorithms are executed locally on a hardware device and the data used by the algorithms is generated by the device
This paper provides powerful hardware support for the design of a simple quad-rotor unmanned aerial vehicle (UAV) based on the rich resources of the STMicroelectronics’ 32-bit microcontroller (STM32) microcontroller and the fusion of multiple hardware sensor modules
Summary
With the new round of development on artificial intelligence (AI), which is typically represented by deep learning, the deep neural networks model has achieved amazing results in pattern recognition and computer vision. Model compression and acceleration offer a promising prospect to facilitate deploying deep neural networks on edge devices It plays a great role in promoting ODML. In reference (Zou, 2019; Lu et al, 2016), a cascade proportional integral derivative (PID) control system based on angle and angular velocity is designed It can achieve the stability of aircraft attitude in flight. With the development of AI algorithms, to deploy the deep neural networks model on UAV embedded devices has important application significance. We propose a method to deploy deep neural networks on the micro controller of the quad-rotor aircraft. One contribution of the work is to provide a reference and guidance to some extent for deploying deep neural networks model on UAV so as to further improve the performance and application range of UAV.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
