Intelligent control of quad-rotor aircrafts with a STM32 microcontroller using deep neural networks. Issue 5 (1st July 2021)

• Record Type: Journal Article
• Authors: Guan, Xiaochun; Lou, Sheng; Li, Han; Tang, Tinglong
• Is Part Of: Industrial robot. Volume 48:Issue 5(2021)
• Page Start: 700
• Page End: 709
• Publication Date: 2021-07-01
• Subjects: Deployment -- Sensor fusion -- Microcontroller -- On-device machine learning; Robots, Industrial -- Periodicals; Machinery in the workplace -- Periodicals; 629.892
• DOI: 10.1108/IR-10-2020-0239
• Languages: English
• ISSNs: 0143-991X

Abstract

Abstract : Purpose: Deployment 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/approach: In 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. Findings: This 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/value: This method provides an application example and a design reference for the implementation of AI algorithms on unmanned aerial vehicle or terminal robots.