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.

中文翻译：

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使用深度神经网络的 STM32 微控制器智能控制四旋翼飞机

目的

在嵌入式设备上部署深度神经网络正变得越来越流行，因为它可以减少数据通信的延迟和能耗。本文旨在给出一种在四旋翼飞机上部署深度神经网络的方法，以进一步扩大其应用范围。

设计/方法/方法

本文提出了一种基于多传感器融合实现四旋翼飞行器飞行任务的设计方案。它集成了姿态采集模块、全球定位系统位置采集模块、光流传感器、超声波传感器和蓝牙通信模块等。四旋翼飞行器采用32位微控制器作为主控制器。为了使四旋翼飞行器更加智能，该研究还提出了一种基于RT-Thread物联网操作系统软件包将预先训练好的深度神经网络模型部署在微控制器上的方法。

发现

该设计为进一步集成人工智能（AI）算法提供了一种简单高效的设计方案，用于四旋翼飞行器的控制系统设计。

原创性/价值

该方法为人工智能算法在无人机或终端机器人上的实现提供了应用实例和设计参考。