Abstract
In recent years, the proliferation of unmanned aerial vehicles (UAVs) has increased dramatically. UAVs can accomplish complex or dangerous tasks in a reliable and cost-effective way but are still limited by power consumption problems, which pose serious constraints on the flight duration and completion of energy-demanding tasks. The possibility of providing UAVs with advanced decision-making capabilities in an energy-effective way would be extremely beneficial. In this paper, we propose a practical solution to this problem that exploits deep learning on the edge. The developed system integrates an OpenMV microcontroller into a DJI Tello Micro Aerial Vehicle (MAV). The microcontroller hosts a set of machine learning-enabled inference tools that cooperate to control the navigation of the drone and complete a given mission objective. The goal of this approach is to leverage the new opportunistic features of TinyML through OpenMV including offline inference, low latency, energy efficiency, and data security. The approach is successfully validated on a practical application consisting of the onboard detection of people wearing protection masks in a crowded environment.
Highlights
Drones, in the form of both Remotely Piloted Aerial Systems (RPAS) and unmanned aerial vehicles (UAV), are increasingly being used to revolutionize many existing applications
We present the integration of an OpenMV Cam H7 microcontroller unit (MCU), which supports the TensorFlow Lite for Microcontrollers (TFLM), into a DJI Tello drone to enable the development and deployment of TinyML applications on a microcontroller on board the drone
The stability of a drone is controlled by three main sensors; the SoC was measured by the voltage and current sensors via a Tello software development kit (SDK)
Summary
In the form of both Remotely Piloted Aerial Systems (RPAS) and unmanned aerial vehicles (UAV), are increasingly being used to revolutionize many existing applications. As the world becomes more dependent on technology, there is a growing need for autonomous systems that support the activities and mitigate the risks for human operators [1] In this context, UAVs are becoming increasingly popular in a range of civil and military applications such as smart agriculture [2], defense [3], construction site monitoring [4], and environmental monitoring [5]. Powered UAVs, which represent the majority of micro aerial vehicles, show a severe limitation in the duration of batteries, which are necessarily small due to design constraints. This problem affects both the flight duration and the capability of performing fast maneuvers (e.g., to avoid obstacles) due to the slow power response of the battery. Despite their unique capabilities and virtually unlimited opportunities, the practical application of UAVs still suffers from significant restrictions [6]
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