Current Status of the Use of Drones in Education in Croatia

Use of drones considerably rises which includes the education, both by the drones and about drones. We analyse the current use of drones in education in Croatia and provide predictions for its future development. We collected corresponding data using available internet sources. The analysis of the collected data indicates a systematic approach to the inclusion of drones in education. There are two directions of growth in the use of drones, the first leads to an increase in the number of drones in a particular institution and the second leads to an increase in the number of institutions that use drones in education. This article presents an insight into the current use of drones as part of education in Croatia

A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones

Fully-autonomous miniaturized robots (e.g., drones), with artificial intelligence (AI) based visual navigation capabilities are extremely challenging drivers of Internet-of-Things edge intelligence capabilities. Visual navigation based on AI approaches, such as deep neural networks (DNNs) are becoming pervasive for standard-size drones, but are considered out of reach for nanodrones with size of a few cm${}^\mathrm{2}$. In this work, we present the first (to the best of our knowledge) demonstration of a navigation engine for autonomous nano-drones capable of closed-loop end-to-end DNN-based visual navigation. To achieve this goal we developed a complete methodology for parallel execution of complex DNNs directly on-bard of resource-constrained milliwatt-scale nodes. Our system is based on GAP8, a novel parallel ultra-low-power computing platform, and a 27 g commercial, open-source CrazyFlie 2.0 nano-quadrotor. As part of our general methodology we discuss the software mapping techniques that enable the state-of-the-art deep convolutional neural network presented in [1] to be fully executed on-board within a strict 6 fps real-time constraint with no compromise in terms of flight results, while all processing is done with only 64 mW on average. Our navigation engine is flexible and can be used to span a wide performance range: at its peak performance corner it achieves 18 fps while still consuming on average just 3.5% of the power envelope of the deployed nano-aircraft.Comment: 15 pages, 13 figures, 5 tables, 2 listings, accepted for publication in the IEEE Internet of Things Journal (IEEE IOTJ

PRIVACY-AWARE AND HARDWARE-BASED ACCLERATION AUTHENTICATION SCHEME FOR INTERNET OF DRONES

Drones are becoming increasingly present into today’s society through many different means such as outdoor sports, surveillance, delivery of goods etc. With such a rapid increase, a means of control and monitoring is needed as the drones become more interconnected and readily available. Thus, the idea of Internet of drones (IoD) is formed, an infrastructure in place to do those types of things. However, without an authentication system in place anyone could gain access or control to real time data to multiple drones within an area. This is a problem that I choose to tackle using a Field Programmable Gate Array (FPGA) that accelerates the k-Nearest Neighbor (kNN) encryption algorithm making it a hardware component. This will allow me to synthesis and implement the three parts of my privacy-aware and hardware-based authentication scheme for internet of drones. I use Vivado and Vivado HLS to obtain results for my authentication scheme. My scheme was able to perform large computational expensive tasks faster than other proposed IoD schemes

DroneTrack: Cloud-Based Real-Time Object Tracking Using Unmanned Aerial Vehicles Over the Internet

Low-cost drones represent an emerging technology that opens the horizon for new smart Internet-of-Things (IoT) applications. Recent research efforts in cloud robotics are pushing for the integration of low-cost robots and drones with the cloud and the IoT. However, the performance of real-time cloud robotics systems remains a fundamental challenge that demands further investigation. In this paper, we present DroneTrack, a real-time object tracking system using a drone that follows a moving object over the Internet. The DroneTrack leverages the use of Dronemap planner (DP), a cloud-based system, for the control, communication, and management of drones over the Internet. The main contributions of this paper consist in: (1) the development and deployment of the DroneTrack, a real-time object tracking application through the DP cloud platform and (2) a comprehensive experimental study of the real-time performance of the tracking application. We note that the tracking does not imply computer vision techniques but it is rather based on the exchange of GPS locations through the cloud. Three scenarios are used for conducting various experiments with real and simulated drones. The experimental study demonstrates the effectiveness of the DroneTrack system, and a tracking accuracy of 3.5 meters in average is achieved with slow-speed moving targets.info:eu-repo/semantics/publishedVersio