Flying Free: A Research Overview of Deep Learning in Drone Navigation Autonomy

With the rise of Deep Learning approaches in computer vision applications, significant strides have been made towards vehicular autonomy. Research activity in autonomous drone navigation has increased rapidly in the past five years, and drones are moving fast towards the ultimate goal of near-complete autonomy. However, while much work in the area focuses on specific tasks in drone navigation, the contribution to the overall goal of autonomy is often not assessed, and a comprehensive overview is needed. In this work, a taxonomy of drone navigation autonomy is established by mapping the definitions of vehicular autonomy levels, as defined by the Society of Automotive Engineers, to specific drone tasks in order to create a clear definition of autonomy when applied to drones. A top–down examination of research work in the area is conducted, focusing on drone navigation tasks, in order to understand the extent of research activity in each area. Autonomy levels are cross-checked against the drone navigation tasks addressed in each work to provide a framework for understanding the trajectory of current research. This work serves as a guide to research in drone autonomy with a particular focus on Deep Learning-based solutions, indicating key works and areas of opportunity for development of this area in the future

Legal Ambiguities Concerning the Use of Unmanned Aerial Vehicles in Marine Scientific Research

How unmanned aerial vehicles challenge the regime of marine scientific research under the United Nations Convention on the Law of the Sea – and how they nevertheless may be accommodate

Airsist -inspections, made simple

Today, bridge Inspections are a complex process. This means they sometimes cannot be properly executed, which poses a danger to society at large. We are , and in the context of the field lab we transform a scientific breakthrough to create value in economic and social terms. The complete work outlines how this business can be created by addressing topics such as the reason for existence, the product, the market & customer, the business model, the roll out, the financials and the outlook. This individual submission presents the topic of defining and developing a product around a new technology, building the right partnerships, budgeting and designing its development and protecting the product through I

The Next Generation of Human-Drone Partnerships: Co-Designing an Emergency Response System

The use of semi-autonomous Unmanned Aerial Vehicles (UAV) to support emergency response scenarios, such as fire surveillance and search and rescue, offers the potential for huge societal benefits. However, designing an effective solution in this complex domain represents a "wicked design" problem, requiring a careful balance between trade-offs associated with drone autonomy versus human control, mission functionality versus safety, and the diverse needs of different stakeholders. This paper focuses on designing for situational awareness (SA) using a scenario-driven, participatory design process. We developed SA cards describing six common design-problems, known as SA demons, and three new demons of importance to our domain. We then used these SA cards to equip domain experts with SA knowledge so that they could more fully engage in the design process. We designed a potentially reusable solution for achieving SA in multi-stakeholder, multi-UAV, emergency response applications.Comment: 10 Pages, 5 Figures, 2 Tables. This article is publishing in CHI202

Public Opinions of Unmanned Aerial Technologies in 2014 to 2019: A Technical and Descriptive Report

The primary purpose of this report is to provide a descriptive and technical summary of the results from similar surveys administered in fall 2014 (n = 576), 2015 (n = 301), 2016 (ns = 1946 and 2089), and 2018 (n = 1050) and summer 2019 (n = 1300). In order to explore a variety of factors that may impact public perceptions of unmanned aerial technologies (UATs), we conducted survey experiments over time. These experiments randomly varied the terminology (drone, aerial robot, unmanned aerial vehicle (UAV), unmanned aerial system (UAS)) used to describe the technology, the purposes of the technology (for economic, environmental, or security goals), the actors (public or private) using the technology, the technology’s autonomy (fully autonomous, partially autonomous, no autonomy), and the framing (promotion or prevention) used to describe the technology’s purpose. Initially, samples were recruited through Amazon’s Mechanical Turk, required to be Americans, and paid a small amount for participation. In 2016 we also examined a nationally representative samples recruited from Qualtrics panels. After 2016 we only used nationally representative samples from Qualtrics. Major findings are reported along with details regarding the research methods and analyses

Integrating AI into UAVs

This research project explores the application of Deep Learning (DL) techniques, specifically Convolutional Neural Networks (CNNs), to develop a smoke detection algorithm for deployment on mobile platforms, such as drones and self-driving vehicles. The project focuses on enhancing the decision-making capabilities of these platforms in emergency response situations. The methodology involves three phases: algorithm development, algorithm implementation, and testing and optimization. The developed CNN model, based on ResNet50 architecture, is trained on a dataset of fire, smoke, and neutral images obtained from the web. The algorithm is implemented on the Jetson Nano platform to provide responsive support for first responders. The study contributes to the intersection of artificial intelligence and autonomous systems, aiming to improve early detection capabilities for critical scenarios

Reference Scenarios and Key Performance Indicators for 5G Ultra-dense Networks

The so-called 5G will revolutionize the way we live, and work. In order to demonstrate the profound changes, we can expect to experience within the next 5 to 10 years, we present use cases for the planned research within the TeamUp5G project. Some use cases are strongly linked to the network layer and aim at developing solutions capable of optimizing the main promising benefits of 5G: extremely low latency and extremely high bandwidth (e.g., handle video streams, traffic congestion, user profiles), in the most efficient way possible. Other use cases focus on commercial applications that make use of middleware applications to enhance their performance. The latter fall into two main areas: real-time virtual reality and live video streaming, which are extremely demanding in terms of latency and bandwidth to provide an acceptable QoE/QoS to multiple users. The use cases presented are built assuming that 5G is essential for their support with appropriate QoE/QoS. Key performance indicators and their range of variation are also identified.info:eu-repo/semantics/acceptedVersio