Sense And Avoid (SAA) & Traffic Alert and Collision Avoidance System (TCAS) Integration for Unmanned Aerial Systems (UAS)

Author(s):  
Eric Portilla ◽  
Alex Fung ◽  
Won-Zon Chen ◽  
Omid Shakernia ◽  
Tom Molnar
Author(s):  
Subodh Bhandari ◽  
Thirupathi Srinivasan ◽  
Justin Gray ◽  
Mark Torstenbo ◽  
Jorge Corral ◽  
...  

Author(s):  
Brendan Duffy ◽  
Swee Balachandran ◽  
Andrew Peters ◽  
Kyle Smalling ◽  
Maria Consiglio ◽  
...  

Drones ◽  
2020 ◽  
Vol 4 (1) ◽  
pp. 8 ◽  
Author(s):  
Alberto Sigala ◽  
Brent Langhals

Over recent decades, the world has experienced a growing demand for and reliance upon unmanned aerial systems (UAS) to perform a broad spectrum of applications to include military operations such as surveillance/reconnaissance and strike/attack. As UAS technology matures and capabilities expand, especially with respect to increased autonomy, acquisition professionals and operational decision makers must determine how best to incorporate advanced capabilities into existing and emerging mission areas. This research seeks to predict which autonomous UAS capabilities are most likely to emerge over the next 20 years as well as the key challenges for implementation for each capability. Employing the Delphi method and relying on subject matter experts from operations, acquisitions and academia, future autonomous UAS mission areas and the corresponding level of autonomy are forecasted. The study finds consensus for a broad range of increased UAS capabilities with ever increasing levels of autonomy, but found the most promising areas for research and development to include intelligence, surveillance, and reconnaissance (ISR) mission areas and sense and avoid and data link technologies.


2019 ◽  
Vol 11 (18) ◽  
pp. 2144 ◽  
Author(s):  
Paula Fraga-Lamas ◽  
Lucía Ramos ◽  
Víctor Mondéjar-Guerra ◽  
Tiago M. Fernández-Caramés

Advances in Unmanned Aerial Vehicles (UAVs), also known as drones, offer unprecedented opportunities to boost a wide array of large-scale Internet of Things (IoT) applications. Nevertheless, UAV platforms still face important limitations mainly related to autonomy and weight that impact their remote sensing capabilities when capturing and processing the data required for developing autonomous and robust real-time obstacle detection and avoidance systems. In this regard, Deep Learning (DL) techniques have arisen as a promising alternative for improving real-time obstacle detection and collision avoidance for highly autonomous UAVs. This article reviews the most recent developments on DL Unmanned Aerial Systems (UASs) and provides a detailed explanation on the main DL techniques. Moreover, the latest DL-UAV communication architectures are studied and their most common hardware is analyzed. Furthermore, this article enumerates the most relevant open challenges for current DL-UAV solutions, thus allowing future researchers to define a roadmap for devising the new generation affordable autonomous DL-UAV IoT solutions.


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