A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles

In recent years, there has been a dramatic increase in the use of unmanned aerial vehicles (UAVs), particularly for small UAVs, due to their affordable prices, ease of availability, and ease of operability. Existing and future applications of UAVs include remote surveillance and monitoring, relief operations, package delivery, and communication backhaul infrastructure. Additionally, UAVs are envisioned as an important component of 5G wireless technology and beyond. The unique application scenarios for UAVs necessitate accurate air-to-ground (AG) propagation channel models for designing and evaluating UAV communication links for control/non-payload as well as payload data transmissions. These AG propagation models have not been investigated in detail when compared to terrestrial propagation models. In this paper, a comprehensive survey is provided on available AG channel measurement campaigns, large and small scale fading channel models, their limitations, and future research directions for UAV communication scenarios

Methods for autonomous wristband placement with a search-and-rescue aerial manipulator

A new robotic system for Search And Rescue (SAR) operations based on the automatic wristband placement on the victims’ arm, which may provide identification, beaconing and remote sensor readings for continuous health monitoring. This paper focuses on the development of the automatic target localization and the device placement using an unmanned aerial manipulator. The automatic wrist detection and localization system uses an RGB-D camera and a convolutional neural network based on the region faster method (Faster R-CNN). A lightweight parallel delta manipulator with a large workspace has been built, and a new design of a wristband in the form of a passive detachable gripper, is presented, which under contact, automatically attaches to the human, while disengages from the manipulator. A new trajectory planning method has been used to minimize the torques caused by the external forces during contact, which cause attitude perturbations. Experiments have been done to evaluate the machine learning method for detection and location, and for the assessment of the performance of the trajectory planning method. The results show how the VGG-16 neural network provides a detection accuracy of 67.99%. Moreover, simulation experiments have been done to show that the new trajectories minimize the perturbations to the aerial platform.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

aColor: Mechatronics, Machine Learning, and Communications in an Unmanned Surface Vehicle

The aim of this work is to offer an overview of the research questions, solutions, and challenges faced by the project aColor ("Autonomous and Collaborative Offshore Robotics"). This initiative incorporates three different research areas, namely, mechatronics, machine learning, and communications. It is implemented in an autonomous offshore multicomponent robotic system having an Unmanned Surface Vehicle (USV) as its main subsystem. Our results across the three areas of work are systematically outlined in this paper by demonstrating the advantages and capabilities of the proposed system for different Guidance, Navigation, and Control missions, as well as for the high-speed and long-range bidirectional connectivity purposes across all autonomous subsystems. Challenges for the future are also identified by this study, thus offering an outline for the next steps of the aColor project.Comment: Paper was originally submitted to and presented in the 8th Transport Research Arena TRA 2020, April 27-30, 2020, Helsinki, Finlan

Military Innovation in the Third Age of U.S. Unmanned Aviation, 1991–2015

Military innovation studies have largely relied on monocausal accounts—rationalism, institutionalism, or culture—to explain technologically innovative and adaptive outcomes in defense organizations. None of these perspectives alone provided a compelling explanation for the adoption outcomes of unmanned aerial vehicles (UAVs) in the U.S. military from 1991 to 2015. Two questions motivated this research: Why, despite abundant material resources, mature technology, and operational need, are the most-capable UAVs not in the inventory across the services? What accounts for variations and patterns in UAV innovation adoption? The study selected ten UAV program episodes from the Air Force and Navy, categorized as high-, medium-, and low-end cases, for within-case and cross-case analysis. Primary and secondary sources, plus interviews, enabled process tracing across episodes. The results showed a pattern of adoption or rejection based on a logic-of-utility effectiveness and consistent resource availability: a military problem to solve, and a capability gap in threats or tasks and consistent monetary capacity; furthermore, ideational factors strengthened or weakened adoption. In conclusion, the study undermines single-perspective arguments as sole determinants of innovation, reveals that military culture is not monolithic in determining outcomes, and demonstrates that civil-military relationships no longer operate where civilian leaders hold inordinate sway over military institutions.Lieutenant Colonel, United States Air ForceApproved for public release; distribution is unlimited

Unmanned Systems Sentinel / 24 June 2016

Approved for public release; distribution is unlimited