In this paper, we develop an analytic methodology to determine the best technology to carry the communication between an Unmanned Aerial Vehicle (UAV) and a ground control station (GCS). We assume herein that the UAV is performing its mission under nominal conditions. For this, we identify some relevant criteria that cover most use-cases. We propose a multi-criteria analysis to determine the best technology to carry the radiocommunication between the UAV and the GCS. In this work, we distinguish between the Control and Non-Payload Communication Channel (CNPC) and the Payload Channel. By studying two different missions, we emphasize that the technology assessment results depend on the use-case as well as the UAV scenario, and that for a same scenario the results for CNPC are different from the Payload communication. In this work, we are focused on the precise agriculture (PA) use-case, and the public safety (PS) use case. We present the assessment results in both Visual Line of Sight (VLOS), and Beyond Line of Sight (BVLOS) scenarios. The latter is very interesting because the communication UAV-GCS becomes of critical importance. I. INTRODUCTION AND RELATED WORKS Owing to their compact size, their reduced weight and increasing capabilities, Unmanned Aerial Vehicles (UAV) are nowadays used in a wide range of civil applications. Three typical categories of missions are identified [1]: UAV-aided ubiquitous coverage (e.g. in case of infrastructure damage and crowded areas), UAV-aided relaying (e.g. between frontline and command center for emergency responses), and UAV-aided information dissemination and data collection (e.g. for precision agriculture). To accomplish its mission, the UAV exchanges information with a Ground Control Station (GCS), through two communication channels: The control and non-payload communication (CNPC) channel and the payload channel. Both of them characterize the Air-to-Ground (AG) communication.

No part of this work may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, microfilming, recording or otherwise, without written permission from the Publisher, with the exception of any material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work.Cover design: VTeX UAB, Lithuania

International audienceThis book provides an introduction to the emerging field of planning and decision making for aerial robots. An aerial robot is the ultimate form of Unmanned Aerial Vehicle, an aircraft endowed with built-in intelligence, requiring no direct human control and able to perform a specific task. It must be able to fly within a partially structured environment, to react and adapt to changing environmental conditions and to accommodate for the uncertainty that exists in the physical world. An aerial robot can be termed as a physical agent that exists and flies in the real 3D world, can sense its environment and act on it to achieve specific goals. So throughout this book, an aerial robot will also be termed as an agent. Fundamental problems in aerial robotics include the tasks of spatial motion, spatial sensing and spatial reasoning. Reasoning in complex environments represents a difficult problem. The issues specific to spatial reasoning are planning and decision making. Planning deals with the trajectory algorithmic development based on the available information, while decision making determines priorities and evaluates potential environmental uncertainties. The issues specific to planning and decision making for aerial robots in their environment are examined in this book and categorized as follows: motion planning, deterministic decision making, decision making under uncertainty, and finally multi-robot planning. A variety of techniques are presented in this book, and a number of relevant case studies are examined. The topics considered in this book are multidisciplinary in nature and lie at the intersection of Robotics, Control Theory, Operational Research and Artificial Intelligence

No abstract

No abstract

No abstract

No abstract

No abstract