Multi-rotor Platform Based UAV Systems

By Franck Cazaurang, Kelly Cohen and Manish Kumar

Multi-rotor Platform Based UAV Systems provides an excellent opportunity for experiential learning, capability augmentation and confidence-building for senior level undergraduates, entry-level graduates, engineers working in government agencies, and industry involved in UAV R&D. Topics in this book include an introduction to VTOL multi-copter UAV platforms, UAV system architecture, integration in the national airspace, including UAV classification and associated missions, regulation and safety, certification and air traffic management, integrated mission planning, including autonomous fault tolerant path planning and vision based auto landing systems, flight mechanics and stability, dynamic modeling and flight controller development.

Other topics covered include sense, detect and avoid systems, flight testing, including safety assessment instrumentation and data acquisition telemetry, synchronization data fusion, the geo-location of identified targets, and much more.

• Provides an excellent opportunity for experiential learning, capability augmentation and confidence building for senior level undergraduates, entry-level graduates and engineers working in government, and industry involved in UAV R&D
• Includes MATLAB/SIMULINK computational tools and off-the-shelf hardware implementation tutorials
• Offers a student centered approach
• Provides a quick and efficient means to conceptualize, design, synthesize and analyze using modeling and simulations
• Offers international perspective and appeal for engineering students and professionals

• Language: English
• Publisher: Elsevier Science
• Release date: Feb 28, 2020
• ISBN: 9780081023587

Franck Cazaurang

Dr. Franck Cazaurang graduated from the Ecole Normale Supérieure de Cachan in 1991 and received a Master degree in Automatic Control and Systems from University of Bordeaux 1 in 1992. From October 1992 to November 1997 he was lecturer in Bordeaux 1 and also a Ph.D. student at Bordeaux 1 University, France. He received the Ph.D. degree in December 1997, and the accreditation to supervise research (HdR) in November 2009. Since October 2010, he is full professor of Automatic control at University Bordeaux 1, Bordeaux, France. His main research interest concerns automatic control, autonomous path planning and path tracking based on robust dynamic inversion and their application to aerospace systems. He has 17 refereed journal papers, 1 chapter in peer reviewed professional book, 35 archival proceedings in professional conferences and 2 patents in the aeronautical domain. Dr. Cazaurang has graduated 7 PhD students and 12 MS students. Since 2003, he is the director of the Master of Engineering program dedicated to engineering and maintenance of Aeronautical and transport systems. This university curriculum is proposed for a five-year study period, which includes an undergraduate and graduate degree program. Each year, around 350 students are enrolled in this university educational program. In 2011, he set up a new e-learning continuing education program dedicated to customer support in aeronautical engineering and maintenance. His teaching activity is focused on automatic flight control for aircraft and UAV and the associated maintenance. These courses are offered to graduate students.

Book preview

Multi-Rotor Platform-based UAV Systems

Theory, Computational Examples, Hardware Testing

Franck Cazaurang

Kelly Cohen

Manish Kumar

Series Editor

Jean-Paul Bourrières

Edited by

Table of Contents

Cover image

Title page

Copyright

Introduction

I.1. Background and motivation

I.2. UAV integration, classification, mission planning and system architecture

I.3. Path planning, separation assurance and fault tolerance

I.4. Technological developments: navigation, flight control, situational awareness, additive manufacturing and collaborative swarming

1: Integration in the National Airspace (Europe and USA) – UAV Classification and Associated Missions, Regulation and Safety, Certification and Air Traffic Management

Abstract

1.1 The challenge of UAS integration in the airspace

1.2 Main stakeholders (ICAO, JARUS, EASA and national regulators)

1.3 French regulation

1.4 Communication issues

1.5 Next steps for UTM/U-space

1.6 Obtaining a remote pilot certificate

2: UAV Classification and Associated Mission Planning

Abstract

2.1 UAS classification, general remarks on UAS missions and general market overview

2.2 Operational specificities of rotary-wing UAS

2.3 Examples of civil applications for multi-rotor platform-based UAS

3: UAV System Engineering

Abstract

3.1 Introduction to system engineering principles

3.2 Operational analysis

3.3 Architecture solution design

3.4 Deck-landing navigation constraints of VTOL

3.5 Navigation chain architecture

3.6 Architecture and constraints of the communication system

3.7 Human factors

3.8 Integration–verification–validation

4: Large-Scale UAV Trajectory Planning Using Fluid Dynamics Equations

Abstract

4.1 Unmanned air vehicles (UAV) and challenges in their applications

4.2 Introduction to path planning and fluid analogy

4.3 Problem formulation

4.4 Fluid analogy

4.5 Prediction sets (PS)

4.6 Governing equations for the centralized approach

4.7 Numerical results

4.8 Conclusion

5: Genetic Fuzzy System for Solving the Aircraft Conflict Resolution Problem

Abstract

5.1 Introduction

5.2 Problem description

5.3 Methodology

5.4 Results

5.5 Conclusion and future work

6: Diagnostics and Fault-Tolerant Path Planning

Abstract

6.1 Introduction

6.2 Differential flatness

6.3 Quadrotor model

6.4 Flatness of the model

6.5 Flatness-based fault-tolerant control of a quadrotor UAV

6.6 Conclusion

7: LQR Controller Applied to Quadcopter System Dynamics Identification and Verification Through Frequency Sweeps

Abstract

7.1 Configuration

7.2 Conventions and assumptions

7.3 State-space representation

7.4 Time-history data collection

7.5 Overview of CIFER®

7.6 Open-loop system identification

7.7 System model verification

7.8 LQR controller optimization

8: Autonomous Navigation and Target Geo-Location in GPS Denied Environment

Abstract

8.1 Introduction

8.2 Related works

8.3 System architecture

8.4 Navigation algorithm

8.5 Target geo-location

8.6 Quadrotor dynamics

8.7 Results

Acknowledgment

9: Real-Time Video and FLIR Image Processing for Enhanced Situational Awareness

Abstract

9.1 Introduction

9.2 Literature review

9.3 Methodology

9.4 Results and discussion

9.5 Conclusion and future work

9.6 Acknowledgments

10: Design, Fabrication and Flight Testing of Small UAVs Using Additive Manufacturing

Abstract

10.1 What is 3D printing?

10.2 Preliminary design considerations

10.3 Motivation for additive manufacturing

10.4 Additive manufacturing for design

10.5 Exotic materials

11: Genetic Fuzzy Single and Collaborative Tasking for UAV Operations

Abstract

11.1 Introduction

11.2 Problem formulation

11.3 Methodology

11.4 Results

11.5 Conclusion and future work

List of Authors

Index

Copyright

First published 2020 in Great Britain and the United States by ISTE Press Ltd and Elsevier Ltd

Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:

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Notices

Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary.

Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility.

To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein.

MATLAB® is a trademark of The MathWorks, Inc. and is used with permission. The MathWorks does not warrant the accuracy of the text or exercises in this book. This book’s use or discussion of MATLAB® software or related products does not constitute endorsement or sponsorship by The MathWorks of a particular pedagogical approach or particular use of the MATLAB® software.

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© ISTE Press Ltd 2020

The rights of Franck Cazaurang, Kelly Cohen, Manish Kumar to be identified as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988.

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A CIP record for this book is available from the British Library

Library of Congress Cataloging in Publication Data

A catalog record for this book is available from the Library of Congress

ISBN 978-1-78548-251-9

Printed and bound in the UK and US

Introduction: UAVs and VTOL Multi-Copter UAV Platforms

Franck Cazaurang; Manish Kumar; Kelly Cohen

I.1. Background and motivation

The development of unmanned aerial vehicles (UAVs), usually termed as drones, has made an impressive journey from military applications to the world hobbyists and, more recently, to what seems to be an ever-growing set of meaningful civilian applications. According to a BI Intelligence report Meola (2017), UAV sales are expected to exceed $12 billion in 2021 compared to $8.5 billion in 2016, which translates to an impressive compound annual growth rate (CAGR) of 7.6%. In their economic impact report, the Association for Unmanned Vehicle Systems International (AUVSI) reported that in the first three years of integration more than 70,000 jobs will be created in the United States with an economic impact of more than $13.6 billion. This benefit will grow through 2025 when we foresee more than 100,000 jobs created and economic impact of $82 billion AUVSI (2013).

The PricewaterhouseCoopers (PwC) report PW Press Release (2016) predicts that the market value of civilian UAVs is more than $127 billion. Let us shed some more light on the breakdown of this last number into the key industries PW Press Release (2016):

–infrastructure: $45.2 billion;

–agriculture: $32.4 billion;

–transport: $13 billion;

–security: $10 billion;

–media and entertainment: $8.8 billion;

–insurance: $6.8 billion;

–telecommunication: $6.3 billion;

–mining: $4.4 billion;

–total: $127.3 billion.

Given the upswing in the financial market and growing trends, there appears to be many good reasons for optimism. Some of us may suggest cautious optimism as hurdles need to be overcome. Technical challenges, immature technology, delays in legislation, ever-changing certification and operational requirements, safety, privacy and security concerns, impatient investors, programmatic risks and so on continue to grow. Given the pace of growth of this industry and the numerous challenges, it is very difficult, if not impossible, to contain all necessary answers and solutions in one resource.

The purpose of this book is to provide important insights into important areas of UAV development and application, which emphasizes the use of multi-rotor platforms. This book is an outcome of the research at the University of Cincinnati, Ohio, in the USA and the University of Bordeaux in France. Furthermore, the chapters of this book from various contributors cover topics such as classification, UAV integration in the national airspace, architecture, mission and path planning, task optimization, separation assurance and fault tolerance, navigation and flight control, and data/image processing. We now provide a brief introduction to the rest of the chapters of this book.

I.2. UAV integration, classification, mission planning and system architecture

In the fall of 2017, Elaine L. Chao, U.S. Secretary of Transportation, stated that US Department of Transportation (2017) The Drone pilot program will accelerate the safe integration of drones into our airspace by creating new partnerships between local governments, the Federal Aviation Administration (FAA), and private drone operators. These partnerships will allow local communities to experiment with new technologies like package delivery, emergency drone inspections, and more, on terms that work for them and in ways that support a unified and safe airspace. The term unified and safe airspace is a key enabler. NASA has stepped up and is leading the way in developing solutions for UAS (Unmanned Aerial Systems) integration in the NAS (National Airspace System) research activities NASA (2016):

–Technical challenge-DAA: UAS detect and avoid operational concepts and technologies – it aims to develop, detect and avoid (DAA) operational concepts and technologies in support of the standards, to enable a broad range of UAS that have communication, navigation and surveillance (CNS) capabilities consistent with IFR operations, and are required to detect and avoid manned and unmanned air traffic.

–Technical challenge-C2: UAS command and control – it aims to develop satellite (Satcom) and terrestrial-based command and control (C2) operational concepts and technologies in support of the standards to enable a broad range of UAS that have CNS capabilities consistent with IFR operations, and are required to leverage allocated protected spectrum.

–Demonstration activity: systems integration and operationalization (SIO)– it aims to demonstrate robust UAS operations in the NAS by leveraging integrated DAA, C2 and state-of-the-art vehicle technologies with a pathway towards certification to inform FAA UAS integration policies and operational procedures.

In Chapter 1, Catherine Ronfle Nadeau and George (Tom) Black discuss the European and the US perspectives on UAV classification and associated missions, regulation and safety, certification and air traffic management. It is interesting to reflect on these two differing points of view as both sides try to grasp the enormity of the challenges which lie ahead towards total integration of UAVs in the NAS. In Chapter 2, Jean Louis Roch introduces the topic of UAS classification and general associated missions by providing specific examples of both already existing and potential future new civilian applications of UAS referred to as enterprise drones or commercial drones as opposed to consumer drones purchased by individuals for non-commercial and non-professional purposes Meola, A. (2017). In Chapter 3, Sophie Lenthilac describes the use of UAS, a fairly complex system and a structured systems engineering approach, which offers a set of useful tools for the following purposes: trade-off studies, functional and non-functional analysis, requirement analysis, operational analysis, insights into designing system architecture, the essential human factor component, and validation and verification techniques.

I.3. Path planning, separation assurance and fault tolerance

UAVs carry a payload and travel from one place to another. The optimization of UAV operations in an integrated airspace requires effective approaches to path planning, as well as separation assurance to ensure safety of operations. The commonly used, electric propelled, multi-rotor UAV platform, which is constrained not to exceed a weight of 55 pounds (FAA restrictions), has relatively low endurance times. It is important to use flight time in an efficient manner, especially when the airspace is cluttered with no-fly zones (airports, urban centers, etc.). In Chapter 4, Mohammadreza et al. describe in detail the development of optimization algorithms for path planning of single and cooperating UAVs, operating in the NAS, in the presence of other moving and/or stationary obstacles. A popular optimization approach suggested to solve this NP-hard path planning problem is based on mixed-integer linear programming (MILP) techniques.

As the number of UAVs in the NAS grows, safety concerns resulting from potential air collisions need to be addressed in a systematic structured approach, which enables the integration of unmanned air vehicles in non-segregated airspace. A quintessential component of the safe airspace requirement is conflict detection and resolution Yang et al. (2016). Chapter 5 discusses a genetic fuzzy logic approach to solve the aircraft conflict resolution benchmark problem described by the Airport Cooperative Research Program (ACRP) (2017). The objective of this benchmark problem is to obtain conflict-free trajectories for the aircraft such that the total cost of maneuvers is minimized. A unique architecture is presented, which consists of a hidden layer of neurons and a layer of fuzzy inference systems (FISs) that provide the final output. An artificial intelligence called EVE is used to train the system, and once it is trained, its capability is evaluated on a set of test scenarios. The EVE training artificial intelligence is a genetic fuzzy tree that is designed to train large-scale intelligent systems. EVE has been applied to an array of problems, including aircraft control Ernest et al. (2016). It accomplishes this high performance and extreme scaling by maximizing the effectiveness of each evaluation of the fitness function. As EVE is a learning system specially aimed at optimizing large-scale fuzzy systems, it is able to be applied recursively on itself. As such, EVE has been self-optimized over many generations, and will serve as the ideal trainer for large-scale UAV separation assurance problems.

The drive to keep costs at a low, the competition in the growing civilian UAV market, the emergence of the multi-copter UAVs from the hobbyist circles, the lack of rigor and regulation as opposed to manned flight and faults in UAS are events that happen, and come mostly in an unexpected manner. A fault may be defined as follows: A fault is an unpermitted deviation of at least one characteristic property or parameter of the system from the acceptable/usual/standard condition Isermann et al. (1997). UAS are safety-critical systems which are imperative to prevent catastrophic events and save life, property, litigation cost and the financial bottom line. Chapter 6 presents a flatness-based approach for fault-tolerant control. For a differentially flat system, a set of variables can be found, named flat outputs, so that states and control inputs can be expressed as their functions and time derivatives. The fault detection and isolation stage is implemented using a simple threshold-based approach; the residual signal is obtained by comparing the signal coming from the sensor and the fault-free version obtained from the differential flatness. Chapter 8 presents a unique approach to mathematical modeling and fault-tolerant controller design for a tiltingrotor quadcopter, which provides additional actuated controls as the propeller motors have an additional degree of freedom that enable the rotor to tilt about the axis of the quadcopter arm. Although conventional quadcopters are commercially available, they are underactuated systems and sensitive to the failure of one propeller, whereas the modified tilt-rotor quadcopter is a fault-tolerant system capable of handling propeller failure.

I.4. Technological developments: navigation, flight control, situational awareness, additive manufacturing and collaborative swarming

As the value of UAS operations increases, we anticipate an increased focus on high-fidelity multi-rotor platform simulation for the purpose of flight control development and validation. This is particularly important in package delivery using UAS where the payload weight, volume and center of gravity may vary, requiring an effective flight controller capable of adaptability. Hence, it is crucial to obtain and develop an accurate dynamic model of the system being analyzed. In the following paragraphs, a literature survey is provided with respect to quadrotor modeling, system identification, control design and instrumentation. Frequency-domain system identification techniques, such as the Comprehensive Identification from Frequency Responses (CIFER®) software package Tischler and Remple (2012), have proven to produce accurate models of various rotorcrafts Woodrow et al. (2013). Input–output data based on flight tests is used to develop a fixed wing aircraft/rotorcraft dynamic model. Frequency-domain methods are especially effective for development and validation of flight control systems for the following reasons: they are well-suited to complex problems, including multiple overlapping modes, unstable systems, low signal-to-noise ratio; frequency responses are non-parametric characterizations obtained without first determining a state-space model structure; broken-loop and closed-loop responses provide an important paper trail for bare-airframe and closed-loop models; feedback stability and noise amplification determined from broken-loop frequency responses, crossover frequency, gain/phase margins; modern handling quality requirements are largely based on frequency responses Wei et al. (2017). In Chapter 7, we use CIFER® to extract and verify a model for a quadcopter, and optimize a flight controller using an Linear quadratic regulator (LQR) approach.

Certain UAV applications require operations in hostile and tough environments such as in a wildfire situation or in caves. In indoor environments, the global positioning system (GPS) is not available for localization, and a camera cannot be effectively used in low or varying lighting conditions. Chapter 8 presents the autonomous navigation of a quadrotor UAV around obstacles in an indoor environment using limited knowledge about the environment generated from on-board sensors comprising inertial and sonar sensors. The system is also capable of tracking a known target and geo-locating it using sensors, and returning the image and position of the target to the ground station. Chapter 9 discusses the use of UAVs in detecting and tracking wildfires to assist in firefighting operations to enhance human effectiveness in this high-risk job. A fuzzy logic system is trained using the genetic algorithm to be able to detect fire pixels using both the visual and Forward-Looking Infrared Radar (FLIR) video feeds as inputs. Furthermore, a two-stage cascaded fuzzy logic system is presented, in which the first stage uses the visual data and the second stage processes the FLIR data to make a near-accurate detection of fire