Autonomous Navigation and Deployment of UAVs for Communication, Surveillance and Delivery E-Book
Hailong Huang
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Autonomous Navigation and Deployment of UAVs for Communication, Surveillance and Delivery Authoritative resource offering coverage of communication, surveillance, and delivery problems for teams of unmanned aerial vehicles (UAVs) Autonomous Navigation and Deployment of UAVs for Communication, Surveillance and Delivery studies various elements of deployment of networks of unmanned aerial vehicle (UAV) base stations for providing communication to ground users in disaster areas, covering problems like ground traffic monitoring, surveillance of environmental disaster areas (e.g. brush fires), using UAVs in rescue missions, converting UAV video surveillance, and more. The work combines practical problems, implementable and computationally efficient algorithms to solve these problems, and mathematically rigorous proofs of each algorithm's convergence and performance. One such example provided by the authors is a novel biologically inspired motion camouflage algorithm to covert video surveillance of moving targets by an unmanned aerial vehicle (UAV). All autonomous navigation and deployment algorithms developed in the book are computationally efficient, easily implementable in engineering practice, and based only on limited information on other UAVs of each and the environment. Sample topics discussed in the work include: * Deployment of UAV base stations for communication, especially with regards to maximizing coverage and minimizing interference * Deployment of UAVs for surveillance of ground areas and targets, including surveillance of both flat and uneven areas * Navigation of UAVs for surveillance of moving areas and targets, including disaster areas and ground traffic monitoring * Autonomous UAV navigation for covert video surveillance, offering extensive coverage of optimization-based navigation * Integration of UAVs and public transportation vehicles for parcel delivery, covering both one-way and round trips Professionals in navigation and deployment of unmanned aerial vehicles, along with researchers, engineers, scientists in intersecting fields, can use Autonomous Navigation and Deployment of UAVs for Communication, Surveillance and Delivery to gain general knowledge on the subject along with practical, precise, and proven algorithms that can be deployed in a myriad of practical situations.
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Table of Contents
Cover
Title Page
Copyright
Author Biographies
Preface
Chapter 1: Introduction
1.1 Applications of UAVs
1.2 Problems of Autonomous Navigation and Deployment of UAVs
1.3 Overview and Organization of the Book
1.4 Some Other Remarks
References
Chapter 2: Deployment of UAV Base Stations for Wireless Communication Coverage
2.1 Introduction
2.2 Related Work
2.3 UAV‐BS Deployment for Maximizing Coverage
2.4 UAV‐BS Deployment for Maximizing Coverage and Minimizing Interference
2.5 Voronoi Partitioning‐Based UAV‐BS Deployment
2.6 Range‐Based UAV‐BS Deployment
2.7 Summary
References
Notes
Chapter 3: Deployment of UAVs for Surveillance of Ground Areas and Targets
3.1 Introduction
3.2 Related Work
3.3 Asymptotically Optimal UAV Deployment for Surveillance of a Flat Ground Area
3.4 UAV Deployment for Surveillance of Uneven Ground Areas
3.5 2D UAV Deployment for Ground Target Surveillance
3.6 3D UAV Deployment for Ground Target Surveillance
3.7 Summary and Future Research
References
Note
Chapter 4: Autonomous Navigation of UAVs for Surveillance of Ground Areas and Targets
4.1 Introduction
4.2 Related Work
4.3 Asymptotically Optimal Path Planning for Surveillance of Ground Areas
4.4 Navigation of UAVs for Surveillance of a Moving Ground Area
4.5 Navigation of UAVs for Surveillance of Moving Targets on a Road Segment
4.6 Navigation of UAVs for Surveillance of Moving Targets along a Road
4.7 Navigation of UAVs for Surveillance of Groups of Moving Ground Targets
4.8 Summary and Future Research
References
Notes
Chapter 5: Autonomous UAV Navigation for Covert Video Surveillance
5.1 Introduction
5.2 Related Work
5.3 Optimization‐Based Navigation
5.4 Biologically Inspired Motion Camouflage‐based Navigation
5.5 Summary and Future Work
References
Chapter 6: Integration of UAVs and Public Transportation Vehicles for Parcel Delivery
6.1 Introduction
6.2 Related Work
6.3 System Model
6.4 One‐way Path Planning
6.5 Round‐trip Path Planning in a Deterministic Network
6.6 Round‐trip Path Planning in a Stochastic Network
6.7 Summary and Future Work
References
Abbreviations
Index
End User License Agreement
List of Tables
Chapter 2
Table 2.1 Algorithm A1–A3.
Table 2.2 Parameter configuration.
Table 2.3 Algorithm B1–B3.
Table 2.4 Parameter configuration.
Table 2.5 Algorithm C1–C3.
Table 2.6 Algorithm
D1–D4
.
Chapter 4
Table 4.1 Frequently used symbols and their meanings.
Chapter 5
Table 5.1 The average values of the linear speed
, the derivative of the UA...
Chapter 6
Table 6.1 The distributions of route traversal times.
Table 6.2 The distributions of departure instants.
Table 6.3 The computation process of applying Algorithm 6.1 to the example n...
Table 6.4 The computation process of applying Algorithm 6.2 to the example n...
Table 6.5 Stored values of
,
and
.
List of Illustrations
Chapter 1
Figure 1.1 The usage of UAVs.
Chapter 2
Figure 2.1 An illustrative street graph.
Figure 2.2 Illustration of several key notations. (a)
is an interior point...
Figure 2.3 The street graph with four stationary BSs.
Figure 2.4 Objective function value vs.
.
Figure 2.5 The trade‐off between the coverage ratio and the communication co...
Figure 2.6 Performance for different values of
. (a) Coverage ratio. (b) Co...
Figure 2.7 The street graph with five stationary charging poles.
Figure 2.8 Objective function value vs.
(
).
Figure 2.9 The trade‐off between the coverage ratio and the interference eff...
Figure 2.10 Comparison of the proposed algorithm with the greedy algorithm. ...
Figure 2.11 The influence of
,
, and
on (a) coverage ratio, (b) interfer...
Figure 2.12 (a) Illustration of the considered scenario. (b) Construction of...
Figure 2.13 (a) The trajectories of 15 UAV‐BSs and the final Voronoi cells f...
Figure 2.14 (a) The quality of coverage VS the number of UAV‐BSs. (b) The co...
Figure 2.15 Demonstration of convergence. (a) Initial positions. (b) Movemen...
Figure 2.16 The influence of measurement uncertainty.
Figure 2.17 Performance for different numbers of users and numbers of UAVs. ...
Chapter 3
Figure 3.1 The visibility cone. Point
can be seen by the UAV, while point
Figure 3.2 A triangulation
consisting of equilateral triangles.
Figure 3.3 The center of an equilateral triangle and the three congruent Vor...
Figure 3.4 Constructing region
from region
.
Figure 3.5 The ground region
.
Figure 3.6 The number of UAVs
vs.
.
Figure 3.7 Deployment of 19 UAVs at 109 m by the proposed approach. The dash...
Figure 3.8 The deployments by the algorithm of [42]. (a)
and the UAVs are ...
Figure 3.9
vs.
.
Figure 3.10 The region a UAV can see.
Figure 3.11 An illustration of a very uneven area.
Figure 3.12 The construction of polygon
by adding
non‐intersecting diago...
Figure 3.13 The triangulation
of the polygon
.
Figure 3.14 The construction of the triangulation
. A, B, C are vertices of...
Figure 3.15 (a) A square area with three very uneven areas. (b) The construc...
Figure 3.16 Using UAVs for target surveillance.
Figure 3.17 Construction of
,
, and
.
Figure 3.18 Candidate sectors (solid curve on the circle centred at
) of th...
Figure 3.19 (a) UAV deployment region and probability density
. (b) Traject...
Figure 3.20 Construction of axes and
.
Figure 3.21 Several typical situations a UAV may meet during the movement. (...
Figure 3.22 The area of interest.
Figure 3.23 Simulation result with 15 UAVs. (a) The horizontal movement. (b)...
Figure 3.24 Simulation results with 15 UAVs for the case with mobile targets...
Figure 3.25 Simulation results by the baseline method. (a) The horizontal mo...
Figure 3.26 Average final QoC in cases with static targets and average appro...
Chapter 4
Figure 4.1 Illustration of an
‐convex closed planar set
.
Figure 4.2 Illustration of constructing the closed paths for UAVs.
Figure 4.3 Constructing region
from region
.
Figure 4.4
‐neighborhood of curve
(set
).
Figure 4.5 Constructing a set of straight lines to partition
into
sub‐re...
Figure 4.6 Trajectories constructed by the proposed approach. (a)
. (b)
....
Figure 4.7 Trajectories constructed by the benchmark method. (a)
. (b)
....
Figure 4.8 Comparison with the benchmark method. (a)
versus
. (b) Computa...
Figure 4.9 (a) The illustration of the direction of the tangent. (b) The ini...
Figure 4.10 The propagation of bushfire.
Figure 4.11 (a–c) Monitoring the frontier of bushfire. (d) The movements gui...
Figure 4.12 Objective function values during the movement.
Figure 4.13 (a–c) Monitoring a complex mobile area. (d) The movements guided...
Figure 4.14 Objective function values for the complex scenario.
Figure 4.15 The considered road
.
Figure 4.16 (a) The initial closest points. (b) The illustration of the dire...
Figure 4.17 The diagram of the proposed navigation scheme.
Figure 4.18 Case 1: UAVs' positions at some key steps. The squares represent...
Figure 4.19 Case 1: The covered number of targets against the total number o...
Figure 4.20 Case 2: UAVs' positions at some key steps. The movements are rec...
Figure 4.21 Case 2: Comparison of the proposed method and the benchmark meth...
Figure 4.22 Case 3: UAVs' positions at some key steps. The movements are rec...
Figure 4.23 Case 3: Comparison of the proposed method and the benchmark meth...
Figure 4.24 A road with curvilinear coordinates.
Figure 4.25 An illustration of Voronoi cells with 5 UAVs.
Figure 4.26 (a) The movements of 4 UAVs (marked by triangles). The initial p...
Figure 4.27 (a) Initial density function and UAV positions. A video recordin...
Figure 4.28 (a) Two groups of targets merge into one group. (b)
coordinate...
Figure 4.29 The Voronoi cells of five UAVs. The pink parts of the paths repr...
Figure 4.30 (a) Static positions of UAVs (black squares) and the visible ran...
Figure 4.31 (a) The deployment of static UAVs. (b–d) The movements of 4 auto...
Chapter 5
Figure 5.1 The feasible flight zone when the target movement prediction is (...
Figure 5.2 The UAV‐target angle and distance. As all the notations in this f...
Figure 5.3 An illustrative example of constructing the path.
Figure 5.4 (a) UAV trajectories that can reach
from
with heading angle
Figure 5.5 The UAV trajectories (solid line) to monitor a target (dash line)...
Figure 5.6 UAV trajectories by the proposed approach (solid) and a random be...
Figure 5.7 A comparison of the proposed method and the benchmark method (ave...
Figure 5.8 Simulation where the target moves along Road 4: (a) Trajectories;...
Figure 5.9 Simulation where the target moves along Road 5. (a) Trajectories....
Figure 5.10 Simulation where the target moves along real roads: (a) Trajecto...
Figure 5.11 The interaction between the optimization algorithm in MATLAB and...
Figure 5.12 Simulation in CoppeliaSim. The movements of the UAV and the targ...
Figure 5.13 The absolute values of the derivative of UAV‐target distance and...
Figure 5.14 The measurements available at the UAV.
Figure 5.15 Illustration of the vector
.
Figure 5.16 Case 1: (a, b) UAV trajectory for 80 and 150 seconds (a video is...
Figure 5.17 Case 2: UAV Trajectory for 78 and 150 seconds (a video is availa...
Figure 5.18 Impact of measurement errors.
Figure 5.19 Case 3: (a‐b) UAV trajectory for 74 and 150 seconds (a video is ...
Chapter 6
Figure 6.1 The illustration of the network combining the public transportati...
Figure 6.2 The classification of parcels based on size, weight, and the dist...
Figure 6.3 An illustration of the public transportation network and the UAV ...
Figure 6.4 A small example to illustrate the path from the depot
to the cu...
Figure 6.5 An example to illustrate the dependence of travel times and waiti...
Figure 6.6 (a) There are five vehicles scheduled to depart at instant 10, 20...
Figure 6.7 The network used to demonstrate how the proposed algorithm works....
Figure 6.8 The CDFs of the two found paths.
Figure 6.9 Converting
to
by turning nodes into vertices and complex edge...
Figure 6.10 Constructing the shortest UAV path.
Figure 6.11 Delivery fails when the bottom vehicle leaves the stop earlier t...
Figure 6.12 The distribution of the appearance time of a vehicle at a stop....
Figure 6.13 Converting
to
accounting for uncertainty. (a) The original n...
Figure 6.14 A working example. (a) The multimodal network with the depot, th...
Figure 6.15 The extended coverage from nodes 2 and 3.
Figure 6.16 (a) A network with four services. (b) The extended coverage vers...
Figure 6.17 The relationship between several key notations of time instants,...
Figure 6.18 Extending the path from
to
by adding a link
.
Figure 6.19 An example of updating
,
and
.
Figure 6.20 Vehicle appearance time distributions and distribution of flight...
Figure 6.21 The robust version to update labels of nodes. (a) Node
can be ...
Figure 6.22 (a) The example used for evaluation. (b) The traversal times of ...
Figure 6.23 (a) Round trip path in the real‐world transit network. The UAV i...
Guide
Cover Page
Autonomous Navigation and Deployment of UAVs for Communication, Surveillance and Delivery
Title Page
Copyright
Author Biographies
Preface
Table of Contents
Begin Reading
Abbreviations
Index
Wiley End User License Agreement
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Jeffrey ReedThomas Robertazzi
Autonomous Navigation and Deployment of UAVs for Communication, Surveillance and Delivery
Hailong Huang, PhDDepartment of Aeronautical and Aviation EngineeringThe Hong Kong Polytechnic University, Hong Kong, China
Andrey V. Savkin, PhDSchool of Electrical Engineering and TelecommunicationsUniversity of New South Wales, Sydney, NSW, Australia
Chao Huang, PhDDepartment of Industrial and Systems EngineeringThe Hong Kong Polytechnic University, Hong Kong, China
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Author Biographies
Hailong Huang received the BSc degree in automation, from China University of Petroleum, Beijing, China, in 2012, and received the PhD degree in Systems and Control from the University of New South Wales, Sydney, Australia, in 2018. He is an Assistant Professor at the Department of Aeronautical and Aviation Engineering at The Hong Kong Polytechnic University, Hong Kong. His current research interests include guidance, navigation, and control of mobile robots, multi‐agent systems, and distributed control.
Andrey V. Savkin received the MS and PhD degrees in mathematics from the Leningrad State University, Saint Petersburg, Russia, in 1987 and 1991, respectively. From 1987 to 1992, he was with the Television Research Institute, Leningrad, Russia. From 1992 to 1994, he held a Postdoctoral position in the Department of Electrical Engineering, Australian Defence Force Academy, Canberra. From 1994 to 1996, he was a Research Fellow in the Department of Electrical and Electronic Engineering and the Cooperative Research Centre for Sensor Signal and Information Processing, University of Melbourne, Australia. From 1996 to 2000, he was a Senior Lecturer, and then an Associate Professor in the Department of Electrical and Electronic Engineering, University of Western Australia, Perth. Since 2000, he has been a Professor in the School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW, Australia. His current research interests include robust control and state estimation, hybrid dynamical systems, guidance, navigation and control of mobile robots, applications of control and signal processing in biomedical engineering and medicine. He has authored/coauthored seven research monographs and numerous journal and conference papers on these topics. He has served as an Associate Editor for several international journals.
Chao Huang received the BSc degree in automation, from China University of Petroleum, Beijing, China, in June 2012, and received the PhD degree from the University of Wollongong, Wollongong, Australia, in December 2018. She is a research assistant professor at the Department of Industrial and Systems Engineering, the Hong Kong Polytechnic University, Hong Kong. Her interests include motion planning, human machine collaboration, fault tolerant, automotive control and application.
Preface
Unmanned aerial vehicles (UAVs), also known as aerial drones, have started to reshape our modern life, thanks to the inherent attributes such as mobility and flexibility. Once national legislations allow UAVs to fly autonomously, swarms of UAVs will populate our city skies to conduct various missions: rescue operations, surveillance, and monitoring, and also some emerging applications such as goods delivery and telecommunications.
This book is primarily a research monograph that presents, in a detailed and unified manner, the recent advancements relevant to the applications of UAVs in wireless communications, surveillance and monitoring of ground targets and areas, and goods delivery. The main intended audience for this monograph includes postgraduate and graduate students, as well as professional researchers and industry practitioners working in a variety of areas such as robotics, aerospace engineering, wireless communications, signal processing, system theory, computer science and applied mathematics who have an interest in the growing field of autonomous navigation and deployment of UAVs. This book is essentially self‐contained. The reader is assumed to be familiar with basic undergraduate level mathematical techniques. The results presented are discussed to a great extent and illustrated by examples. We hope that readers find this monograph interesting and useful and gain a deeper insight into the challenging issues in the field of autonomous navigation and deployment of UAVs for communication, surveillance, and delivery. Moreover, in the book, we have made comments on some open issues, and we encourage readers to explore them further. The material in this book derives from a period of research collaboration between the authors from 2018 to 2022. Some of its parts have separately appeared in journal and conference papers. The manuscript integrates them into a unified whole, highlights connections between them, supplements them with new original findings of the authors, and presents the entire material in a systematic and coherent fashion.
In preparation of this research monograph, the authors wish to acknowledge the financial support they have received from the Australian Research Council. This research work has also received funding from the Australian Government, via grant AUSMURIB000001 associated with ONR MURI grant N00014‐19‐1‐2571. Also, the authors are grateful for the support they have received throughout the production of this book from the School of Electrical Engineering and Telecommunications at the University of New South Wales, Sydney, Australia, the Department of Aeronautical and Aviation Engineering and the Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China.
Furthermore, Andrey Savkin is grateful for the love and support he has received from his family. Hailong Huang and Chao Huang are also grateful for the support from their parents.
Hailong HuangAndrey V. SavkinChao Huang
Chapter 1Introduction
1.1 Applications of UAVs
Thanks to the inherent attributes such as mobility and flexibility, unmanned aerial vehicles (UAVs), also known as aerial drones, have started to reshape our modern life. Once national legislations make laws to allow UAVs to fly autonomously, swarms of UAV will populate the sky of our cities to conduct various missions: rescue operations, surveillance, and monitoring, and also some emerging applications such as goods delivery and telecommunications. Some typical applications of UAVs are summarized in Figure 1.1.
UAVs can service humans. A typical example is that UAVs play the role of aerial base stations to provide communication service to cellular users, especially in some congested urban areas [1]. This is a promising solution to 5G and beyond‐5G networks. It is also very useful in disaster areas where the communication infrastructures are down. Also, UAVs have been used to track targets, such as humans, animals, and vehicles [2], and in agriculture [3], traffic monitoring [4], architecture inspection [5], environment monitoring [6], disaster management [7]. Furthermore, UAVs can provide service to wireless sensor networks (WSNs) [8]. Working as the aerial sinks, the UAVs can collect sensory data from distributed sensor nodes. They can navigate ground robots since they may have a better view of the environment, and they can also collaborate with ground robots to execute complex tasks. Beyond those presented in Figure 1.1, package delivery is another service UAVs can provide, which also attracts great interest from both the research community [9] and logistics companies [10–14].
In addition to the sole‐UAV usage, the collaboration between UAVs and ground vehicles for surveillance and parcel delivery has gained attention. A key point is using one or more UAVs to visit a given set of positions. Considering the limited capacity of the on‐board battery, the flying time of a UAV is constrained. A straightforward idea is to install ground charging stations at which a UAV can recharge or replace its battery [15]. Where to deploy the ground charging stations influences the coverage performance that UAVs can achieve. Given a set of ground charging stations, the review [16] focuses on the path planning problem of a UAV to visit a given set of positions successfully. Another idea is to use ground robots to function as mobile charging platforms [17–19]. Specifically, the chapter [17] considers the usage of a battery‐constrained UAV and a battery‐unlimited ground robot for large‐scale mapping. The UAV can recharge its battery on the ground robot. While the ground robot can only move on the road network, the UAV can traverse the areas off the road network. The authors provide a strategy for the cooperation of the UAV and ground robot such that they can finish the mapping mission under the energy constraint of the UAV. The chapter [18] considers a similar scenario as [17]. The authors provide an integer program for this problem. Different from [17, 18], in [19], the UAV can travel with a ground robot together and recharge its battery during the movement. Clearly, this strategy reduces the time to finish the mission.
Figure 1.1 The usage of UAVs.
1.2 Problems of Autonomous Navigation and Deployment of UAVs
In order to fully reap the benefits of UAVs in the aforementioned real‐life applications, some core technical challenges, including the 3D placement of multiple UAVs, the trajectory/movement design, the energy efficiency optimization. These deployment and autonomous navigation of UAVs play an extremely important role for the usage of UAVs. A typical scenario is that a team of collaborating UAVs is conducting a mission for which it is needed to determine some optimal operation status including physical positions and other application‐dependent attributes such as transmission powers when UAVs serve as aerial base stations.
For a static situation, we can formulate some UAV navigation and deployment problems as optimization problems. The objective function can be application‐dependent. For example, when UAVs are used to serve cellular users, a typical problem is how to deploy UAVs to cater to wireless users' instantaneous traffic demands. Existing research has investigated the trajectory planning problem for a single UAV to relay information [20] and broadcast/multicast data packets [21]. Besides the trajectory planning problem, researchers have also investigated the UAV deployment so that wireless coverage is provided to the static users in a target region, by designing the optimal operating location in 3D space [22], minimizing the number of the stop points for the UAV [23], and minimizing the total deployment time [24]. When UAVs are used to monitor ground targets, some metric describing the quality of surveillance will be regarded as the objective. Moreover, the deployment problem often comes with some constraints. An important constraint is the connectivity [25]. When multiple UAVs operate together, they need to form a connected network with some ground base station for communication. The common approach is to introduce a connectivity graph, which can restrict the relative positions of the UAVs so that a valid communication channel between a pair of UAVs is guaranteed. Another constraint is about collision avoidance. For a particular area, there may be existing some infrastructures such as buildings, which may be regarded as no‐fly zones [26]. Such no‐fly zones further place some constraints to the UAV deployment problem. Then, the deployment problem becomes a constrained optimization problem, and the solution to this problem is the positions of the UAVs.
For a dynamic situation, the optimal positions of UAVs will be time‐varying. In this case, deployment and navigation of UAVs are coupled. The optimal positions of UAVs are computed by addressing the deployment problem, and then the UAVs are navigated from their current positions to new positions. During the navigation process, it should be guaranteed that the connectivity is maintained, UAVs do not collide with any obstacles and do not enter any no‐fly zones. Model predictive control (MPC) [27] has been recognized as an important tool to address this type of constrained optimization problems. A review of recent results on deployment and navigation of teams of collaborating UAVs for surveillance can be found in the survey paper [28]. Moreover, a review of challenges and achievements in reaching full autonomy of UAVs is presented in [29]. The research monograph [30] studies various applications of UAVs for support of wireless communication networks.
Though the research community has already made a great contribution to the navigation and deployment of UAVs, many of the existing approaches suffer from the complexity for real‐time implementation. Additionally, the mobility of ground targets (in wireless coverage and also surveillance applications) is generally overlooked by many research articles, based on which, the mobility of UAVs needs to be carefully considered to get a better quality of service (QoS). Motivated by such research gaps, and to facilitate the application of UAVs, navigation and deployment methods should be implemented in real‐time at each UAV using local information only. This requires proposed methods be computationally efficient. Moreover, the optimality of the overall performance of the UAVs should be guaranteed. Therefore, decentralized algorithms are often needed for UAV deployment and navigation.
1.3 Overview and Organization of the Book
In this section, we briefly describe the results presented in this research monograph.
This book is problem‐oriented, not technique‐oriented. So each chapter is self‐contained and devotes to a detailed discussion of an interesting problem that arises in the rapidly developing area of UAVs' applications. We present relevant approaches from a control system viewpoint. Thus, in Chapters 2–6, we first present system models and then formulate problems of interest, which are followed by proposed approaches to address the problems. Finally, we present computer simulation results to illustrate the effectiveness of the proposed approaches. The organization of the book is as follows.
In Chapter 2, we discuss an application of UAVs in providing cellular service as aerial base stations. We study a problem of proactive UAV deployment. The deployment of UAVs plays a key role for the quality of service in such applications. Two typical scenarios are studied. The first scenario is in urban areas, and the UAVs are deployed over streets to avoid collision with buildings. The second scenario is for disaster areas. We formulate several optimization problems to optimize the quality of service provided by the UAVs, and computationally efficient algorithms are presented to address these problems.
Chapter 3 discusses some recent developments in using UAVs to monitor ground areas and targets. Specifically, we present approaches to finding the minimum number of UAVs equipped with ground‐facing video cameras and their deployment positions to fully monitor an area of interest, which can be either a flat area or an uneven area with buildings, hills, or mountains. We also present algorithms that can find the optimal positions of UAVs to survey a group of ground targets within a certain area. We develop deployment algorithms for both the 2D and 3D deployment of UAVs. Theoretical analysis on the performance of these approaches is also provided.
In Chapter 4, we discuss applications of UAVs for surveillance and monitoring of ground areas and targets, which corresponds to various practical applications including but not limited to surveillance of disaster processes such as offshore oil spills, flood and coal ash spills, and monitoring ground vehicles and pedestrians. We present several decentralized algorithms for navigation of a team of UAVs to collaboratively conduct surveillance missions. The properties of these algorithms such as optimality are discussed.
Chapter 5 focuses on covert video surveillance using UAVs, which is a relatively new research area. Different from usual surveillance applications discussed in Chapters 3 and 4, covert surveillance requires that the intention of the UAVs is not discovered by the targets of interest. We present two approaches to this problem. The first approach is optimization‐based. We present a new metric to characterize the disguising performance, which evaluates the change of the relative distance and angle between the UAV and the target. Then, we formulate an optimization problem, which jointly maximizes the disguising performance and minimizes the energy efficiency of the UAV, subject to the motion constraint of the UAV and the requirement of keeping the target within view. We present a dynamic programming method to plan the UAV's trajectory in an online manner. The second approach is a biologically inspired motion camouflage‐based method. To achieve motion camouflage, the UAV always moves on the straight line segment connecting the target and a fixed reference point. A sliding mode control strategy is developed, which only takes the bearing information as input. We present extensive computer simulations to demonstrate the performance of these approaches.
In Chapter 6, we discuss the applications of UAVs in the last‐mile parcel delivery. UAVs have been considered as a promising tool for future logistics industry by many companies thanks to reduced cost and increased mobility. However, one barrier is the limited flight time due to the limitation of onboard batteries. This chapter presents recent research results on using public transportation vehicles to assist UAV delivery. A particular attention is paid to path planning problems when UAVs can travel with public transportation vehicles, and several algorithms are presented to deal with these problem in different situations.
1.4 Some Other Remarks
Chapter 2 of this book studies using UAVs for wireless communication coverage, Chapters 3–5 are about using UAVs for video surveillance of ground areas and targets, and Chapter 6 concentrates on UAV assisted delivery. On the other hand, Chapters 3 and 3 of this book studies UAV deployment, Chapters 4 and 5 address UAV navigation, and Chapter 6 concentrates on UAV flight scheduling. Furthermore, Chapters 1–4 studies teams of UAVs, Chapter 5 concentrates on using a single UAV, whereas Chapter 6 studies UAVs collaborating with ground public transportation vehicles. Also, it should be pointed out that Chapters 2–5 study deterministic models that often contain large uncertainties, whereas Chapter 6 addresses both deterministic and stochastic models.
It should be pointed out that teams of collaborating autonomous UAVs guided by decentralized navigation algorithms developed in this book can be naturally viewed as networked control systems; see e.g. [31] and references therein.
The main results of this research monograph were originally published in the journal papers [32–49].
The literature in the field of autonomous UAV navigation and deployment for communication, ground surveillance, and parcel delivery is vast, and we have limited ourselves to references that we found most useful or that contain material supplementing this text. The coverage of the literature in this book is by no means complete. We apologize in advance to many authors whose valuable research contributions have not been mentioned.
In conclusion, the area of autonomous navigation and deployment of UAVs is a fascinating discipline bridging robotics, aerospace engineering, system theory, control engineering, communications, information theory, computer science, and applied mathematics. The study of decentralized UAV navigation and deployment problems represents a difficult and exciting challenge in system engineering. We hope that this research monograph will help in some small way to meet this challenge.
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Chapter 2Deployment of UAV Base Stations for Wireless Communication Coverage
2.1 Introduction
Due to the tremendous increase of recent wireless traffic demand, Internet Service Providers (ISPs) have been dedicated to developing effective strategies to improve user experience in cellular networks [1]. Densification of stationary base stations (BSs) is one solution [2]; however, it has various drawbacks such as the high cost of site rental and backhaul links. More importantly, it may not be efficiently utilized in nonpeak periods, which is a waste of precious resource. An alternative solution is to deploy autonomous unmanned aerial vehicles (UAVs), which work as flying BSs, to provide Internet service to user equipments (UEs). Because of the flexibility, the utility of UAVs in cellular networks attracts lots of research on the communication models including the air‐to‐ground pathloss model [3] and the interference model [4], and the placement problem including the placement of a single UAV [5, 6] and several UAVs [7, 8], etc.
Although the implementation of UAVs in cellular networks requires various techniques to come together, in this chapter, we focus on one key issue: the deployment of UAVs. The positions of UAVs not only influence the coverage of the area of interest but also impact the interference at a certain UE from different UAVs. The interference places challenges on the signal demodulation at UEs, because when the signal to interference and noise ratio is below a threshold, UEs cannot demodulate the intended signal. Therefore, the fundamental question we answer here is how to deploy the UAVs such that they can serve the largest number of UEs and impose the least interference on UEs in a given area.
Different from the assumption in many existing papers, i.e. UEs are randomly scattered or following a predefined distribution (see e.g. [2]), we consider a more practical scenario. In particular, the UEs to be served by UAVs are outdoor. This assumption can make the performance of our approach closer to reality because indoor UEs are not the targets of UAVs. To represent city environments, we adopt a street graph model. We construct a street graph with a set of points, and each street in the area is represented by a subset of these points. Furthermore, we associate each street point with a UE density function, which reflects the traffic demand at this point during a certain period of time. Such UE density function plays an important role in determining the positions of UAVs. In practice, the UE density function can be constructed via either history recordings or online crowdsensing [9]. In this chapter, we choose the former method, i.e. we build up the UE density functions based on a real dataset collected from a social discovery mobile App: Momo [10], which can be a good reflection of the real UE distribution.
Regarding the feasible positions of UAVs, we have the following concerns. First, to avoid hitting tall buildings, the UAVs are deployed over the streets. Second, the UAVs should be deployed within a certain range of the existing electric power poles (we call it charging pole in the rest of the chapter to indicate its function), where they can recharge the battery.1 The reason to involve this condition is that the working time of the battery on the state‐of‐the‐art commercial UAVs, such as DJI,2 is quite limited. Thus, to guarantee on the service time, the flying time between the charging pole and hovering position should be carefully controlled, otherwise, the UAVs may run out of battery. There are some stationary BSs on the street graph and the UAVs together with these BSs that form a connected communication graph. In particular, the BSs work as access points and the UAVs serve UEs. A request from a UE will be sent to a BS by the serving UAV either directly or via other relay UAVs.
We first assume that the UAVs use different frequencies to transmit data to UEs. We formulate an optimization model to maximize the UE coverage and minimize the relay cost between UAVs. We seek the optimal positions on the street graph for the UAVs to optimize the objective subject to that of the UAVs and the BSs to form a connected graph. Second, we formulate an optimization problem seeking the optimal positions of the UAVs on the street graph to maximize UE coverage and minimize the interference effect. This problem is slightly similar to the blanket coverage problem of [11]. However, in the problem considered in this chapter, complete (perfect) blanket coverage is not realistic. Furthermore, unlike [11], we take into account interference. We analyze the properties of local maxima of our problem, based on which, we then propose a distributed algorithm to determine the locally optimal positions for UAVs. In this distributed algorithm, each UAV only requires the local information such as the UE densities and the positions of the nearby UAVs, which makes it superior to some existing centralized algorithms, e.g. [8, 12–14]. We prove that the proposed algorithm converges to the local maxima within a finite number of steps.
Another scenario this chapter considers is the use of autonomous UAVs to provide wireless communication services to UEs in a disaster area. After a disaster such as an earthquake, while some people are stuck inside buildings, others are somehow relocated to some open areas such as a public square for a temporary stay. For example, the Sichuan earthquake in 2008 forced about 4.8 million homeless people to relocate [15]. When a tsunami is expected, the government may ask people to leave the area. People may travel by car and follow a safe route, and then a lot of cars will be on the road. Since all or most of the existing cell towers in the affected area are destroyed, or they cannot serve such a high amount of people, people in the area may not get cellular service. In these situations, autonomous UAVs are deployed to service the affected people.
Unlike [12, 13, 16–18] assuming that the UAVs are deployed at the same altitude, we consider the case when the UAVs are deployed at different altitudes to avoid collisions. Following [12, 13], it is assumed that each user is covered by the nearest UAV, i.e. the UAV with the shortest distance to this user. The objective is to minimize the average UAV‐user distance. To address this optimization problem, a novel distributed algorithm is proposed. So it has an advantage over centralized algorithms, see e.g. [12, 13]. Instead of assuming the availability of users' locations, the proposed algorithm is based on only the strength of the received signal when the UAV communicates with users and some information that is shared by nearby UAVs. From some initial position, this algorithm reactively navigates each UAV to the center of mass of the set of the users it serves. Unlike the proactive algorithms of [12, 13, 16, 17, 19], the proposed algorithm is reactive and implementable in real‐time. Furthermore, it runs on each UAV individually and does not require heavy computations. The convergence of the algorithm is proved, and its performance is evaluated through computer simulations.
The main contributions of this chapter are twofold. First, we formulate some optimization problems for the optimal deployment of UAVs. For the case of urban areas, all the feasible positions of UAVs are at some street points. In the case of disaster areas, they are allowed to move in 3D space. Second, we present decentralized algorithms to navigate the UAVs to at least locally optimal positions. These algorithms are effortless to implement and practical in real applications compared to those centralized ones. The main results of this chapter were originally published in [20–23].
The rest of the chapter is organized as follows. Section 2.2 discusses some closely related work. Section 2.3 presents the deployment method for maximizing coverage. Section 2.4 presents the deployment method for maximizing coverage and minimizing interference. Section 2.5 presents the Voronoi partitioning‐based deployment method, and Section 2.6 further presents the range‐based reactive deployment method. Finally, Section 2.7 summarizes the chapter.
2.2 Related Work
The utility of UAVs to provide network service in cellular networks is a relatively new research area. With the development of 5G networks, UAVs will play a more and more crucial role as a continuous network support [24]. In this section, we discuss some relevant publications to this chapter and highlight the differences with our work.
One group of publications focuses on the basic wireless communication models, such as the downlink and uplink between UEs and UAVs, and between UAVs and stationary BSs. Here, the wireless communication models are different from the traditional models for ground senders and receivers. For example, in [3], the authors model the air‐to‐ground link. They show that there are two types of propagation situations: UAVs have line‐of‐sight (LoS) with UEs and UAVs have nonline‐of‐sight (NLoS) with UEs due to reflections and diffractions. A closed‐form expression is presented for the probability of LoS between UAVs and UEs. The reference [4] focuses on the interference effect. To be specific, the maximum coverage of two UAVs in the presence and absence of interference between them is investigated. Further, the air‐to‐ground channel models utilizing more information about the environment such as the shapes of the buildings were investigated [25].
Based on these fundamental models, another category considers some interesting applications. For instance, a proactive UAV deployment framework is proposed in [26], which aims at lowering the workload of existing stationary BSs. Consider three typical social activities: stadium, parade, and gathering are considered. Also, a traffic prediction scheme based on the traffic models for these activities is presented. Moreover, they discuss an operation control method to evenly deploy the UAVs. There are two main disadvantages: First, interference is not considered. Second, the operation control method evenly deploys the UAVs, which is not suitable in practice. Reference [27] studies the scenario where a UAV serves UEs which move along a street. It compares with the approaches using stationary BSs and it illustrates that the UAV introduces a considerable gain in channel quality and outperforms the comparing case in throughput. Reference [28] proposes a proactive deployment of cache‐enabled UAVs to improve the quality of experience for users. The UAVs cache some popular content based on a prediction model. Such cache would be able to reduce the data packet transmission delay, although achieving the precise prediction model is difficult in real applications. The structure of the system is worth studying. Remote radio heads (RRHs) are grouped into clusters and each RRH can only use the preassigned frequency to avoid interference. RRHs are connected to the cloud of the baseband unit (BBU) via a capacity‐constrained DSL link. RRHs transmit data packets to users via cellular links. Drones are connected to BBU through cellular links, and they transmit data packets to users via mmWave. In such a structure, the interference exists in the following two links: RRHs to users and BBU to drones.
The design of UAV communication architecture has also been studied. In [29], the authors discuss two types of communication links: UAV‐ground and UAV‐UAV. The UAV‐ground communication involves control command transmission, UAV status report, UAV‐UE data transmission, etc.; and UAV–UAV communication involves sense‐and‐avoid information‐sharing and wireless backhaul support. The UAV‐ground communication may have both LoS and NLoS links; while UAV‐UAV communication is mainly dominated by the LoS component, so the latest radio access technologies such as mmWave and free‐space optical communication can be used [30]. In particular, the UAVs and BSs form a connected graph for the communication purpose, i.e. whenever any served UE sends a request, it can be served without a significant delay due to the connectivity problem. Moreover, Ref. [31] identifies some challenges associated with multitier UAV networks and investigates the feasibility of a two‐tier UAV architecture, where some big UAVs are at a higher fixed altitude and small UAVs are at a lower fixed altitude.
The third group of relevant work is about the deployment of small cells. Reference [32] studies how to deploy base stations to satisfy both cell coverage and capacity constraints. Meta‐heuristic algorithms are presented to address the optimization problem, where UE density is considered. Reference [33]
