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Background: Unmanned Aerial Vehicle (UAV) technology has exploded in recent years. Presently UAVs are beginning to be major in roads into geographical mapping, site inspection, agriculture, and search and rescue. Methods: This paper reviewed patents and papers worldwide related to both hardware and software for the construction and deployment of UAVs and is intended to provide a snapshot of currently available UAV technologies, as well as to identify recent trends and future opportunities in affiliated hardware and software. Results: Basic components related to self-designed units are explained (e.g. platform selection, autopilot control comparison and sensor selection), and current applications and research areas are discussed. Since autonomous navigation is a key technology in UAV applications, concepts about this are also explained. Conclusions: Both in the self-designed and commercial markets, UAV components are becoming modularized. By following a standard components list, it is no longer difficult to make a customised UAV. In this way, commercial products are becoming cheaper and more standardized in their performance. Current limitations of UAVs has also become more readily detectible. Extending the flight time, improving autonomous navigation abilities, and enriching the payload capacity will be the future research focus to address these limitations.
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Title State of Technology Review of Civilian UAVs
Author(s) Chen, Siyuan; Laefer, Debra F.; Mangina, Eleni
Publication
date 2016
Publication
information Recent Patents on Engineering, 10 (3): 160-174
Publisher Bentham Science Publishers
Item
record/more
information http://hdl.handle.net/10197/8152
Publisher's
version (DOI) http://dx.doi.org/10.2174/1872212110666160712230039
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State of Technology Review of Civilian UAVs
Siyuan Chen1, Debra F. Laefer1, and Eleni Mangina2
1University College Dublin – Urban Modelling Group School of Civil Engineering Dublin,
Ireland & Earth Institute, Ireland and 2University College Dublin - School of Computer Science
Dublin, Ireland
Abstract: Background: Unmanned Aerial Vehicle (UAV) technology has exploded in recent
years. Presently UAVs are beginning to be major in roads into geographical mapping, site
inspection, agriculture, and search and rescue. Methods: This paper reviewed patents and papers
worldwide related to both hard-ware and software for the construction and deployment of UAVs
and is intended to provide a snapshot of currently available UAV technologies, as well as to
identify recent trends and future opportunities in affiliated hardware and software. Results:
Basic components related to self-designed units are explained (e.g. plat- form selection, autopilot
control comparison and sensor selection), and current applications and research areas are
discussed. Since autonomous navigation is a key technology in UAV applications, concepts
about this are also explained. Conclusions: Both in the self-designed and commercial markets,
UAV components are becoming modularized. By following a standard components list, it is no
longer difficult to make a customised UAV. In this way, commercial products are becoming
cheaper and more standardized in their performance. Current limitations of UAVs have also
become more readily detectible. Extending the flight time, im- proving autonomous navigation
abilities, and enriching the payload capacity will be the future research focus to address these
limitations.
Key words: Unmanned Aerial Vehicles, Drone, Multi-rotor, Navigation, Remote Piloted
Airborne Systems.
INTRODUCTION
A Unmanned Aerial Vehicle (UAV) is defined by the U.S. Federal Aviation
Administration (FAA) as an aircraft flown with no pilot on board [1]. Drones, Remote Piloted
Airborne Systems (RPAS) are also terms that are commonly used. UAVs were first introduced
for American military usage in 1950’s [2]. Such efforts continue in transport, surveillance, and
combat. However, in recent years, UAVs have gained great popularity in civilian usage.
Improvements in sensor and control technology have enabled UAVs to gain unprecedented
prominence in a wide range of non-military applications. This is especially true for multi-rotor
units because of their low cost and significant flexibility (due to their small size). Their
popularity is further promoted by remote control capabilities that can reduce the aircraft
operator’s exposure to dangerous environments for various forms of documentation and
investigation. Potential applications include search and rescue [3-5], civil engineering [6],
agriculture [7-9], wildlife conservation[10-12], and infrastructure inspection [13-17], as well as
transportation [18]. More use cases are shown in figure 1. According to a survey made by the
Association for Unmanned Vehicle Systems International (AUVSI) on 3136 exemptions in the
United States, the top three applications in UAVs industry are construction, infrastructure and
agriculture (fig. 2) [19]. Notably, in many jurisdictions usage is restricted by aviation authority
regulations, as opposed to commercial viability.
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Fig. 1. UAV Usage Categories
Fig. 2. UAV application distribution based on the first 3,136 exemptions granted in the United
States, as per January 20, 2016 [19]
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UAV TYPES
UAVs can be classified in many ways such as usage (civilian vs. military), maximum
take-off weight (MTOW), and lift style (fixed-wing vs. multi-rotor). The relative capabilities
between fixed-wing and multi-rotor UAVs were recently summarized by Brien et al. [20].
According to that division, the fixed-wing UAVs are more stable and larger and have higher
flying capabilities, greater payload capacity, and better endurance but at the expense of a certain
level of agility and cost.
Table 1. Capabilities of fixed-wing/multi-rotors (adapted from [20])
UAV COMPOSITION
Amongst civilian UAVs, typical components include the following: a frame, a driving
system, a power system, and a pilot system. To remotely control it and to obtain real-time video,
a controller, camera, camera gimbal, and monitor are also required. Figure 3 illustrates the basic
components for a camera-equipped UAV. Table 2 shows the electronic components used in a
typical system.
Fig. 3. Typical components of a Multi-rotor UAV (adapted from [21])
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Table 2. Components
Function
Use
Picture
Driving
Drives the prop in small multi-rotors
[22]
Drives the prop and controlled by the
Electronic speed controller
[23]
Regulates the electrical power
supplied to the motors, which is
managed by the flight controller
[24]
Power
Provides energy to the driving system,
pilot system, and payloads
[25]
Control &
Communi
cation
Receives commands from the ground
station (receiver antenna) and sends
telemetry and other data (e.g. video)
from the on-board system (transmitter
antenna)
[26]
Sends operator commands to the
UAV and receives real-time flight
data
[27]
Autopilot
Controls the power of each motor and
other systems depending on the data
received from the sensors, as well as
the commands from the ground
station; can be a commercial standard
device with open software or
something developed for a specific
solution
[28]
Establishes the geographic position of
the RPAS
[29]
Determines the distance from the sea
level
[30]
Integrated accelerometers, gyroscopes,
and magnetometers. Determines the
current rate of acceleration, changes in
rotational attributes and orientation drift
[31]
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Function
Use
Picture
Determines the relative speed
between the air and the RPAS
[32]
Points towards the ground to keep its
position over a specific area over the
ground; also records images.
[33]
Determines the distance from ground
and various obstacles
[34]
Ranges and detects static/moving
obstacles
[35]
Gives a first person view of the flight
and can be used to take pictures or
record video
[36]
Payloads
Produces photographs; usually
stabilized with a gimbal device
[37]
Thermography
Camera
Produces thermal images
[38]
Laser Sensors
Generates a point cloud of the built
and natural geometry
[39]
Gas Sensors
Detects chemical substances and
concentration in the air (e.g. CO2)
[40]
Temperature
Sensors
Determines environmental
temperature
[41]
Radioactivity
Sensors
Measures radioactivity
[42]
Other
Many other systems (physical and
electronic) and sensors can be easily
integrated
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FRAMES AND BOOMS
Frame materials are highly varied and may include wood, plastic, metal, glass, or carbon
fibres. Traditional and innovative manufacturing processes, such as injection moulding, laser
cutting, computerized numerical control (CNC) processing, 3D printing technology have all been
applied to UAV’s construction. For example, in 2013, engineers from the Advanced
Manufacturing Research Centre (AMRC) at the University of Sheffield cooperated with the
Boeing company to design and manufacture a fixed wing UAV constructed entirely of
acrylonitrile butadiene styrene (ABS), a common plastic in 3D printing. Specifically, the
application of Computational Fluid Dynamics (CFD) analysis and 3D printing technology were
able to improve aerodynamic performance [43]. Specifically, CFD was used to optimise the
chosen design and to assess the lift, drag, pitching moment, and other characteristics over a range
of angles of incidence. The application of 3D printing technology resulted in smooth leading and
trailing edges over each half-span, which was suited to the low Mach number flight regime under
which the UAV would operate.
In contrast to fixed-wing UAVs, multi-rotors UAV do not have high geometric
requirements for their frame design, except for the booms’ number and layout (fig. 4). The layout
could be of an X-shape, Y-shape, or V-shape along with the general quad-rotor, hexa-rotor, and
octo-rotor arrangements. The number of booms is usually 3, 4, 6, or 8. Additionally, each boom
may support one or two layers of props. More booms and more props tend to improve stability
and enhance payload capabilities but at the expense of battery efficiency and, thus, flight time.
More props also provide enhanced redundancy, in case of the loss of one or more rotors [44].
a. Quad-rotor
b. Hexa-rotor
c. Octo-rotor
d. Y-shape
E. V-shape
F. Double layer
Fig. 4. Multi-rotors Layout
DRIVING SYSTEM
Electronic motors are widely used as UAV driving systems. As shown in the Table 3
coreless motors and brushless motors are favoured, because they can provide a higher rolling
speed and bigger torque, as well as being more efficient and durable when compared to
traditional DC motors. However, to transform DC power into AC power and control the motor
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speed, an electronic speed controller (ESC) must be connected with a brushless motor, which
will increase the weight and system complexity.
Fuel engines and hybrid engines are also sometimes used in UAVs. A German company
recently launched Yeair, the first UAV with a combustion engine. The UAV has a 60 minute
flight duration and can support a 5 kg payload [45]. The American company Top flight
developed the Airborg H6 1500, which has a 5000W rated (6000W peak) hybrid engine, with a
11.3 litre tank and a 16,000 mAh LiPo battery. With this, more than 2 hours of flight time can be
achieved with only 1 gallon (3.8 litre) of gasoline, even with a 9 kg payload [46].
POWER SYSTEM
The power system provides energy to the driving system, pilot system, and even payloads.
Fuel and LiPo batteries are the most commonly used energy sources in UAVs. However, a power
cable has also been utilized to provide a constant power supply. This was introduced by the
Israeli company Skysapience, as part of their HoverMaster series of UAVs to host payloads of up
to 18 kg including CCD/IR cameras, radars, lasers and hyperspectral sensors. A cable serving as
a power supply and wideband data link secures them (fig. 5). A similar method was also used in
CyPhy’s products under the following patents [47,48]. Only last year, the Canadian company
Energy Or Technologies developed a proton exchange membrane (PEM) fuel cell system and
with it achieved the world’s longest multi-rotor UAV flight of 3 hours and 43 minutes [49].
In addition, new charging technologies are coming onto the market. They address the short
battery life issue. One option is an outdoor wireless charging system developed by the German
company Skysense (fig. 6). Alternatively, using solar energy is an environmentally friendly way
to extend the flight time. Berry [26] patented a UAV with a wide fuselage equipped with a solar
turbine and external solar film to extend flight time. An alternative approach is a charging pile
combined with solar energy and wireless charging panels [50].
Fig. 5. Power cable used in
HoverMaster [51]
Fig. 6. Wireless charging system
[52]
CONTROL AND COMMUNICATION
To remotely control a UAV and obtain a First Person View (FPV) from the camera, a
ground station is required. A typical ground control station [53] includes a management unit, a
telemetry module, a user control module, a graphical user interface (GUI), and a wireless data
link subsystem. The wireless data link subsystem is used for remote communication with a UAV.
The telemetry module is coupled to the ground control station and is configured to download
on-board data from the UAV to the ground station and is configured to upload commands from
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the ground station to the UAV. Control is often assisted through the use of a GUI, which
includes a display module that is configured to display an FPV from the UAV’s camera.
Communication frequency between the ground station and a UAV can occur at different
rates. Typical ones are 5.8 GHz, 2.4 GHz, 1.2 GHz or 900 MHz. While 900 MHz has great
obstacle penetration, a setting of 5.8 GHz has good range per watt and is well-suited for open
areas. Most Remote Control (R/C) circles used in UAVs operate at 2.4 GHz [54]. Wi-Fi, Zigbee
and 3G/4G all work around this frequency. Wi-Fi and Zigbee can be used for short distance
communication (usually less than 100 m), while 3G/4G can be used in long-distance
communication but with a short delay.
AUTOPILOT SYSTEM AND OPEN-SOURCE PROJECTS
The autopilot system is the brain of a UAV. A typical autopilot system includes a flight
controller board, a gyroscope, an accelerometer, a barometer, a magnetometer, and a global
positioning system (GPS). In the last few years, autopilot systems have greatly benefitted from
open-source projects (OSPs) and online communities. The OSPs constantly optimize the
autopilot system by integrating different sensors and increasing reliability and stability. This was
particularly notable in 2014, when the Linux Foundation, a non-profit organization dedicated to
accelerating the growth of Linux and collaborative development, announced the founding of the
Dronecode Project, a common, shared open-source platform for UAVs [55]. Founding members
included 3D Robotics, Baidu, Yuneek, DroneDeploy, and Intel, amongst others. Dronecode
includes the APM/ArduPilot UAV software platform and associated code from 3D Robotics. It
also incorporated the partner PX4 project led by Lorenz Meier from ETH [56]. Based on the PX4
and APM projects, 3D Robotics published the Pixhawk autopilot platform at the end of 2014.
This portion of UAV control, especially as it matures into some form of autopilot system, is one
of the most rapidly changing aspects of the technology. A sampling of available OSPs and their
URLs are listed in Table 3 [57].
Table 3. Open-source Projects (adapted from [57])
Project Name
Features
URL
ArduPilot
Has a set of autopilot products including hardware,
firmware, and software and is supported by the Linux
Foundation DroneCode effort
http://ardupilot.c
om/
Openpilot
Focuses on developing two hardware platforms: the
CopterControl (both Original and CC3D) and the
Revolution and is an autopilot platform with a full INS
unit onboard
http://www.open
pilot.org
Paparazzi
Was designed for multi-unit control with autonomous
flight as the primary focus and manual flying as
secondary
https://wiki.papa
razziuav.org
Pixhawk
Supports PX4 and APM flight stacks and is supported by
the Computer Vision and Geometry Lab at ETH Zürich, as
well as the Linux Foundation DroneCode effort
https://pixhawk.
org
MultiWii
Is general-purpose software to control a multi-rotor RC
model; first designed to control a tricopter
http://www.mult
iwii.com
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Dronecode
Is an open source, collaborative project that brings
together existing and future open source UAV projects
under a non-profit structure governed by the Linux
Foundation
https://www.dro
necode.org/
DIY Drones
Has created the world's first "universal autopilots",
ArduPilot Mega (APM) and partakes in the Pixhawk
development.
diydrones.com
Erlerobotics
Is a Linux-based open source project for UAVs and
robots that is combined with Robot Operating System
http://erlerobotic
s.com/
Robot Operating
System (ROS)
Is an open-source, meta-operating system, which provides
services from an operating system, hardware abstraction,
low-level device control, message passing between
processes, and package management
http://www.ros.
org/
COMMERCIALLY AVAILABLE MULTI-ROTOR UAVS
Initially, most companies aimed at penetration in the consumer market and focused on
making UAVs cheaper and more reliable. These efforts are reflected in refinement of the
structural design, improvement of flight control system, and enhancement of camera resolution.
In contrast, in the last two years, with the maturity of the civilian, professional UAV market,
there has been a shift towards greater functionality and the development of more user-friendly
devices. More specialized UAVs have been marketed for sports photography, filmmaking, sites
investigation and agriculture application. In the early years, there were three well-known
consumer UAV manufacturers – Parrot, SZ DJI Technology Co. (DJI), and 3D Robotics (3DR).
After that, further companies entered the market, such as Yuneec, SenseFly and Ehang.
According to UAVGLOBAL, there are currently 240 commercial UAV manufacturers around
the world [58].
Parrot
In 2010, the French company Parrot released their first generation quadrotor, AR.Drone 1.0.
It had an ultrasonic altimeter (thereby providing vertical stabilization up to 6 meters), 15-watt
brushless motors, an 11.1 volt lithium polymer battery, and a forward-facing camera. With a
self-generated Wi-Fi hotspot, the UAV can be remotely controlled by an iOS device and is
equipped to send live video streams to mobile devices [59]. These features make it quite popular.
About half million of units have been sold to date [60]. Their new product Bebop is equipped
with a fish-eye lens camera that can take photos at 90 degree without loss in image quality.
DJI
In 2006, DJI was founded in China and started to design a flight control system for
helicopter like UAVs. In 2013, DJI produced their first, small-sized, ready-to-fly quadrotor, the
Phantom 1.0. With a built-in GPS sensor, it has a position-holding function making it useful for
inspection work. The unit also comes with a GoPro Hero action camera and an adjustable-angle
mount. The overall design provides significant stability in windy environments and improved
remote sensing data acquisition. This product helped DJI enter the market. According to a survey
from AUVSI, DJI now manufactures 65% of the platforms in the United States market [61] and
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70% worldwide [62]. In the last two years, they have shifted their focus to the professional
market and invested in autonomous flight system development. Their latest product, the Phantom
4 combines advanced computer vision and sensing technology and was lunched in March of
2016. The Phantom 4’s obstacle sensing system features two forward-facing optical sensors that
scan for obstacles and automatically directs the aircraft around any impediments [63].
Furthermore, they also designed an octocopter named MG-1 for agriculture applications, a
quadrotor named Inspire for filmmaking [64] ,and a quadrotor platform named Matrice 100 for
research use [65]
3DR
3D Robotics was founded in 2009 in the United State. In 2014, they introduced their first
ready-to-fly, consumer products: IRIS and IRIS+. The products are distinguished by the
inclusion of the Follow Me technology [66], which enables a UAV to follow a moving target and
keep its camera automatically centred on it. In 2015, they lunched a more advanced model,
named Solo, equipped with two on-board computers marketed for both consumer and
professional aerial photography [67]. Later, as strong proponents of open source technology,
3DR announced that they would no longer produce any other legacy models except Solo and
released all of the 3D printable files for the IRIS+ exclusively to the MyMiniFactory community
[68]. With a 3D printed frame, users only need to purchase the electronic components from 3DR,
and they can build their own IRIS+ drone.
OTHERS
In 2015, Lily Robotic introduced their Lily quadrotor, which can be launched by being
thrown into the air. The waterproof body allows flying in the rain or snow, thereby making it
more suitable for sports photographing [69]. In the autonomous flying areana, Intel invested $60
million in the Chinese drone-maker Yuneec who recently released their Yuneec Typhon H UAV,
which is equipped with an Intel RealSense camera and an Intel CPU for obstacle detection and
collision avoidance [70]. For site investment applications, with the requirement of covering a
large survey area, SenseFly launched their fixed wing UAV-ebee that can cover 12 km² in a
single flight [71]. Moreover, the move towards multiple functionality is evidenced by the
German company Microdrones’ md4 series drones with its wide range of remote sensing
accessories including a laser scanner, thermal camera, and miniature, multiple camera arrays
[72].
TYPICAL UAVS IN THE MARKET
Some typical UAV products are demonstrated in Table 2. The lower end of the market
starts a bit over the $200 mark, with a lot of choices between $800 and $1,500. This level of
UAV typically has 4 props with a built-in camera or an electronic gamble with a Gopro camera.
This class of products rarely has extra payload capability. The upper portion of the market starts
at $3000 and more commonly has 6 or 8 props with a carbon fibre, foldable frame and powerful
payload expansion mounts. The payload capability can range up to 5,000g. They are usually
equipped with a professional camera or multifunction cameras and a gamble. The flight time is
still a bottleneck for UAVs. Especially with payloads, it is hard for most consumer-based UAVs
to fly longer than 30 minutes. Flight time is largely independent of cost, but highly dependent
upon payload and self weight.
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Table 4 Typical Commercial Drones
Product
Company
Price
Locale
Flight Time/
Payload
Camera/
Operation Range/
Features
Image
Reference
AR Drone
2.0
Parrot
$249
France
12 min/
100g.
Built-in 720p Cameras
200m/
Supports multiple
controlling devices
[73]
Bebop 2
Parrot
$799
France
22 min/
20g.
Stabilised 1080p Cameras
180° vision HD
2000m/
[74]
Iris+
3D Robotics
$599
US
22 min/
400g.
GoPro Camera/
1000m/
[75]
Solo
3D Robotics
$919
US
25 min/
420g.
GoPro Camera/
805m/
Powered by twin computers
Ballistic parachute system
[76]
HEXO+
Squadrone
System
$999
US
15 min/
200g.
GoPro Camera/
100m/
2-second battery swap
[77]
Phantom 4
DJI
$1,399
China
28 min/
300g.
Built-in1080p Camera, 4K
resolution video/
5000m
Automatically avoid
obstacles.
[78]
Inspire 1
Pro
DJI
$3,899
China
18 min/
1700g.
Built-in1080p Camera, 4K
resolution video/
2000m/
Independent camera
controller
[79]
MATRICE
100
DJI
$3,299
China
40 min/
1000g.
External camera
stabilization gimbal/
5000m/
[65]
QR X900
Walkera
$4,399
China
25 min/
3000g.
External camera
stabilization gimbal/
1000m/
Parechute protection
device
[80]
Typhoon
H
Yuneec
$1,299
China
25 min/
600g.
Built-in1080p Camera/
800m/
Integrated autonomous
flight models
[81]
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Product
Company
Price
Locale
Flight Time/
Payload
Camera/
Operation Range/
Features
Image
Reference
Tornado
H920
Yuneec
$8,000
China
25 min/
5000g.
Gimbal for Panasonic
Lumix GH4/
2000m/
Integrated autonomous
flight models
[82]
MD4-1000
Microdrones
$2,000
Germany
45 min/
1200g.
External camera
stabilization gimbal/
500m
[83]
eXom
SenseFly
44
000
Switzer-
land
22 min/
1200g
Built-in HD camera and
thermal camera/
2000m
[84]
eBee
SenseFly
$12,000
Switzer-
land
50 min/
150g
Sony WX or thermoMAP/
3000m
[71]
Fig. 7. Research areas in the UAV domain (after [85]
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CURRENT UAV RESEARCH
According to a survey by Demir [85], UAV research can be classified into two aspects:
technology and operations. Technology research is related to the development of all UAV
systems, while those in operation address how UAV systems are used and are geared towards
their effective deployment. Fig. 7 shows an overview of the main research areas in the UAV
domain. Here, only the technology part is presented. In this aspect, autonomy is the most active
topic. Because UAVs have 6 degrees of freedom and are moving at a high speed, effective
manual remote control is challenging when working in geometrically complex environments. In
such scenarios, autonomous navigation technologies are a great support to operators. As
mentioned above, UAV companies (e.g. DJI, Inter, and Yuncee) invest significantly in this
function. Academic research groups are also interested in this field. For example, MIT’s group
employed a stereo camera with fixed-wing UAVs to avoid collisions at speeds of 30 Km/h [86],
while Carnegie Mellon University’s group presented an imitation learning strategy for
high-speed UAV autonomous flying through dense forest environments [87]. In that project, they
applied the DAgger (DAtaset aggregation) algorithm [88] to train the flight controller system. By
iteratively mimicking a human expert’s behavior, the controller performance improved. During a
flight test, the UAV avoided more than 680 trees when travelling 3.4km at speeds up to 1.5m/s.
Despite this notable success, to achieve full autonomous navigation, there are significant issues
to be resolved besides collision avoidance. These include path planning and automatic mapping
and scanning. While topics can be studied individually, they are typically addressed in composite
solutions. One major example is the simultaneous localization and mapping (SLAM) problem.
This topic has merged sensor application, obstacle detection, pose estimation, and map
generation. These elements are discussed below.
SLAM
SLAM is widely used in UAV navigation systems for the simultaneous tracking of an UAV and
the updating of the detection information in an unknown environment. SLAM has long been
studied for ground-based robots in two-dimensional (2D) situations and is now being introduced
into the more complex three-dimensional (3D) context required of UAVS. Hardware used in a
SLAM process could be classified into two groups: (1) environment detection sensors and (2)
positional estimation sensors. Environmental detection sensors give the on-board computer
obstacle information about the immediate environment, while positional sensors help to estimate
the UAV’s movement and register it in the same coordinate system (Fig. 8).
OBSTACLE DETECTION SYSTEM
The critical factor for collision avoidance is obstacle perception. Because UAV payloads and
computing abilities are typically quite limited, detection devices to improve obstacle perception
must be lightweight (e.g. monocular camera[89], stereo cameras [86], RGB-D cameras [90],
Light Detection and Ranging (LIDAR) sensor [91], and ultrasonic distance sensors [92]). A
typical UAV object detection system is shown in Fig. 9 and includes two stereo camera pairs
(one pointing forwards, one pointing backwards) and a tilted, continuously rotating, 3D laser
scanner for perceiving the environment in all directions [93]. Depending upon the direction, the
measurement density of the 3D laser scanner varies and has its maximum in a forward-facing
cone. Only a small portion above the UAV’s back is shadowed. In addition, eight ultrasonic
sensors are mounted in a ring around the UAV. For localization and state estimation, a
downward pointing optical flow camera is mounted (in addition to the two stereo camera pairs
and the 3D laser scanner).
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Fig. 8. SLAM Process Steps
Fig. 9. Typical UAV objects detect system setup [93]
POSE ESTIMATION SYSTEM
Sensors included in the autopilot controller, such as a barometer, a magnetometer, GPS
devices, can help locate the absolute position of a UAV on a map system (see section 6.3).
Unlike ground robots that only move in 2D, UAVs move in 3D space and can have 6 degrees of
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freedom. To address this, an inertial measurement unit (IMU), which integrates accelerometers,
gyroscopes, and magnetometers is used to track a UAVs movement in 6 degrees of freedom
(rotational attributes like pitch, roll, and yaw, as well as linear movement up/down, forward/back
and left/right shows in Fig. 8). Those data are used to calculate the relative position of the
vehicle within a mapping system.
MAPPING SYSTEM
Environment and self-position information are processed in the on-board computer or sent
back to a ground station computer. All location and geometric information are presented in a
single coordinate, map system. Obstacles, collision-free areas, and unknown spaces are presented
in this map. Depending upon the processing capabilities and the task requirements, different map
models could be applied (e.g. point cloud, polygonal model, and surface element model). To help
address the limited computing power of a UAV, an octree-based map [94] is widely used in
SLAM-based processing.
An octree-based map represents obstacles in an octree structure – a hierarchical data
structure for spatial subdivision in 3D (fig. 10). In such a data structure, each node in the octree
represents the space contained in a cubic volume, usually called a voxel. This volume is
recursively subdivided into eight sub-volumes until a given minimum voxel size is reached [95].
To accommodate limited UAV computing ability and on-board memory constraints, Droeschel
[96] introduced a multi-resolution octree map into a collision avoidance system. In that work (fig.
11) closer obstacles have higher resolutions and more distance obstacles have lower resolutions
(i.e. represented by bigger voxels).
Fig. 10. Octree Structure Identifying the Location of a Single Data Point
Fig. 11. Multi-resolution Octree Map [96]
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16!
As the position of UAVs is always changing, obstacles will be detected from different
angles and distances. To correlate and align scan results taken from different positions is a
challenge for the mapping process. For this, an alternative data index was adopted by Andreas in
the form of a k-d tree [97] as part of the 6D SLAM process [98]. As explained in detail by
Bentley, a k-d data index is a binary tree that has a node representation for every k-dimensional
point (Fig. 12). In this data index, each node can be considered as generating a borderless plane
that divides the space into two parts, known as half-spaces. In that data structure, if the sensor
finds an obstacle and wants to compare it with previous data, the on-board computer only needs
to search points in some specific branches, not the whole map. This process (commonly referred
to as a nearest neighbour search) can significantly accelerate the SLAM process [98].
Fig.12. K-d Tree in 3D space
CURRENT & FUTURE DEVELOPMENTS
In the current market, users can easily find UAVs with live video and autopilot functions
within a $500 budget. For the professional user, at around $5000 there is a lot of choice, such as
UAVs with 6 or 8 props, a 1-3 kg payload, multiple sensors, and attachment options for detailed
data acquisition. According to the data firm CBinsights, drone startups raised just over $450M in
equity financing across 74 deals in 2015. That’s up more than 300% over 2014 [99]. Those
investments will continue to make civilian UAVs even cheapereasier to use, and more
functional in the coming years. The overall UAV market may be further subdivided by
application areas, as UAV companies try to distinguish themselves with respect to extended
flight time, improved device control for autonomous navigation, and water/wind resistant
capabilities. Developers are also attempting to explore new fields for UAVs by enriching the
payload composition.
In recent years, the hardware technologies supporting UAVs have improved significantly.
Most patents have been in these areas, especially in hardware design and system direction
control, as the software side has been largely dominated by the open source community, where
notable advances have occurred in autopilot system research. Currently, flight time is the major
UAV limitation. In support of this, new power supply schemes are being studied. Further areas
where notable advances are likely are in swarm technology to allow the simultaneous operation
of multiple drones or new UAV designs to improve flight stability and energy efficiency.
!
17!
CONCLUSIONS
Despite the huge and rapidly growing popularity of civilian UAVs, there is not presently a
single reference that shows the current state of the technology. This paper addresses this deficit
by comparing currently available commercial with respect to hardware, software and peripherals.
Considerations for cost, payload, and flying time are also addressed. The present market is spit
into professional and hobby level equipment with the cost break point being around $3,000.
While companies are presently striving for improved navigation and durability (including
water-proofing) there are also significant attempts being made to divide up the market by
developing (or at least marketing) specific UAVs for particular applications. Future UAVs are
likely to show significant advances in payload capacity, flight time, functionality, and enhanced
autonomous flight capabilities.
CONFLICT OF INTEREST
The authors have no commercial interest in any of the products discussed herein.
ACKNOWLEDGMENTS
This project was made possible through the generous support the European Union’s
Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant 642453
and funding through Geological Survey Ireland grant 2015-sc-042
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... • The maximum overrun of the system can be controlled using controllers [28,29]. ...
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Drones, also known as Crewless Aircrafts (CAs), are by far the most multi - level and multi developing technologies of the modern period. This technology has recently found various uses in the transportation area, spanning from traffic monitoring applicability to traffic engineering for overall traffic flow and efficiency improvements. Because of its non-linear characteristics and under-actuated design, the CA seems to be an excellent platform to control systems study. Following a brief overview of the system, the various evolutionary and robust control algorithms were examined, along with their benefits and drawbacks. In this chapter, a mathematical and theoretical model of a CA’s dynamics is derived, using Euler’s and Newton’s laws. The result is a linearized version of the model, from which a linear controller, the Linear Quadratic Regulator (LQR), is generated. Furthermore, the performance of these nonlinear control techniques is compared to that of the LQR. Feedback-linearization controller when implemented in the simulation for the chapter, the results for the same was better than any other algorithm when compared with. The suggested regulatory paradigm of the CA-based monitoring system and analysis study will be the subject of future research, with a particular emphasis on practical applications.
Chapter
This chapter discusses the kinematic models of some wheeled mobile platforms and the aerial four motors helicopter-type vehicles. Such models show that commanding suitable velocities to the robots analyzed, the wheeled omnidirectional, unicycle and car-like robots and the aerial multirotor robot as well, causes velocities in the world frame, meaning that the commanded vehicles can move to any point in the navigation plane or in the navigation 3D space, in the cases of wheeled robots or aerial robots. Therefore, such models can be used to design automatic kinematic controllers, able to guide such robots in their navigation. As such controllers deal with the velocities of the robots ignoring the inertial effects, they are called kinematic controllers, and are responsible to move the vehicles in their working space. The design of such kinematic controllers, however, is not addressed here, but in Chap. 4.
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