Conference PaperPDF Available

Autonomous UAV System Development for Payload Dropping Mission

Authors:
  • Bhimasena Research and Technology
Conference Paper

Autonomous UAV System Development for Payload Dropping Mission

Abstract and Figures

In this paper, power and autonomous system in UAV (Unmanned Aerial Vehicle) for payload dropping mission will be presented. This UAV was designed to participate Kontes Robot Terbang Indonesia 2013 (Indonesia Aerial Robot Contest 2013), payload dropping category, at Institut Teknologi Bandung, Indonesia. In this competition, provided a mission which the UAV has to drop payloads in some target points which is only known when UAV in the flight time. The UAV has to fly autonomously point-to-point while it also search the true point to drop the payloads by camera capturing. By integrating UAV’s system with GCS (Ground Control System), the true point will be known by the operators in the ground control station and the mission can be accomplished.
Content may be subject to copyright.
The Journal of Instrumentation, Automation and Systems
Autonomous UAV System Development for Payload
Dropping Mission
Ghozali S. Hadi, Rivaldy Varianto, Bambang Riyanto T.§, Agus Budiyono¤
Bhimasena Research, Technology, and Development
Department of Aeronautics and Astronautics Engineering, Institut Teknologi Bandung, Indonesia
§Department of Electrical Engineering, Institut Teknologi Bandung, Indonesia
¤Department of Aerospace Engineering, Konkuk University, Korea
AbstractIn this paper, power and autonomous system in UAV
(Unmanned Aerial Vehicle) for payload dropping mission will be
presented. The UAV consists of two main parts, airframe and the
avionics system. There are two important subsystems of avionics
system which will be discussed, power system and autonomous
system. There are three Li-Poly batteries applied in this system to
power the autonomous system. The autonomous system consists of an
Ardupilot Mega v2.4.2 board (autonomous board), a GPS (Global
Positioning System) sensor, attitude sensors, ESC (Electronic Speed
Controller), servos, a brushless motor, a camera, a data transmitter, an
Audio/Video transmitter, and also a Radio RC receiver. This UAV was
designed to participate Kontes Robot Terbang Indonesia 2013
(Indonesia Aerial Robot Contest 2013), payload dropping category, at
Institut Teknologi Bandung, Indonesia. In this competition, provided a
mission which the UAV has to drop payloads in some target points
which is only known when UAV in the flight time. The UAV has to fly
autonomously point-to-point while it also search the true point to drop
the payloads by camera capturing. By integrating UAV’s system with
GCS (Ground Control System), the true point will be known by the
operators in the ground control station and the mission can be
accomplished.
KeywordsAirframe, power system, autonomous system, Kontes
Robot Terbang Indonesia 2013, dropping payloads.
I. INTRODUCTION
OWADAYS, Unmanned Aerial Vehicle (UAV) has been
developed and used for myriad practical purposes, from
civilian tasks [1] into military missions [2]. UAV is widely used
because it is safer and more convenient than manned aerial
vehicle [3]. There are some types of UAVs, which are classified
by various parameters such as weight, engine type, wing
loading, maximum altitude, endurances and ranges. For civilian
tasks, people commonly use multirotor type and fixed wing
type because of easy-manufacturing. In this paper, we want to
present about fixed wing UAV for payload dropping mission.
The fixed wing UAV has one rotor for its propulsion and has
some control surfaces (aileron, rudder, and elevator) to control
movement of the UAV. Fixed wing UAV has some advantages
compared with multirotor type, such as its long endurance and
its ability to carry adequate-heavy payload. Because of the long
endurance and adequate-heavy payload needed for our payload
dropping mission, we chose fixed wing platform for our UAV.
Figure 1 Fixed Wing Unmanned Aerial Vehicle
It is important to make an optimized UAV’s design system
based on the desired mission. The optimization is obtained by
considering the lack and benefit of some UAV’s components
then consider the best trade-off between them.
In this paper, power and autonomous system in UAV for
payload dropping mission will be presented to show how to
integrate UAV’s avionics system (power and autonomous
system) with UAV’s airframe platform. The airframe design of
this UAV will be shown in Section II. In Section III and IV,
autonomous system and power distribution in UAV will be
detailed. Then, system integration (integration between
avionics system and airframe platform) will be presented in
Section V. In Section VI, result experience and analysis will be
presented. Finally, the conclusion of this paper will be shown in
Section VII.
II. AIRFRAME DESIGN
This UAV is named as Aksantara. Aksantara’s airframe is
dominantly made of foam and strengthened by aluminum in
UAV’s wing and tail boom. Foam is chosen because it is not
too heavy and adequate easy-to-manufacture [4]. The
dimension of Aksantara can be seen in Figure 2 and 3. And the
appearance of Aksantara’s airframe can be seen in Figure 4.
Corresponding author: G. S. Hadi (e-mail: ghadi@bhimasena.com).
This paper was submitted on October 23, 2014; revised on November 11,
2014; and accepted on November 17, 2014.
N
The Journal of Instrumentation, Automation and Systems
73
UAV’s movement (pitching, rolling, and yawing) is
adjusted by controlling UAV’s control surface. Aksantara’s
control surface consists of two ailerons (one in each wing side),
one elevator in tail, and two rudders (one in each tail side). In
addition, there are two fixed-wheels in the bottom-rear of
fuselage and one adjusted-wheel in bottom-front. The
adjusted-wheel is controlled parallel with rudder. This
adjusted-wheel is very important when UAV take-off and land.
Figure 2 Dimension of Aksantara’s airframe (top view)
Figure 3 Dimension of Aksantara’s airframe (side view)
Figure 4 Aksantara’s airframe
III. AUTONOMOUS SYSTEM
The main part of autonomous system is autonomous board.
We used Ardupilot Mega v2.4.2 as UAV’s autonomous board.
This board is very important because it will control almost of
the other parts of this system autonomously to accomplish the
UAV’s mission. It is not only responsible with UAV’s
stabilization, control and guidance but also with data
transmission-to-ground station and payload dropping
mechanism. The Ardupilot Mega board will control two servos
to embody payload dropping mechanism. The diagram of
autonomous system can be seen in Figure 5.
Figure 5 Aksantara’s autonomous system diagram
Figure 6 Aksantara’s autonomous system process
74
Figure 5 shows that almost of the avionics system in UAV is
integrated with autonomous board, except camera and video
transmitter. Camera will transmit streaming video data to
ground station independently. Camera will capture the image
while the UAV fly autonomously. When the pilots in ground
station seeing the monitor and find that the UAV is in the right
place to drop the payload, they are allowed to control the
autonomous board from ground station to drop the payload by
controlling the payload dropping servos. Besides, this
autonomous board has failsafe mechanism. This mechanism is
used when either ground station lost data transmission from
UAV or the battery is in low voltage. If one of these condition is
occurred, the UAV will come back to home (place where it start
to fly) autonomously regardless the mission have been
accomplished or not. The flow chart of autonomous system
process is shown in Figure 6.
There are three target places in this competition where we
must drop the payload to accomplish the mission. But, there are
also two fake target places given. That is why we have to find
the true payload dropping target from the streaming video
captured by camera when the UAV fly autonomously. After the
mission has been accomplished, pilot can land the UAV
manually by using remote RC. The only condition allowed to
use manual mode is when the UAV is going to land or take-off.
IV. POWER DISTRIBUTION
There are two important things that making power
distribution topic becomes worthy to be discussed. First is
appropriate voltage level for all electronic components in UAV
and electronic isolation from motor noise. That’s why we have
to distribute the power sources properly in order to solve these
two important issues. Below is the Aksantara’s power
distribution diagram.
Figure 7 Aksantara’s power distribution diagram
A. Voltage Level
We used battery Li-Poly (Lithium Polymer) for Aksantara’s
power sources because Li-Poly battery has light weight and be
able to store large energy in small package [6]. There are three
Li-Poly batteries used for Aksantara’s power source. These are
5500 mAH 4 cells battery, 1500 mAH 3 cells battery, and 2200
mAH 3 cells battery. We chose three LiPoly batteries with
different specification to optimize the UAV’s total weight and
also to comply with voltage level requirement of some
electronic components, such as camera and video transmitter.
We chose 4 cells for brushless motor because this motor need
power in this voltage level. However, the other electronic
components need 5 Volt power source. So, the 3 cells 2200
mAH battery was converted by two LDO (Low-Dropout)
regulators with output voltage ~5 Volt. These two LDOs were
used because of electronic isolation case which will be
discussed then.
B. Electronics Isolation
Electronic isolation is needed to isolate electronic
components from motor brushless, such as direct back EMF
(Electric and Magnetic Field) effect [5], which can impact
power source voltage level obtained by electronic components
whereas every electronic components has voltage level
constraint. Therefore, electronic isolation is implemented in
order to there is no electronic trouble, such as autonomous
board get auto-restart.
As shown in Figure 7, power source for brushless motor is
different with power source for autonomous board. However,
power source for Electronic Speed Control (ESC) which will
control brushless motor speed is taken from autonomous board.
Furthermore, Pulse Width Modulation (PWM) signal which
control ESC output is also triggered by autonomous board. That
is why we implement power separation in autonomous board
and also use two LDOs to convert one 3 cells (11.1 Volt)
battery into two 5 Volt power sources. Because by default,
Ardupilot Mega v2.4.2 has feature which user can choose
whether do power separation between input PWM signal (from
receiver remote RC) and output PWM signal (to servos and
ESC) or not. In this board, there is a diode which can control the
power flow (rectifier) and also a pin jumper. If user place the
jumper, user just need to give power in output PWM signal
side. But if user removes the jumper, user need to give power in
both input PWM signal side and output PWM signal side. And
we chose to remove the jumper because we wanted to do power
separation. Here is the image to illustrate the power separation
in Ardupilot Mega v2.4.2.
Figure 8 Illustration of power separation in Ardupilot Mega v2.4.2
V. SYSTEM INTEGRATION
Firstly, we want to present some of important
components/devices used in this UAV’s project. We used
autonomous board from Arduino, Ardupilot Mega v2.4.2, as
shown in Figure 9. All sensors needed (gyroscope,
accelerometer, magnetometer, and GPS) are included in this
board by default. However, we added external GPS sensor
The Journal of Instrumentation, Automation and Systems
75
instead of using the default GPS sensor because the default
sensor is not adequate good in sensing. We used GPS module
from Mikrokopter, MK GPS v2.1, as shown in Figure 10. This
GPS module use U-BLOX LEA6S as its module receiver and
this GPS module is also completed by additional compass
sensor. By default, Ardupilot Mega board has a data protocol to
send data inter-peripheral named as mavlink protocol which is
basically used UART (Universal Asynchronous Receiver/
Transmitter) communication. So, we chose data transceiver
from RCTimer which has implemented this data protocol. The
picture can be seen in Figure 11. And the last, we used
brushless motor BL2832 from EMAX as shown in Figure 12.
This motor draws current until 69 Ampere for 60 seconds (in
full thrust/speed condition).
Figure 9 Autonomous Board (Ardupilot Mega v2.4.2)
Figure 10 GPS module (MK GPS v2.1) from Mikrokopter
Figure 11 UAV’s data transceiver from RCTimer
Figure 12 Brushless motor (BL2832) from EMAX
All of the avionics components place in airframe should be
managed so it can be efficient in wiring and it can be effective
in function. Furthermore, we also considered every batteries
placement carefully because it could impact in CG (Center of
Gravity) of airframe position. Figure 13 illustrate avionics
components placement in Aksantara’s airframe.
Figure 13 Electronics components placement
Figure 14 System Integration (side view)
Figure 15 System Integration (front view)
76
In ground station, Aksantara’s status was monitored by GUI
(Graphical User Interface) software which is default from
Ardupilot, Mission Planner. Below is an interface example of
Aksantara’s status in Mission Planner.
Figure 16 Main menu (included Attitude status) in Mission
Planner.
Figure 17 Waypoint plan’s interface in Mission Planner.
VI. RESULT AND ANALYSIS
A. Result
We implemented some method/procedure to ensure that
Aksantara was finished as well as we hoped. First, Aksantara’s
airframe was tested by seeking the appropriate CG of
Aksantara. Then, we did flight test to analyze Aksantara’s
performance. But this first flight test was done with manual
flight mode (without autonomous system). And the result was
pretty good. It could also do maneuver-movement adequate
good.
Then, we tested avionics components separately and also
calibrated some sensors. And the last, we did flight test with all
integrated components, including the autonomous system.
When Aksantara started to take-off manually (in manual flight
mode) with full thrust/motor-speed, suddenly we got lost to
control Aksantara with remote RC. Then unfortunately
Aksantara flied with full motor-speed but it was uncontrolled.
Finally Aksantara got crashed. We will discuss the reason
analysis and its proposed solution in the next section.
B. Analysis
The most possible reason why we got lost control is because
of some troubles in autonomous system, such as error/noisy
digital signal. This analysis is obtained because when
Aksantara got crashed autonomous board did not get auto-reset
proven by attitude status in Mission Planner. Then, there are
some possible reasons why error/noisy digital signal are
happened.
First, this trouble can be happened because of not standard
wiring. Therefore, if there is some big vibration, there will be
some wire become not fixed or unconnected unexpectedly. The
only one solution for this case is using the standard wiring
which is very robust in vibration.
Second, this trouble can be happened because of electric and
magnetic field (EMF) induction caused by high current flow.
This assesment comes from high current wire (look at Figure
13) which can distribute current until 96 Ampere when the
motor in full thrust/speed. And Aksantara’s lost control was
happened when it went for take-off and needed full thrust from
motor brushless. There are two possible solutions for this case.
First, by rearranging the electronic components placement so
the autonomous board is not in the high EMF induction area
which makes this board become throuble. Second, by
implementing the Faraday cage to nest high current wire. The
conductor used for Faraday cage should be connected to ground
channel of Aksantara’s avionics system. By using this method,
EMF induction effect can be vanished.
Third, the error/noisy digital signal can be happened because
of direct back EMF effect from motor brushless [5]. Because,
although we had done electronic separation in our flight test, we
only embodied power separation whose ground channel has not
been isolated perfectly. One of the best solutions is using
optocoupler (optical isolator) between autonomous board side
and ESC-plus-motor brushless side. This method is used to be
implemented in signal/data transfer process between
peripherals whose ground channel have to be isolated each
other [7]. Below is the example diagram of optocoupler
implementation.
Figure 18 Diagram of optocoupler implementation.
VII. CONCLUDING REMARKS
Aksantara’s airframe has been ready-to-fly. It had been
verified in the pretty good flight test. Refinement should be
implemented in the Aksantara’s avionics system and the
integration system. This refinement is needed to vanish EMF
induction effect and error/noisy digital data which cause
autonomous board trouble. Improvement in avionics system
can be implemented by adding Faraday cage and optocoupler
isolation. In the other hand, standard wiring and correct
electronic components placement should be applied in the
integration of the avionics system.
The Journal of Instrumentation, Automation and Systems
77
ACKNOWLEDGMENT
This work was partly supported by Edy Sofyan from LAPAN
(National Institute of Aeronautics and Space), Indonesia.
REFERENCES
[1] R. K. Lea, R. Allen and S. L.Merry, “A comparative study of control
techniques for an underwater flight vehicle,International Journal of
Systems Science, Vol. 30, No. 9, pp. 947- 964, 1999. CrossRef
[2] Maddalon, Jeffrey M., Hayhurst, Kelly, J., Koppen, Daniel, M.,
Upchurch, Jason M., Morris, Allan T., Verstynen, and Harry A,
Perspectives on Unmanned Aircraft Classification for Civil
Airworthiness Standards. Virginia: NASA Langley Research Center,
2013
[3] Department of Defense USA, Unmanned Aircraft System Roadmap.
Office of the Secretary of Defense, 2005
[4] Satoshi Suzuki, Takahiro Ishii, Gennai Yanagisawa, Kazuki Tomita, and
Yasutoshi Yokoyama, Multy-body Dynamics Modeling of Fixed-Pitch
Coaxial Rotor Helicopter, Journal of Unmanned System Technology, Vol.
1, No. 1, 2013
[5] Alphonsus and Chan Kai Rui, Unmanned Aerial Vehicle Structure and
Propulsion, B.E Thesis, Departement of Mechanical Engineering,
National University of Singapore, 2012
[6] Jianwen Shao, Direct Back EMF Detection Method for Sensorless
Brushless DC (BLDC) Motor Drives, M.Sc Thesis, Virginia Polytechnic
Institute and the State University, 2003
[7] NASA’s Glenn Research Center, Technology Opportunity Lithium
Polymer Batteries, NASA, 2009
[8] Agilent Technologies Inc, Optocoupler Designer’s Guide, Agilent, 2002.
... This UAV configuration is customization of common double tail boom fixed wing UAV. Figure 1 shows 3D drawing of this UAV in CAD (Computer-Aided Drafting) format. The dimension of this UAV design is illustrated in Figure 2. The illustration of interfacing system design of avionics system of the UAV is described in Figure 3 [5]. From this diagram it can be known that there are two kinds of signal frequencies to handle wireless communication between UAV and GCS. ...
Article
Full-text available
In this paper, design of avionics system and control scenario of small hybrid Vertical Take-Off and Landing (VTOL) UAV will be presented. The UAV configuration is a hybrid-UAV configuration combining feature of rotary-wing UAV and fixed-wing UAV. There are two topics that will be discussed in this paper. Firstly, avionics system of the UAV including its power system and its peripherals interfacing. Secondly, control system design of the UAV including flight scenario when the UAV is on transition mode/stage. Rotary-wing UAV configuration has advantages such as easy-and-stable when take-off and landing. Rotary-wing UAV also does not need special area to do take-off and landing. While Fixed-wing UAV configuration has advantages such as high maneuverability and high endurance. This hybrid UAV is designed to obtain all of these advantages.
Conference Paper
The European Union (EU) regulations require the pilots of Unmanned Aircraft System (UAS) to register and pass a basic theory exam from 2021. Therefore, it is essential to systematically gather information for pilots for utilizing UAS in a safe and meaningful manner especially in Nordic weather conditions. This book chapter describes the Nordic challenges for UAS operations. These can be categorized into two main categories: technological and operational. Based on the extensive literature review and practical experience of the authors, both categories of challenges especially with relevance to the severe Arctic weather conditions are presented. The relevant weather conditions include changing the density of the air, dust and solid particles clouds, extreme light conditions, freezing rain, heavy and gusty wind, heavy clouds, ice fog, rain, mist and fog combinations, rapid temperature changes, snow, storm and hailstorm, temperature crossing 0 C several times within 24 hours, wind shear and whirlwind. Many technical and operational challenges (e.g., weather-related phenomena) are overlapping on both categories and can be mitigated partly by technological advances and partly by operational preparedness. Finally, the future challenges and needs in UAS research are also discussed.
Chapter
Unmanned aerial vehicles have shown great potential in fast shipping and delivery, including delivering emergency support and services to the disaster (natural or manmade) hit areas where manual reach is infeasible. For accurate and effective emergency service delivery at the adverse sites, the UAVs need to fly close to the ground. Due to the low-altitude flight, there may be many stationary obstacles (e.g., trees and buildings) on the path of a UAV. Detecting these obstacles is crucial for successful mission accomplishment and evading crash. The existing obstacle detection methods limit the flying speed of a UAV due to the latency in processing and analysing the in-flight sensed data. To mitigate this, we propose to equip the UAV with the prior information of the obstacles on its trajectory. In case of an obstacle, the UAV slows down to avoid the obstacle; otherwise, it travels with a much higher speed. As experiment, we fed the UAVs with the satellite images from the Google Maps. It is observed that the proposed approach improves the overall flying speed of the UAVs to a great extent.
Article
Full-text available
The study on multiple unmanned aerial vehicles (UAVs) reconnaissance task allocation problem is an important research field, which is significant for both military and civilian applications. This problem has often been considered as a multiple traveling salesman problem where the targets are considered as points. In this paper, we present a novel mathematical model that classifies heterogeneous targets as point targets, line targets and area targets to improve the fidelity of the model. It is a complex combinatorial optimization problem, for which we can hardly get an optimal solution as the scale of the problem expands. A new heuristic algorithm called grouping ant colony optimization algorithm is proposed for this new model. Compared with traditional ant colony algorithm, pheromone is divided into membership pheromone and sequence pheromone corresponding to grouping and permutation characteristics of the model, respectively. Also, negative feedback mechanism is introduced to accelerate convergence speed of the algorithm. The simulation results demonstrate that the new algorithm can consider comprehensively the performance of different UAVs and the characteristic of heterogeneous targets. It outperforms existing methods reported in the literature in terms of optimality of the result, and the advantage gets more obvious with the scale of reconnaissance task allocation problem expanding.
Article
Full-text available
Building construction has developed from the use of primitive tools to that of machinery, with a tendency toward automation. Automation of processes and robotics can improve efficiency, accuracy and safety in construction. On the other hand, structural prefabrication for construction is increasingly being adopted worldwide to enhance productivity and to alleviate the environmental impact of conventional construction processes. The combination and application of automation and prefabrication technologies may therefore introduce new developments to the construction industry. This paper provides a comprehensive review of the use of automation technology for structural prefabrication and construction, including recent developments, challenges and future trends. Five stages following the sequence of construction are proposed: design, construction management, robotic manufacturing, autonomous transportation and automatic structural assembly. The paper concludes that the widespread use of automation technology is preferable to structural prefabrication for construction, and that the design for robotic construction introduced through connection innovations may be beneficial as a means of avoiding complex operations and thus improving the efficiency of robotic assembly processes.
Chapter
This paper firstly present the autopilot architecture of a remotely operated vehicle (ROV), and then introduces the hardware of the underwater ROV. Secondly, it focuses on the autopilot software design of the ROV. Finally, experiments in water are carried out to validate the autopilot functions. The main work of this paper is the re-design and implementation of the proposed autopilot system based on Ardupilot. To maintain the integrity of the original functions of Ardupilot, some new functions are added to design a new flight mode. At the same time, in order to ensure that the original ground control station ‘QGroundControl’ can run simultaneously with the PC control software, modifications of Ardupilot motor library and message parsing methods are formulated. Further more, through the PC control software, it is possible to send control commands such as depth hold and attitude hold to the ROV while the relevant data can be recorded for analysis of control performance.
Chapter
Environmental sensing is the most crucial task that needs to be performed in order to analyze the situation of a region during a disaster. The devices deployed in such regions are responsible for sensing and communication effectively. During a disaster, the operation of these devices may be affected by the environmental conditions and their respective power constraints. Moreover, the mobility of these devices in the network leads to a challenging task to perform sensing and communication in such an environment. The disaster recovery may need different sensor data at various points of time. In such cases, the selectivity of data from different sensors and its dissemination in real time are the most important tasks. In this paper, the proposed algorithm is based on the situation-aware conditional sensing for disaster-prone areas using unmanned aerial vehicles. The technique presented in this paper focuses on the control of way points of the aerial vehicles based on the events detected in the Internet of Things environment.
Article
Full-text available
The paper presents the mathematical model of a quadrotor unmanned aerial vehicle (UAV) and the design of robust Self-Tuning PID controller based on fuzzy logic, which offers several advantages over certain types of conventional control methods, specifically in dealing with highly nonlinear systems and parameter uncertainty. The proposed controller is applied to the inner and outer loop for heading and position trajectory tracking control to handle the external disturbances caused by the variation in the payload weight during the flight period. The results of the numerical simulation using gazebo physics engine simulator and real-time experiment using AR drone 2.0 test bed demonstrate the effectiveness of this intelligent control strategy which can improve the robustness of the whole system and achieve accurate trajectory tracking control, comparing it with the conventional proportional integral derivative (PID).
Article
Purpose – During flight, a small-size autonomous helicopter will suffer external disturbance that is wind gust. Moreover, the small-size helicopter can carries limited payload or battery. Therefore control system of an autonomous helicopter should be able to eliminate external disturbance and optimize energy consumption. The purpose of this paper is to propose a hybrid controller structure to control a small-size autonomous helicopter capable to eliminate external disturbance and optimize energy consumption. The proposed control strategy comprise of two components, a linear component to stabilize the nominal linear system and a discontinuous component to guarantee the robustness. An integral control is included in the system to eliminate steady state error and tracking reference input. Design/methodology/approach – This research started with derived mathematic model of the small-size helicopter that will be controlled. Based on the obtained mathematic model, then design of a hybrid controller to control the autonomous helicopter. The hybrid controller was designed based on optimal controller and sliding mode controller. The optimal controller as main controller is used to stabilize the nominal linear system and a discontinuous component based on sliding mode controller to guarantee the robustness. Findings – Performance of the proposed controller was tested in simulation. The hybrid controller performance was compared with optimal controller performance. The hybrid controller has better performance compared with optimal controller. Results of the simulation shows that the proposed controller has good performance and robust against external disturbances. The proposed controller has better performance in rise time, settling time and overshoot compared with optimal controller response both for step input response and tracking capability. Originality/value – Hybrid controller to control small-size helicopter has not reported yet. In this research new hybrid controller structure for a small size autonomous helicopter was proposed.
Article
Full-text available
In open field operations, such as in the isolated mountainous area, the need of small, compact, and portable surveillance system is inevitable [7]. It is to give the team a surveillance capability while reducing carried-load significantly. This system is a portable mini unmanned aerial vehicle (UAV) with its mobile Ground Control Systems included. Within this paper, the general design processes are presented from determining DRO, initial sizing, aerodynamics analysis, structure, mechanism, backpack design until flight testing to validate the design. Some simple simulations by XFLR5 and complex simulation CFD are also conducted to predict the aerodynamics characteristic of UAV. Outdoor flight testing has been conducted and also still on progress for further results
Article
ABSTRACT Brushlesss dc (BLDC) motors and their drives are penetrating the market of home appliances, HVAC industry, and automotive applications in recent years because of their high efficiency, silent operation, compact form, reliability, and low maintenance. Traditionally, BLDC motors are commutated in six-step pattern with commutation
Article
Unmanned, underwater vehicles have been developed considerably in recent years. Remotely operated vehicles (ROVs) are increasingly used for routine inspection and maintenance tasks but have a range that is limited by the umbilical cable. For long rangeoperations, suchas oceanographicexplorationandsurveying, autonomous under- watervehicles(AUVs)areemergingwhichhaveon-boardpowerandareequippedwith advanced control capabilities to carry out tasks with the minimum of human interven- tion. AUVstypicallyresembletorpedoesinthat mosthavecontrol surfacesandasingle propulsion unit, and must move forwards to manoeuvre. Such vehicles are calledight vehicles. This paper describes techniques which are candidates for control of aight AUV andidenti® escontrollersusedonsomeexistingvehicles. Sinceunderwatervehicle dynamics are nonlinear, fuzzy logic and slidingmode control were felt to have promise forautopilot applicationduetotheirpotential robustness. Followingdevelopment using a comprehensive simulation programme, the controllers were tested using the experi- mental vehicle, Subzero II, and their performance compared with that of a classical linear controller. The relative merits of the methods for practical implementation are discussed.
Verstynen, and Harry A, Perspectives on Unmanned Aircraft Classification for Civil Airworthiness Standards
  • Jeffrey M Maddalon
  • Hayhurst
  • J Kelly
  • Koppen
  • M Daniel
  • Jason M Upchurch
  • Allan T Morris
Maddalon, Jeffrey M., Hayhurst, Kelly, J., Koppen, Daniel, M., Upchurch, Jason M., Morris, Allan T., Verstynen, and Harry A, Perspectives on Unmanned Aircraft Classification for Civil Airworthiness Standards. Virginia: NASA Langley Research Center, 2013
Multy-body Dynamics Modeling of Fixed-Pitch Coaxial Rotor Helicopter
  • Satoshi Suzuki
  • Takahiro Ishii
  • Gennai Yanagisawa
  • Kazuki Tomita
  • Yasutoshi Yokoyama
Satoshi Suzuki, Takahiro Ishii, Gennai Yanagisawa, Kazuki Tomita, and Yasutoshi Yokoyama, Multy-body Dynamics Modeling of Fixed-Pitch Coaxial Rotor Helicopter, Journal of Unmanned System Technology, Vol. 1, No. 1, 2013
Unmanned Aerial Vehicle Structure and Propulsion
  • Chan Kai Rui
Alphonsus and Chan Kai Rui, Unmanned Aerial Vehicle Structure and Propulsion, B.E Thesis, Departement of Mechanical Engineering, National University of Singapore, 2012
  • Nasa 's Glenn Research Center
NASA's Glenn Research Center, Technology Opportunity Lithium Polymer Batteries, NASA, 2009
  • Nasa's Glenn Research
  • Center
NASA's Glenn Research Center, Technology Opportunity Lithium Polymer Batteries, NASA, 2009