International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org
Volume 4, Issue 1, January 2015
ISSN 2319 - 4847
NON GPS NAVIGATION USING
SIMULTANEOUS LOCALIZATION AND
MAPPING IN UAV
RAZEEN RIDHWAN U1,AASISH C2, BHARATHRAJ S3, MOHAMMED AZARUDEEN
JAFFURULLAH4, CYRIL ANTHONY A5
1
M.TECH (AVIONICS), HINDUSTAN UNIVERSITY, CHENNAI, INDIA.
2
M.TECH (AVIONICS), HINDUSTAN UNIVERSITY, CHENNAI, INDIA.
3
M.TECH (AVIONICS), HINDUSTAN UNIVERSITY, CHENNAI, INDIA.
4
M.TECH(MECHANICAL), ADVETI, ABUDHABI, U.A.E.
ABSTRACT
This project is solution for the task of autonomous navigation of a UAV through a completely unknown environment by using
solely a single camera and inertial sensors onboard. Many existing solutions suffer from the problem of drift from the
dependency on a clean GPS signal. The novelty in the here-presented approach is to use a monocular simultaneous localization
and mapping (SLAM) framework to stabilize the vehicle in six degrees of freedom. This way, we overcome the problem of both
the drift and the GPS dependency. The pose estimated by the visual SLAM algorithm is used in a linear optimal controller that
allows us to perform all basic maneuvers Such as hovering, set point and trajectory following, vertical takeoff, and landing. All
Calculations including SLAM and controller are running in real time and online while the UAV is flying. No offline processing
or preprocessing is done. We will show real experiments that demonstrate that the vehicle can fly autonomously in an unknown
and unstructured environment.
Keywords: SLAM based navigation, NON GPS navigation, vision based navigation and optical flow navigation using
KINECT.
ACKNOWLEDGEMENTS
We would like to thank Dr. Dalbir Singh and Wg.Cdr. R.S. Kumar for helping us immensely with this project. We
would have not gone as far as we did without the help of Dr. Dalbir Singh with the optical flow concepts. Even
sacrificing some of his work time to help us, he was a main driving force in our project. We would like to thank R.S.
Kumar for being our mentor and supporting us every step of the way. Even though he was extremely busy, he would
always meet up and arrange time to check up on our progress.
1. INTRODUCTION
This paper will presents flight test results for a navigation system based on SLAM (Simultaneous Localization and
Mapping), a technique which couples targeting and navigation. SLAM provides autonomous systems with a real-time
navigation, mapping and precision target location capability. SLAM does not require external information such as that
provided by GPS or by a priori map data, though if available, can be used to generate more accurate solutions. This
reduces the requirement for GPS and presents an opportunity for using a lower fidelity sensing suite to generate
targeting solutions. An unaided integrated navigation solution with an IMU as the primary sensor input can become
unacceptable within a few minutes so that guidance and targeting are impossible. On the other hand, in the absence of
GPS, a SLAM based navigation solution can constrain the flight vehicle position estimates to within an accuracy that is
suitable for autonomous navigation. Furthermore, SLAM also enables the tracking of targets to an accuracy required to
support third party prosecution. SLAM can be thought of as a hybrid of two well-known problems: tracking and
navigation through localization. In tracking, the position and attitude of the sensing platform is assumed to be known
to a degree of certainty and feature/target locations in the environment are to be determined. In navigation through
localization, the feature/target locations are known to some degree and the objective is to determine the sensing
platform location and attitude. If both the feature/target locations in the environment and the sensing platform location
and attitude are unknown, then the two problems are coupled. The process of solving the coupled problem is known as
SLAM.
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org
Volume 4, Issue 1, January 2015
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2. LITERATURE SURVEY
Several authors have tackled the problem of navigation and control in GPS-denied environments. Researchers have
investigated scanning range sensors for mapping the surrounding environment and use this information for navigation.
Klein and Murray (2007) split the SLAM task into two separate threads: the tracking thread and the mapping thread.
The tracking thread is responsible for the tracking of salient features in the camera image, i.e., it compares the
extracted point features with the stored map and thereby attempts to determine the position of the camera. This is done
with the following steps: first, a simple motion model is applied to predict the new pose of the camera. Then the stored
map points are projected into the camera frame, and corresponding features are searched. This is also often referred to
as the data association. Klein and Murray’s algorithm does not use an EKF-based state estimation and does not
consider any uncertainties, either for the pose of the camera or for the location of the features. The difficulty of
simultaneously mapping features while providing accurate state feedback to the vehicle controller, these steps have
been separated in the past. This method of first-mapping-then-localizing has useful application for vehicles needing to
maintain stability during periodic GPS loss, however does not easily generalize to situations where both these tasks
need to be done simultaneously. Madison et al., (2007) presented an Extended Kalman Filter (EKF) based design where
the vehicle states were estimated along with the inertial locations of features in 3D space. They presented a method in
which a vehicle with a traditional GPS-aided INS estimates the locations of features in ight when GPS was active, and
then use these features for aiding the INS in absence of GPS. Features were selected and tracked using a Lucas-Kanade
feature tracker. Simulation results for a vehicle that momentarily loses GPS were provided. Jacob Willem Langelaan
(2006) used an unscented Kalman filter for simultaneously estimating vehicle states as well as feature locations in
inertial space. A Mahalonobis norm (The Mahalanobis norm is a measure of the distance between a point P and a
distribution D. It is a multi-dimensional generalization of the idea of measuring how many standard deviations away P
is from the mean of D ) was used as a statistical correspondence for data association from frame-to-frame for each
estimated feature. New feature locations were initialized using a projection onto the ground plane. Simulation results
for a UAV navigating through a 2D environment were presented, however no flight test results were reported. Fowers
(2008) implemented a Harris feature detector along with a random sample consensus (RANSAC) algorithm for the
correspondence of features in an FPGA (Field-Programmable Gate Array) processor. Their architecture provided drift
corrections to an INS for stabilizing a quad-rotor vehicle for short-term hover in an indoor flight environment. By
assuming that the vehicle remains relatively level, the RANSAC algorithm was used to provide estimates of the
translation, yaw rotation, and change in scale. RANSAC is a model fitting algorithm where a collection of points are
fitted against a model, and the points are sorted into groups of inliers and outliers. These estimated values were then
used to provide IMU-drift correction measurements. Wu and johson developed (2013) a method for fusing feature
information from a monocular vision sensor in an extended Kalman filter. The approach in those papers relied on
tracking features whose locations were estimated when GPS was active. When GPS was inactive, they demonstrated
that this method ensures bounded hover of a rotorcraft UAV through flight test results. However, this method could not
sustain waypoint based flight without a priori knowledge of feature locations and estimate the position of new feature
points without GPS. The key missing element was an algorithm for safely selecting and removing features in an online
feature-reference database. This is remedied here by creating a real-time reference feature management algorithm
which ranks the features based on how often their predicted locations match measured location. Features that are not
seen consistently over a threshold number of images are dropped and replaced with new features; this enables the UAV
to navigate while autonomously flying between waypoints in a completely GPS-denied unmapped dynamic environment
without knowing feature locations a priori. Jonghyuk Kim and Salah Sukkarieh (2004) used the SLAM augmented
with GPS/INS system for to shows two capabilities (online map building, and simultaneously utilizing the generated
map to bound the errors in the Inertial Navigation System (INS) ) of landmark tracking and mapping using GPS
information, and more importantly, aiding the INS under GPS denied situation. If GPS information is available, the
SLAM integrated system builds a landmark-based map using a GPS/INS solution. If GPS is not available, the
previously and/or newly generated map is used to constrain the INS errors. Simulation results will be presented which
shows that the system can provide reliable and accurate navigation/landmark-map solutions even in a GPS denied
and/or unknown environments, such as urban canyons, indoor, or even underwater. Wu et al., (2013) used V-INS to
sustain prolonged real-world GPS-denied light by presenting a V-INS that is validated through autonomous light-tests
over prolonged closed-loop dynamic operation in both indoor and outdoor GPS denied environments with two rotorcraft
UAS. A monocular V-INS for GPS-denied navigation and real-time closed-loop control of unstable UAVs in both
outdoor and indoor environments is presented, A novel feature database management algorithm is presented, which
ranks features based on a confidence index and removes features if they fall below a dynamic threshold related to the
number of feature correspondences, Flight test results where the navigation solution from the presented V-INS for realtime long-term closed-loop control of unstable resource-constrained rotorcraft platforms are presented in real-world
situations. The algorithms are validated on two classes of rotorcraft UAVs in closed-loop Junho et al.,(2013) presented
a monocular vision based SLAM algorithm method with a particular focus on navigation in riverine environments. The
proposed method has been developed from observing the planarity of the feature locations around the river surface with
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org
Volume 4, Issue 1, January 2015
ISSN 2319 - 4847
the presence of coplanar features and the knowledge of the camera height. The FastSLAM algorithm was then applied
to the coordinate transformation ranging method for localization and mapping. They consider epipolar geometry in
order to perceive the attitude of the camera fixed on the MAV with this information, the range and bearing of the
landmarks on the region around the river surface is calculated. For this a 90o rotation was applied in the roll, pitch, and
yaw directions respectively while recording an image stream. The attitude of the camera can be determined from the
essential matrix. For navigation algorithm authors compensate their attitude estimation results with the actual scale.
Theodore (2006) presented a fight-tested vision aided autonomous landing system for landing at unprepared sites. Their
work comes probably the closest to what has been achieved here, and highlights the key difficulty in performing vision
aided landing: ensuring consistent vision aided navigation solution from fight altitude (e.g. 50m Above Ground Level
(AGL)) to ground (0m AGL). However, they relied on GPS until reaching an altitude of 12 meters AGL, after which
monocular SLAM based pseudo-GPS was used until 2 meters AGL, from where the landing was performed using only
inertial measurements until weight on wheels switch was triggered. In contrast to their work, the vision aided
navigation system presented in our work overcomes the difficulties in using vision aided solution in closed-loop control
to perform landings from hover altitude (50 m AGL) to landing (0 meters AGL). Achtelik et al.,(2009) in their paper
they used LIDAR for alternatively to GPS, successful results have recently been achieved using laser range used an
Hokuyo laser scanner ( LIDAR ) and two-dimensional (2D) SLAM for autonomous navigation in a maze. With their
platform, they won the international competition of MAVs (IMAV), which consisted of entering and exiting from a
maze. In contrast to cameras, laser range finders have the advantage of working in texture-less environments. Although
very appealing, the use of range finders is not optimal because these sensors have a restricted perception distance and
limited field of view (typically only in a plane) and are still heavy for MAVs.We note that both the power consumption
of the sensor itself and the energy to lift its weight have to be taken into account for the system’s energy budget. Thrun
and Michael (2006), in GraphSLAM a graph of robot poses and landmark observations is obtained. To obtain a global
map, landmark observations from multiple poses are refactored into constraints between those poses. GraphSLAM
chooses to explicitly marginalize out landmarks to improve the pose estimates. The resulting graph and associated
matrix are, under most conditions, very sparse and can be efficiently optimized. Of particular relevance to this
dissertation is the addition of the capability of using GPS readings to improve the resulting solutions. The implicit
extremely optimistic white-noise assumption in this work is
3.METHODOLOGY
SLAM is a technique used by mobile robots to create a map of an unknown environment, or update a map of a known environment,
while simultaneously keeping track of its location in this map
3.1Process of SLAM
The process of SLAM algorithm was shown in Figure1. The landmark data is extracted using an onboard camera. The extracted data
is uploaded and associated on the onboard processor. This processor process the data and estimate the state. This state is then
updated to the landmark and thus the landmark is mapped.
Figure 1 SLAM Process
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3.2 System Architecture
We have designed a quad rotor Autonomous Unmanned Aerial Vehicle (SQADRON2) capable of navigating
indoors/outdoors without any external navigation aids like GPS. At the base level, the on-board IMU, the flight
controller and the main processor create a feedback loop to stabilize the quadrotor at an update frequency of ~500Hz.
Further, the realtime visual odometry algorithm estimates the vehicle’s position relative to the local environment, while
an EKF combines these estimates with the IMU outputs to provide accurate state estimates of the position and velocity
at 15Hz.
Figure 2 System Architecture( SLAM using Microsoft kinect ).
Figure 3 Overall System Architecture.
Figure2 and 3 shows the overall system architecture. These pose estimates enable the flight controller to navigate the
quadrotor through the cluttered environment stably. To mitigate cumulative errors from the odometry algorithm, we
use SLAM using both EKF and ICP algorithm to create a global map, ensuring consistent pose estimates. As the robot
navigates it simultaneously builds the map and localizes itself in the map, this way SLAM provides globally consistent
position estimates of the vehicle from the map will be created. It performs pure visual slam using feature matching
algorithms. RGBD SLAM is run as a node in ROS. It requires point cloud data, from the Kinect, and produces a 3D
model of the environment as a point cloud. RGBD SLAM version 1 was found to be very slow and required a large
amount of memory and CPU power ultimately increasing the overall power consumption. To get it to work properly the
robot must move extremely slowly as the frame rate of the point clouds is so low due to the lack of processing power
available.
4. SOFTWARE
4.1. Introduction
Computer software or simply software is any set of machine-readable instructions that directs a computer's processor to
perform specific operations. Computer software contrasts with computer hardware, which is the physical component of
computers. Computer hardware and software require each other and neither can be realistically used without the other.
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Computer software includes computer programs, libraries and their associated documentation. The word software is
also sometimes used in a more narrow sense, meaning application software only. At the lowest level, executable code
consists of machine language instructions specific to an individual processor – typically a central processing
unit (CPU). A machine language consists of groups of binary values signifying processor instructions that change the
state of the computer from its preceding state. Software written in a machine language is known as "machine code".
However, in practice, software is usually written in high-level programming languages that are easier and more
efficient for humans to use (closer to natural language) than machine language High-level languages are translated,
using compilation or interpretation or a combination of the two, into machine language. Software may also be written
in a low-level assembly language, essentially, a vaguely mnemonic representation of a machine language using a
natural language alphabet. Assembly language is translated into machine code using an assembler.
4.2 Python Programming
Python is a powerful programming language. It has efficient high-level data structures and a simple but effective
approach to object-oriented programming. Python’s elegant syntax and dynamic typing, together with its interpreted
nature, make it an ideal language for scripting and rapid application development in many areas on most platforms.
Figure 4. Software Layers [18]
The Python interpreter and the extensive standard library are freely available in source or binary form for all major
platforms from the Python Web site. The Python interpreter is easily extended with new functions and data types
implemented in C or C++ (or other languages callable from C). Python is also suitable as an extension language for
customizable applications
Figure 5 Program Flow for localization and navigation
4.3 Summary
The general purpose programming language is a programming language designee to be used for writing software in a
wide variety of application domains. High level programming language is a programming language with strong
abstraction from the details of the computer. In comparison with low level languages, it may use natural language
elements which are easier to use, Python programming language is used in this project. It is a widely used general
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purpose high level programming language. Moreover it has an advantage of code readability and fewer lines of code
than C. Python supports multiple programing paradigms.
5. MODELING
5.1. Introduction
In 3D computer graphics, 3D modeling is the process of developing a mathematical representation of any threedimensional surface of an object (either inanimate or living) via specialized software. The product is called a 3D model. It can be
displayed as a two-dimensional image through a process called 3D rendering or used in a computer simulation of physical
phenomena. The Squadron 2 structural design was drawn using CATIA. This software offers a solution to shape design, styling,
surfacing workflow and visualization to create, modify, and validate complex innovative shapes from industrial design to Class-A
surfacing with the ICEM surfacing technologies. CATIA supports multiple stages of product design whether started from scratch or
from 2D sketches. CATIA is able to read and produce STEP format files for reverse engineering and surface reuse.
5.2. 3D Modeling of SQADRON 2
3D models represent a 3D object using a collection of points in 3D space, connected by various geometric entities such as triangles,
lines, curved surfaces, etc. Being a collection of data (points and other information), 3D models can be created by
hand, algorithmically (procedural modeling), or scanned. 3D models are widely used anywhere in 3D graphics. Actually, their use
predates the widespread use of 3D graphics on personal computers. Many computer games used pre-rendered images of 3D models
as sprites before computers could render them in real-time. Today, 3D models are used in a wide variety of fields. The engineering
community uses them as designs of new devices, vehicles and structures as well as a host of other uses. We used 3D models as the
basis for physical devices that are built with 3D printers and CNC machines.
Figure 6. Over all structural design of SQADRON 2
Figure 7. Design of Propeller Shield
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Figure 8. SQADRON 2 design with Propeller Shield
Figure 9. SQADRON 2
5.3. Summary
The 3D modeling software enabled the creation of 3D parts, from 3D sketches, composites up to the definition of
mechanical assemblies. The software provides advanced technologies for mechanical surfacing. It provides tools to
complete product definition, including functional tolerances as well as kinematics definition. The software provides a
wide range of applications for tooling design, for both generic tooling and mold & die.
6. Result
The SLAM navigation will be best suited for these situations. is the pattern of apparent motion of objects, surface and
edges in visual scene caused by the relative motion between the observer and scene. The optical flow navigation works
with a down ward facing camera as the major component. A multirotor platform is chosen as the UAV with which the
optical flow navigation is demonstrated. The optical flow navigation will be best suited for these situations. The optical
flow is the pattern of apparent motion of objects, surface, and edges in visual scene caused by the relative motion
between the observer and scene. The UAV consists of an automatic Flight Control System which also includes a central
processing unit. The main processor is fed with a reference image as an input. The onboard downward facing camera
captures the image. The captured image is then fed to the processor. The processor then compares the captured image
with that of the reference image. It is programmed using python programming language to do the sequence of actions.
Python is a powerful programming language. It has efficient high-level data structures and a simple but 10 effective
approach to object oriented programming. Python’s elegant syntax and dynamic typing, together with its interpreted
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nature, make it an ideal language for scripting and rapid application development in many areas on most platforms.
The Python interpreter and the extensive standard library are freely available in source or binary form for all major
platforms from the Python Web site. The Python interpreter is easily extended with new functions and data types
implemented in C or C++ (or other languages callable from C). Python is also suitable as an extension language for
customizable applications. The SURF algorithm is used for image matching. Then the position and x, y coordinates are
calculated and also error estimation is being made. The compensated values are then converted to pulse position
modulation signals and fed to the Flight control system. The flight control system consists of the gyros and
accelerometers and provides the stability for the aircraft. We used Horn and Schunk optical flow method. Brightness
constancy assumption.
Figure 10 3D image matching
7. FUTURE ENHANCEMENTS AND CONCLUSION
7.1. Introduction
The ongoing project is developing the 3D mapping of an indoor environment for the purpose of navigating an aerial
robot under these environments without the use of GPS.
7.2. Future Enhancements
This navigation helps in mapping an area .The current project is to map an indoor area. Completing the analysis works
on the model which can increase the reliability of the system. We are currently working on the endurance of the vehicle
too, in order to map an outdoor area. We would like to develop a private area mapping over areas like our institution.
7.3. Conclusion
In this paper we have presented, the development of a quadrotor Unmanned Aerial Vehicle intended for navigating
cluttered, GPS denied indoor environment. We will describe our solution to this problem using RGBD sensor by
Microsoft Kinect. We have developed a hierarchal suite of algorithms which augments an effective mechanism for
autonomous navigation, planning, localizing and mapping of unknown environment.
REFERENCES
[1] Allen D. Wu, Eric N. Johnson, Michael Kaessz, Frank Dellaertx, and Girish Chowdhary .2013.Autonomous Flight
in GPS-Denied Environments Using Monocular Vision and Inertial Sensors. Journal of Aerospace Information
Systems, Vol. 10, No. 4 (2013), pp. 172-186.
[2] Allen Wu, Girish Chowdhary, Eric N. Johnson, Daniel Magree, Andy Shein. 2013.GPS-Denied Indoor and
Outdoor Monocular Vision Aided Navigation and Control of Unmanned Aircraft. Journal of Field Robotics
Volume 30, Issue 3, pages 415–438, May/June 2013.
[3] Jacob Willem Langelaan. Journal of Guidance Control and Dynamics.2007. State estimation for autonomous flight
in cluttered environments. (Impact Factor: 1.27). 09/2007; 30(5):1414-1426. DOI: 10.2514/1.27770.
[4] Jonghyuk Kim and Salah Sukkarieh.2004.SLAM aided GPS/INS Navigation in GPS Denied and Unknown
Environments . ARC Centre of Excellence for Autonomous Systems, The University of Sydney, Australia.
[5] Junho Yang , Dushyant Rao , Soon-Jo Chung, and Seth Hutchinson. 2013.Monocular Vision based Navigation in
GPS-Denied Riverine Environments. Proceedings of the AIAA Infotech@ Aerospace Conference, St. Louis,
MO,Aug 28, 2013.
[6] Klein, G.,and Murray, D.2007. Parallel tracking and mapping for small AR workspaces. ACM IEEE and
International Symposium on Mixed and Augmented Reality.
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[7] Lynne E. Parker, Kingsley Fregene, Yi Guo and Raj Madhavan.2002. Distributed Heterogeneous Sensing for
Outdoor Multi-Robot Localization, Mapping, and Path Planning. Published in Springer Netherlands,Multi-Robot
Systems: From Swarms to Intelligent Automata 2002, pp 21-30
[8] Richard W. Madison, Gregory L. Andrews, Paul A. DeBitetto, Scott A. Rasmussen, and Matthew S. Bottkol.2007.
Vision-Aided Navigation for Small UAVs in GPS-Challenged Environments. Presented at InfoTech at aerospace
conference Rohnert Park, CA.
[9] Markus Achtelik, Abraham Bachrach, Ruijie He, Samuel Prentice and Nicholas Roy.2009.Stereo Vision and Laser
Odometry for Autonomous Helicopters in GPS-Denied Indoor Environments - Technische Universit at M¨unchen,
Germany Massachusetts Institute of Technology, Cambridge, MA, USA.
[10] Spencer G. Fowers.2008.Stabilization and Control of a Quad-Rotor Micro-UAV Using Vision Sensors. All Theses
and Dissertations. Paper 1375.
[11] Theodore, C., Rowley, D., Ansar, A., Matthies, L., Goldberg, S., Hubbard, D., and Whalley, M.2006.Flight trials
of a rotorcraft unmanned aerial vehicle landing autonomously at unprepared sites.
[12] Thrun and Michael Montemerlo Sebastian.2006.The GraphSLAM Algorithm with Applications to Large-Scale
Mapping of Urban Structures. The International Journal of Robotics Research May 2006 vol. 25 no. 5-6 403-429
[13] http://users.cecs.anu.edu.au/~Jonghyuk.Kim/Research_SLAM.htm (Jon Kim’s world of Autonomous Navigation
and Mapping)
[14] http://www2.mae.ufl.edu/~rick/rick_pro/slam/
[15] http://robotosha.ru/robotics/structured-light-kinect.html
[16] http://www.zonawired.com/microsoft-muestra-el-interior-de-kinect-4109/
[17] http://robotica.unileon.es/mediawiki/index.php/PCL/OpenNI_tutorial_2:_Cloud_processing_%28basic%29
[18] http://en.wikipedia.org/wiki/Software
AUTHORS
RAZEEN RIDHWAN received the B.E. degrees in Aeronautical Engineering from Mohamed
Sathak Engineering college, india in 2012,. During 2008-2012, and now he is doing M.Tech in
avionics in Hindustan University,Chennai, India. He and his team called as TEAM
RECONNAISSANCE participate and won the most innovative design award for his team’s
invention UAV called as SQADRON2 in INTERNATIONAL ARIEAL ROBOTIC COMPETITION
( IARC ) MISSION7 held in china 2014.
AASISH Pursed his BE Aeronautical engineering from HITS and currently doing M.Tech
avionics in this same institution. AASISH is freak in UAV’s and DRONES, having done a few
projects in UAV’s and having many flying models. He is 2013 competitor of IARC and SAE
internationals and He and his team called as TEAM RECONNAISSANCE participate and won the
most innovative design award for his team’s invention UAV called as SQADRON2 in
INTERNATIONAL ARIEAL ROBOTIC COMPETITION ( IARC ) MISSION7 held in china
2014.
CO-AUTHORS
BHARATH RAJ, This AERO SPACE engineer currently doing his M.Tech avionics engineering
in HITS. Attending a number of workshops, seminars and group activities, he has been trying to
master the skill of aircraft design. He has joined the TEAM RECONNAISSANCE with IARC
Mission7, 2014. Currently this team developing their SQADRON2 as multi-application UAV for
both civil and military purpose.
MOHAMMED AZARUDEEN, this aeronautical engineer having a great experience in teaching
and research area. He designed and flied a aircraft names as SPITE fire along with his students
team and having great interest in avionics and propulsion area. Currently he is working in
ADVETI, ABUDHABI, U.A.E.
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