DTU 2013

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Delhi Technological University 1

Delhi Technological University

Student Unmanned Aerial Systems

Competition 2013 Journal Paper

Figure 1: Aarush-M

AbstractAarush-M is a twin-boom, inverted V-tail UAS designed for delivering situational awareness in a disaster

struck area. Command and control over UAS is done via a 2.4 GHz radio link, while the intelligence

gathered is transmitted over a 5 GHz link. A highly modular and portable system, Aarush-M can be flight

ready in less than 30 minutes, providing an endurance of 20 minutes. This paper presents requirementsanalysis of the UAS followed by the design description. Flight testing and evaluation results are also

presented which validate the performance parameters .The final section elaborates the safety measures

adopted by the team to ensure safety of the personnel and UAS at all times. Having successfully

conducted two dry-runs of the competition mission, team UAS DTU is confident that Aarush-M will be

able to support the US Marines in their humanitarian relief and security mission in the earthquake struck

Caribbean island.

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Table of Contents

Abstract .................................................................................................................................................. 1

1. Introduction .......................... .......................... ......................... .......................... ......................... .... 3

2. Systems Engineering Approach ........................ ......................... .......................... ......................... .... 3

2.1. Mission Analysis and Requirements Specification ....................... .......................... .................... 32.2. Design Rationale ......................... ......................... ........................... ......................... ................ 3

2.2.1. Intelligence Gathering Payload .......................... .......................... ......................... ............ 4

2.2.2. Guidance and Navigation ........................... ......................... .......................... .................... 5

2.2.3. Airframe .......................... ......................... .......................... ......................... ..................... 5

3. UAS Design Description............................ ......................... .......................... ......................... ............ 6

3.1. Air Vehicle .................................................................................................................................... 7

3.1.1. Conceptual & Preliminary Design ........................... .......................... .......................... ....... 7

3.1.2. Wing analysis and configuration selection .......................... .......................... .................... 7

3.1.3. Fabrication and Developmental Tests ......................... ......................... .......................... ... 8

3.1.4. Propulsion System ....................... .......................... ......................... ........................... ....... 9

3.1.5. Power System Design and Layout of Avionics.................. ......................... ....................... 10

3.2. Payload .......................... .......................... ......................... .......................... ......................... .. 11

3.2.1. Imagery System Payload ....................... .......................... ......................... ....................... 11

3.2.2. SRIC System Payload ......................... .......................... ......................... ......................... .. 11

3.3. Data Processing ........................... ......................... ........................... ......................... .............. 11

3.3.1 Image Processing ......................... .......................... ......................... ........................... ..... 11

3.3.2. SRIC Data Acquisition.................................... ........................... ......................... .............. 14

3.4. Communications ......................... ......................... ........................... ......................... .............. 14

3.5. Ground Control Station ....................... .......................... ......................... ........................... ..... 14

3.6. Mission Planning ......................... ......................... ........................... ......................... .............. 15

4. Flight Testing and Evaluation Results ........................ ......................... .......................... .................. 15

4.1. Navigation Performance ............................................................................................................. 15

4.2. Payload Performance .......................... .......................... ......................... ........................... ..... 16

4.2.1. Imagery ........................... ......................... .......................... ......................... ................... 16

4.2.2. SRIC ........................ .......................... ......................... .......................... ........................... 18

5. Safety ....................... ......................... .......................... .......................... .......................... .............. 18

5.3. Safety in design of the UAS.............. ......................... .......................... ........................... ......... 18

5.4. Safety in mission execution/operation the UAS .......................... .......................... .................. 19

5.5. Failure Mode Effect Analysis ........................ .......................... ......................... ....................... 20

6. Acknowledgements ………………….…………………………………….……………………………………………………………..21

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1. Introduction

The Unmanned Aerial Systems team at Delhi Technological University is proud to present Aarush-M to

compete at the eleventh annual SUAS competition. The team has been participating in the Student

Unmanned Aerial Systems SUAS Competition since 2009 and has gained valuable experience in design,

development, and operation of a UAS. The team comprises of undergraduate students from diverse

engineering backgrounds. The preparation for the competition started in October 2012 and after 4.5

hours of flight testing till May 25, the team is confident that Aarush-M will complete the mission safely

and successfully.

2. Systems Engineering Approach

Taking cue from the judge’s comments from last year , this year, the team’s approach to the competition

has been mission-oriented, rather than technology-oriented. This section describes the systems

engineering paradigm followed by the team.

2.1.

Mission Analysis and Requirements Specification

The mission clearly entails objectives and thresholds for Key Performance Parameters. To score

maximum points, the performance with regard to each parameter should be close to the objectives.Therefore, the team defined a set of KPPs given in Table 1 below with modified thresholds and

objectives based on their importance and likelihood. The expected performance during competition is

highlighted in green color in the table.

S. No. Parameter Threshold Objective

1. Navigation Dynamic Waypoint Dynamic Search Area

2. Launch/Recovery Automatic Launch Automatic Launch and

Recovery

3. Imagery All Characteristics

(3 Autonomously )

All Characteristics

(All Autonomously)

4. Target Location Within 250 feet Within 50 feet

5. SRIC Data Acquisition Manually Autonomously

6. Mission Completion

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dictated the performance of the overall system:

1.

Intelligence Gathering Payload (IGP)

2.

Guidance , Navigation and Control System (GNC)

3.

Communications

4.

Airframe

For each of the design elements, anomalies in the previous year’s competition entry were analyzed and

the goal of the development process was to mitigate these in the new UAS. A variety of options for each

design element were considered, and the option that fulfilled its own requirements, while not

conflicting with the requirements of the other elements was chosen.

2.2.1.

Intelligence Gathering Payload

This design element is considered the most important from the competition’s perspective. Gathering

situational awareness is the foremost requirement of the competition, ergo the team locked down this

subsystem in the early stages of the design. This system also dictated the “payload fraction” and

“payload aperture” requirements of the airframe design team, thereby making the IGP the most critical

design element. The IGP consists of two functionally different elements:

Imagery System

Our previous year’s imagery system comprised of a Canon G10 for capturing images, which after

rigorous testing, was found incapable of giving the required image quality, especially for character

recognition. After analysing the imagery system requirements, a DSLR camera was sought as a suitable

upgrade. All DSLR cameras supported by libgphoto2 library were listed and further shortlisting was

done based on camera properties, weight, camera availability and budget. Various tests inside

laboratory and during test flights revealed that Canon 500D was able to capture images at a higher rate

as compared to Canon G10. This ensured that UAS covers entire search area by providing significant

overlap of land between consecutive images.

Parameter Canon

G10

Canon EOS

500D

Nikon

D5100

Comments

Resolution

(MP)

14.7 15.1 16.2 12-16 MP was found to be optimal to meet

requirements.

Sensor size

(mm)

7.44 X

5.58

22.3 X 14.9 23.6 X 15.7 Larger sensor captures more light and gives

better quality image.

Shutter

Speed(s)

1/4000 to

15

1/4000 to 30 1/4000 to

30

Fast shutter speed is preferable.

ISO 80 - 1600 100 –3200 100 – 6400 High ISO crucial in low light conditions

Weight 14.15 oz. 38.51 oz. 41.58 oz. Canon 500D is lighter among other DSLRs

Price (USD) 509/- 691/- 619/- Prices nearly equal.

Availability Low High Moderate In Indian markets.

(* green color highly favorable *yellow color moderately favorable * red color not favorable)

Table 2: Comparison between Canon G10, Canon EOS 500D and Nikon D5100

A B: B derives requirements from AAirframe

GNC IGP

Communications

Figure 2: Relationship between the requirements of sub-systems

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SRIC

It was decided that gathering data from the SRIC would be accomplished using a wireless network

adapter because of the simplicity of the approach and minimal weight addition. The selection of the

network adapter was done by limiting the physical size and weight of the adapter and connectivity, as

shown in Table 3:

Table 3: SRIC Requirements

Previous year’s UAS failed to connect to the SRIC because of the large turning radius of the vehicle. To

improve the probability of data acquisition from SRIC unit, the UAS was deemed to have a lower orbit

radius and higher communication range.

2.2.2.

Guidance and Navigation

The single component affecting the performance of the navigation system is the autopilot. The teamsurveyed different COTS (Commercial-Off-The-Shelf) and open source autopilots. The team’s previous

year’s UAS used Piccolo II autopilot from Cloud Cap Technology, which gave satisfactory performance.

Piccolo II, initially served as a reference autopilot used to compare other, low-cost alternatives. The

main contender to Piccolo II in the open source domain was ArduPilot Mega 2.5, which was the team’s

initial choice. However, preliminary flight testing revealed serious design limitations in the APM 2.5. The

performance of the APM, even after hours of tuning, was nowhere near the ballpark required for the

competition and hence the APM 2.5 was replaced by the Piccolo II as the GNC unit.

2.2.3.

Airframe

The team had previously been using a commercial, off the shelf airframe - Sig Rascal 110 that supported

a payload of 2 lbs..However, Sig Rascal was deemed unsuitable for this year’s mission due to the

increased payload weight, low wind tolerance and smaller turn radius that was demanded. The teamthus decided to develop a custom airframe to meet the unique requirements of the system.

2.2.3.1.

Design Objectives

According to the requirements analysis of the aircraft, a Statement of Objectives (SOO) was made which

set the basis of the aerodynamic and mechanical design. Table 4 lists the SOO for Aarush-M. The

complete design description of the airframe is given in UAS Design section.

Parameter Objective Threshold

Gross Take-off Weight (GTOW) < 25 lbs. < 55 lbs.

Endurance > 150 mins >30 mins

Payload >13.2 lbs. >10 lbs.

Take off Distance

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3. UAS Design DescriptionFigure 3: System Overview

2.4 GHz5.8 GHz

2.4 GHz

AARUSH-M

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3.1.

Air Vehicle

Team UAS DTU chose to develop a custom airframe with a wingspan of 122” and having an empty

weight of 32 lbs.. The airframe has been designed and fabricated by undergraduate mechanical

engineering students of the team. It is powered by Hacker A80-8 electric motor with a 22x8 propeller. A

single axis gimbal for the camera has been integrated with the system, to provide +/- 45 degree roll

compensation.

Considering the time constraints, the prototyping of the airframe was broken into three phases for rapid

development:

i)

Conceptual and Preliminary Design

ii)

Fabrication and Developmental Tests

iii)

Flight Testing and Evaluation

3.1.1.

Conceptual & Preliminary Design

A preliminary weight estimate was deduced from

the statement of objectives and data gathered

about other UAS belonging to the same class asAarush-M. MATLAB was used for all the theoretical

analysis. Various design specifications of different

small class UAS (10 – 100 lbs.) were studied and this

statistical data was used to estimate the weight

using regression analysis. This gave a good initial

estimation of 35 lbs.. Once the weight was

determined, the SOO and four parameters namely

stall speed, take-off distance, landing distance,

turning radius were used to construct a constraint analysis graph. A design space for the airframe which

would meet all the threshold requirements was obtained as can be seen in the Figure 4. The power

loading vs. wing loading plot depicts infinite number of points which satisfy the design requirements;

ergo it was difficult to choose an optimum value. Few points for low power loading and wing loading

were selected and compared on the basis of overall scoring with weightage assigned to different

parameters. The highest scoring point in the design space was selected.

Table 5: Parameters Weightage

Since practical outcome and performance always deviate from the theoretical analysis and estimation,

sensitivity charts for GTOW vs. Payload, Endurance were prepared. This gave an opportunity to assess

the changes in performance with the changes in design value.

Output of Conceptual Design–

Specifications of the Air Vehicle

3.1.2.

Wing analysis and configuration selection

A variety of low speed, high lift airfoils were analyzed with the help of XFLR5 and their drag polars and

GTOW Endurance Payload Take off Run Landing Distance Min. Control Speed

35% 20% 25% 5% 5% 10%

GTOW 35 lbs. Wing Span 122 inches

Power required 3.8 hp Wing Area 11.02 sq ft.

Take-off Distance 110 ft. Payload 7 lbs.

Landing Distance 100 ft. Endurance 20 mins

Figure 4: Power Loading vs. Wing Loading

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lift curves were studied. Simulation for wing analysis was performed based on various computational

models (Lifting Line Theory, Vortex Lattice Method). The result thus obtained concluded the wing

design.

Propulsion configurations such as pusher, tractor, twin-engine were considered as prospects for the

design. Propeller efficiency, vibration isolation, flexibility in tail, manufacturability & weight were the key

parameters which were used to discern these configurations. Pusher configuration with twin boom

inverted V-tail was chosen for being lighter than H-tail. Besides being lighter, inverted V-tail also gives

an advantage of proverse yaw which increases the wind and gust tolerance of the UAS. The pusher

configuration provides a larger field of view for the camera and better vibration isolation. Such a

configuration also allowed the avionics system to be easily accessible. The wings have been designed for

high turning rate and tolerate a structural load of 6 Gs. Figure 6 shows the finalized assembly of the

airframe

Simulations were done to diagnose and fix the problems in flight characteristics and dynamic stability of

the airframe. The stability and control analyses were performed in AVL and a full 6 DoF simulation was

done in X-Plane. The two simulations concurred, with a static margin of +10% giving a satisfactory result,

which was chosen for the airframe.

3.1.3.

Fabrication and Developmental Tests

One of the ancillary objectives of the team was to develop a robust aerial

platform fit for indigenous research, besides performing in SUAS ‘13. The

fabrication process for the new airframe was carried out completely in the

UAS-DTU lab at DTU. The fuselage features a monocoque shell design

composed of carbon fiber/epoxy sandwiching balsa sheet for additional

stiffness. Sandwiched laminates of carbon fiber, glass fiber and balsa sheet

were made for testing and experimentally determining their strengths

which would be further used for wing and tail skins. As a result 200 GSM

glass fiber (45 degrees) and balsa sheet were used. The 45 degree

orientation provides much greater load transfer and shear strength. Wing

spar was constructed according to the structural calculations which gave

the load and the bending moment along the spar. Unidirectional CF strip,

400 GSM CF and balsa wood was used to build the spar. It was subjected to

cantilever destructive test failing at 168 lbs. where as it was designed for

124 lbs. with a factor of safety 2, thereby passing the test with a good

margin. The design features twin CF booms and solid spring CF landing gear. Landing gear, wing & tail

skins were manufactured using CNC cut medium density fiber molds. The wing assembly consists of two

Figure 6: Final CADFigure 5: Wing Analysis

Figure 7: Load vs. Wingspan

Plot

Figure 8: Wing Spar

destructive test

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Figure 12: Hacker A80-8 Brushless DC motor Figure 13: Aarush-M at Karnal Airport

outboard sections and one mid-section. To reduce the time in assembly and easy replacement standard

bolts of 4 and 5 mm have been used.

3.1.4.

Propulsion System

The team studied two options – a two stroke engine and an electric motor, to meet the power

requirement of 3.5 HP. A comparison chart was prepared, based on prior experience, as shown in Table

6, where green color indicates a favorable condition and red indicates an unfavorable condition.

A two stroke 50 cc DA engine was tested, but encounteredseveral mid-air engine failures leading to emergency

landings. The reliability of the engine was not satisfactory,

especially at elevated temperatures. Operational factors

such as maintenance, troubleshooting etc. deemed the

engine unfit for operation with Aarush-M. However, these

risks were mitigated by an electric motor which provided

equivalent thrust and higher reliability, low acoustic

signature and almost no maintenance.

MotoCalc was used to compare different motors and propellers. A brushless DC motor – Hacker A80-8

powered by three 10S 5000mah Lithium Polymer batteries with a propeller of 22x8 was selected. This

propulsion system provided an endurance of 20 minutes under static conditions. The result was inaccord with the time required to complete the mission.

Table 6: Propulsion System Selection

Parameter Two Stroke

Engine

Electric

Motor

Reliability

Vibration

Endurance

Maintenance

Weight

Figure 9: Fuselage Bottom Skin Figure 10: Mold Pattern Figure 11: Left Wing

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3.1.5. Power System Design and Layout of Avionics

The power sources were selected on the basis of their size, weight and energy density. Lithium Polymer

chemistry was chosen because of its high discharge capacity and high energy density. The battery

capacity was optimized recursively with flight tests so as to save weight. The power of the control

surfaces actuators was kept separate from the avionics system. A switch board was placed under the

avionics hatch to allow selective powering of components during ground testing.

Table 7 shows the power requirements chart which was prepared for battery selection once the on-

board components were specified:

Avionics Component Mission Ampere Hours

(30 minute flight time)

Power Source Factor Of

Safety

Piccolo II Autopilot System 600 mAh

14.8V 3900 mAh Li-Poly 1.9PandaBoard 350 mAh

DLink Network Switch 200 mAh

SRIC Wi-Fi Router 300 mAh

5 Ghz Wireless Router 600 mAh

Canon EOD 500 DSLR 600 mAh 7.4 Wh 1000 mAh Li-ion 1.67

Actuator servos 1350 mAh 7.4 V 2600mAh Li-Poly 2

Table 7: Power Requirement Chart

Separating the control surface power and avionics power sources had twofold advantages:

Increased reliability of aircraft control: In case of avionics power failure, the aircraft control

systems shall remain active. This increases the reliability of the UAS as a whole.

Eliminating loading effects at servos: Isolating the power at servos precludes the dropping of

voltage at their input below their operating point i.e. 4.8 V. It was been empirically ascertained

that running the avionics and control servos simultaneously from the same 5V source resulted in

loading effects which may lead to terminal voltage at servos dropping below their operating

point i.e. 4.8 V.

Figure 14 Layout of avionics on-board Aarush-M

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Figure 15: Onboard Imagery Peripherals

3.2.

Payload

3.2.1. Imagery System Payload

The team opted for a user-centred approach for the effective operation of imagery system during

mission. The imagery system is designed to require minimum human intervention and deliver minimum

false positives. Several use cases were designed to test out the individual features of the software and

hardware system, and various test flights were conducted to simulate the mission which helped inidentifying bugs and bottlenecks. It was observed during flight tests that aircraft banked as much as 40

degrees. Hence, the Canon EOS 500D is housed inside a gimbal which is roll compensated up to +/-45

degrees. The competition objectives also require imagery system to be capable of analysing the off-

centre target which could be up to 250ft. cross-range. The gimbal is capable of being controlled via a

joystick at the ground station when put into manual mode, to accomplish this objective.

3.2.2.

SRIC System Payload

Data from SRIC is accessed via network adapter that transmits the file data via the Imagery link itself.

The Imagery router is used for the SRIC data downlink because the amount of data transferred from the

SRIC is a) intermittent and b) small enough to not hinder any pending Imagery data transfer for more

than a few seconds. These assumptions were well justified when the setup was tested in the lab and

during flights. The test procedures are described under the Testing and Evaluation section of this paper.

3.3. Data Processing

3.3.1

Image Processing

The mission objectives require the imagery system to perform Automatic Detection/Cueing,

Figure 16: SRIC Information Flow Diagram

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Classification, or Identification (ADCCI) on acquired aerial images in real time. The data processing unit

was, therefore designed to be reliable, efficient and fast. Rigorous testing during test flights revealed

more than eighty five per cent success rate of the entire sub-system.

a. Image Acquisition: The on-board computer runs a headless version of Ubuntu 11.04. It controls

the camera parameters such as aperture, shutter speed, focus, and image quality etc. using

libgphoto C library. The code running on Pandaboard on-board computer captures the images everythree seconds to provide optimum overlap. Excess overlap is avoided to reduce computational

overhead. As soon as the image is captured, the GPS information is stored in the image metadata as

exif tags. These images are simultaneously transmitted to the Ground Station using a secured Wi-Fi

link created by Groove Routers. Image transfer takes about 2-3 seconds which is equal to the time

required to capture one image. This time interval has proven to be sufficient for real-time image

processing within given mission time.

b. Graphical User Interface (GUI): The GUI was developed in C++ using QT library. The primary

objective while designing the GUI was to reduce mission execution time by making the compilation

of target data sheet easy for the imagery operator. The need to increase the speed of the GUI was

catered by running few small processes that can run independently on separate threads. The visible

components on the GUI are divided such that all data being processed is displayed on one screen

while all processed target data is displayed on the other. This separates the active target-related

data from diagnostic information which is not used during normal operation. The GUI also lets the

administrator communicate directly with the on-board computer. It stores all processed targets in a

SQL database common to all users and is capable of generating a text file for submission in

accordance with the competition’s requirement. Screenshots of GUI are shown in Figure 17:

c. Image Analysis:

The image processing code for autonomous target classification and identification was written in

C++ using OpenCV, an open source image processing library. To process about three hundred

images in the allotted mission time, it is imperative for the image processing software to be fast andaccurate. Thus, a laptop with NVIDIA Graphics Processing Unit is used to improve the image

processing rate. The image processing technique has been described in the flow chart shown in

Figure 18:

Screen 2: Contains data being processed

or the active dataScreen 1: Contains Processed Target Data and

diagnostic informationFigure 17: Image Processing

GUI

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Segmentation: Segmentation was found to be a crucial part for autonomous processing. A

frequency tuned approach of segmentation to extract salient objects is used to segment

targets from the images. The result of segmentation is a gray scale image in which salient

objects (targets in our case) appear whiter. A graph cut based technique is then used toextract targets from its background.

Color Recognition: To recognize the colors, a histogram of colors for the target is generated

using the Hue values from HSV color space. The highest peak of this histogram gives the

shape color while the second highest peak gives the color of alphabet.

Shape Recognition: Previous year’s system used ray tracing technique to identify shapes of

target. The technique was found to be highly reliable and was improved for distorted shapes

also. Once correctly segmented, the unit can now recognize various polygonal and non-

Original Image Texture of ground flattened

using mean shift filtering

Saliency Map of Image

Extracted Target Using MaskMask created using Graph

Cut Segmentation

Histogram of color distribution in

extracted target for color

recognition

Distance Theta curve for

Shape Recognition

Letter extracted from Image for

recognition based on Eigen Space

Figure 18: Imagery Analysis Flowchart

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polygonal images with 78% accuracy.

Letter Recognition: Letters are first segmented from the target using graph cut approach.

This is done using the method of scale and rotation invariant letter recognition based on

Eigen spaces. This letter recognition module was trained by an artificial dataset. The

implementation resulted in a success rate of less than fifty percent, with certain characters

being recognized only if they were well defined in the image.

3.3.2. SRIC Data Acquisition

Data acquisition from the SRIC required manual intervention from the payload operator to access the

file. This was one of the reasons why the team couldn’t access the file last year. After selecting the

payload required for this task, it was decided to automate the process by writing scripts for both

Windows (batch file) and Linux (shell script), which allowed us to perform this task from either the

Mission Control Centre (MCC), or the Information Gathering Station (IGS). The script, when given the

required parameters of the SRIC’s remote laptop and router e.g. IP Address, Username, Password etc.,

tries persistently to connect to the FTP server running on the remote laptop. Once a connection is

established, it navigates to the specified directory, extracts the text file, and stops execution upon

successful file transfer.

3.4. Communications

There are three communication channels between the air vehicle and the ground station:

Manual R/C control

Telemetry downlink and uplink

Imagery downlink

The manual R/C control is the most critical link and utilizes a 2.4 GHz frequency hopping spread

spectrum transmitter receiver to ensure a robust link, and allow manual override at any time. Such a

modulation technique provides superior noise immunity as compared to FM/AM transmitters.

The telemetry downlink and uplink is done via 2.4 GHz Microhard transceivers that are part of theautopilot package. The frequency was chosen because the other option of utilizing the 900MHz band is

not possible without licensing in India.

The payload connectivity requires large bandwidth to keep latencies to a minimum. As a result, a 5.8GHz

wireless router is used to communicate with the air vehicle. The TCP/IP protocol of the router ensures

that the transmitted packets are delivered at the ground station.

3.5.

Ground Control Station

The PGS setup makes the gathered intelligence easily accessible to the judges and the operators. A

portable ground station has reduced our setup time by a factor of 10 and thus allows Aarush-M to be

ready for deployment in less than 30 minutes, as stipulated by the competition rules.

The ground station supports a maximum of three payload operators: one administrator and two other

users. Each user runs an independent Graphical User Interface that shares same database over wired

network. A separate MCC operator laptop displays the telemetry data from the UAV.

The PGS requires an 110V AC power source and provides the user ample control over the power of

various system components, thereby allowing the controller to switch off / reset them when needed.

Spare power outputs are also given to allow future expansion. It also provides an umbilical power cord

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to power the RC transmitter separately which precludes battery drain in ground testing. A charging port

for transmitter battery is also incorporated to charge the same during long flight hours.

3.6.

Mission Planning

At the onset of preparation for the competition, the mission plan was based on retrospection of

previous year’s performance, this plan, was evolved with each flight test to adapt to the new system’s

capabilities.

Figure 19: General Flight Plan Profile

It was observed that the initial waypoint navigation, including the take-off took about 3 minutes, while

one traversal of the search area in progressive wave pattern took about 4.5 minutes. Since sufficient

overlap is maintained between consecutive aerial images clicked, only one round of the search area was

sufficient. Extraction of text file from SRIC required less than 30 seconds. The pop-search area was also

tried and it was found out that its traversal took about a minute. These time estimates helped us

develop a general flight plan, which would be used for mission during competition.

4.

Flight Testing and Evaluation Results

The test flights were methodically scheduled to test and tune the performance of each major sub-

system rigorously, with minimum risk, to ensure that the system gets enough flight time to be reliable

and worthy of a competition entry.

Subsystem F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 F11 F12

Autopilot R/C Lateral Tuning LongitudinalTuning

Waypoint

Navigation

Autonomous

Takeoff &

Landing Buffer Flights

Imagery Camera ParametersSelection

Altitude

Optimization

Competition rehearsal – real-time code run;

sweep pattern analysis

SRIC Static Testing Altitude

Variation

Orbit Radius

Variation

Transferred File

Changes

Competition

rehearsal

Table 8: Flight Testing Schedule

4.1. Navigation Performance

Improving the navigation system performance is central to the success of the mission, since it directly

impacts the performance of imagery and SRIC data acquisition. Prior to the flight testing phase, thedynamic model of the aerial-vehicle was developed using AVL, R/C test flight results and vehicle design

parameters. This dynamic model was used in Piccolo II’s proprietary simulator to carry out control law

tuning in software-in-the-loop simulation environment.

The optimum gains found during simulation were then used as initial points for in-flight gain tuning. The

gains were tuned iteratively, until the desired performance of the various command loops was attained.

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Canon G-10 Canon EOS 500D (DSLR)

Figure 21: Target Image

Quality Comparison

Following two images show the waypoint navigation performance during normal day and a windy day.

Figure 20: Clockwise from top-left: Waypoint navigation on normal day (wind = 2 m/s); Waypoint navigation on

windy day (wind = 7 m/s); Bank angle strip chart during orbit

4.2.

Payload Performance

4.2.1.

Imagery

4.2.1.1

Target characteristics

Accuracy of the imagery system was validated by various tests

inside the laboratory and in test flights. Images clicked by Canon

EOS 500D showed significant improvements in image quality in

comparison with the images clicked by Canon G10. For example, target

acquired from images captured by Canon G10 and by Canon EOD 500D are shown in Figure 21.

Data analysis unit was rigorously tested for different target shapes, colors and letters. It was found that

S. No. Parameter Result 1. Bank angle tolerance 2 deg

2. Altitude tolerance 13 ft.

3. Waypoint tracking tolerance 10 ft.

4. Airspeed Tolerance 4.9 ft./s

5. Auto-takeoff 5 successful attempts

6. Auto-landing 1 successful attempt

Table 9: Autopilot performance specifications

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processing an image took about 4 seconds, which makes it reliable for real time applications. The

current image processing software can segment targets autonomously with 72% accuracy. This number

would increase on a ground with lesser pattern variation which gives lesser false positives. The shapes

that can be identified autonomously include star, cross, circle, semi-circle, triangle, square, rectangle,

arc, trapezium and rhombus with 78% accuracy. The accuracy for character recognition was found to be

less than 30%. This number is low, because of the size of the character in images, noise and other

complexities involved in recognition. The Table 10 shows the results obtained during test flights with

few of the target types.

(* green color correct * red color incorrect *grey color Not Analyzed)

Table 10: Imagery Test results for a given set of inputs

4.2.1.2

Target Location and letter orientationDetermination of the target location requires vehicle’s GPS coordinates, altitude and heading

information. These values are tagged by the on-board computer in each of the image. If the plane’s GPS

location at the time of capturing is considered as the actual target location, the maximum error was

found to be within the threshold value of 250ft. It can be calculated as follows(refer to Figure 22):

Max error will occur when target is present at corner of the

image.

√ (

) (

) Where, VFOV is Vertical field of view = 42.2°

HFOV is Horizontal field of view = 63.3°

To improve the target location estimation a set of mathematical equations were used which utilize the

latitude, longitude, altitude, heading and camera field of view to transform the target’s pixel coordinates

into the actual GPS coordinates. This code, however, gave poor results in test flights. It was later

S.

No.

Cropped

Image

After

Segmentation

Shape Shape Color Letter Letter Color

1 Semi-Circle Red Not analyzed White

2 Square Blue P Yellow

3 Star Sea Blue A Sea Blue

4 Semi-Circle Sea Blue Not analyzed Red

5 Triangle Pink Not analyzed Not analyzed6 Cross Red Not analyzed Red

7 Circle Yellow T Grey

8 Semi-Circle Pink Z Blue

9 Triangle Sea Blue D Pink

10 Rhombus Yellow T Grey

Vehicle’s

GPS

Figure 22: Max GPS Error

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0

50

100

150

200

0 100 200 300 400

T

i m e T a k e n ( s e c o n d s )

Orbit Radius (feet)

Transfer of 1 MB of data at different altitudes

and orbit radii200 ft

300 ft

400 ft

500 ft

600 ft

observed that the GPS heading information was being updated with a delay of 5-10 seconds. This

problem is being corrected as of this writing by using a better GPS unit.

4.2.2.

SRICHaving missed out on extracting data from the Simulated Remote Information Center (SRIC) in the 2012

SUAS Competition, the team was keen to perform rigorous testing for our SRIC setup to successfully

execute it in the competition. Testing of the SRIC was done in three phases:

1.

Lab testing: The test environment was set up in the lab to verify the functioning of all systems.

This testing phase was used to

debug and tune the scripts for

automated data extraction.

2.

Altitude and Orbit Radius

variations: The plane’s altitude

and orbit radius above the SRIC

was varied and the access time

was recorded, if the file was

received at all. The result from

these tests showed that for best

results, the plane should fly

somewhere between 200 – 300

feet above ground level, with

orbit radius 150 feet.

3.

Changes in transferred files: Further tests were conducted to figure out the maximum amount

of data that could be acquired from the SRIC in under a minute. To do this, the size of file(s) to

be acquired was increased, starting from a simple text file, all the way to a video. The results

showed that at least 3 MB of data could be accessed in under a minute with optimum flight

conditions.

(Note that every trial required around 5 seconds making the initial connection. The Time Taken

value does not take this into account)

Type of File Size (MB) Time Taken (seconds)

Text File 0.1 0

JPEG Image 0.5 2

PDF File 2.2 13

MP4 Video 26.4 204

5. Safety

The competition demands special attention to safety of personnel and the UAS. The team approached

the development of each design element keeping in mind these crucial criteria. There are two levels ofsafety measures adopted by the team:

5.1

Safety in design of the UAS

Safety in design of all the sub-systems was accounted for at the onset of development. The degree of

safety was quantified by a number called “factor of safety” (FOS) which provided a safety margin for the

design of all critical elements. A higher FOS implied greater the safety margin and hence, more

reliability. The FOS also accounted for theoretical and fabrication inaccuracies which were revealed only

Figure 23: Time taken vs. Orbit Radius for different altitude

Table 11: Time taken for acquisition of different files

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in the later stages of the development.

The foremost safety requirement from the UAS design was the accessibility of manual override in case

of autopilot control failure. Such a failure could happen due to autopilot power loss, or accidental faults

in the autopilot output lines. The team mitigated these sources of error by separating the actuator and

avionics power, and incorporating an RX MUX that allowed manual override any time during the flight.

These corrective measures ensured that the actuators would be powered in the case of avionics power

loss, and that the safety pilot will be able to take control of the plane.

Most of the faults that have compromised the mission have, in the team’s experience, been traced to

electrical sources. The team has taken extra measure of precaution, by using conduits to protect wires

from physical damage and extend their “mean time between failures”. The selection of wire gauges and

insulation types was made keeping in mind the required ampacity, operating temperature and a

conservative factor of safety.

Booster extensions have been used at the output of the RX MUX, in order to prevent loading of its

outputs and signal loss at the actuator. The connectors between the different components of the UAS

were identified as major failure points, and all of them have either been substituted by “positive lockingtype” Molex connectors or, encapsulated by servo extension locks.

The airframe has been designed to handle load factors in excess of 4 which is much higher than the

normal flight envelop. The camera is one of the most critical components on board the UAS; ergo, it has

been recessed inside the fuselage to ensure its integrity even in the case of a main landing gear collapse.

5.2

Safety in mission execution/operation the UAS

While measures were taken in design process to ensure a safe build, the operation of the UAS ensures

that a safe design stays safe for long periods of time. The team tackled this problem by characterising

the “Mean Time between Failures” (MTBF) for critical components. This number allowed the team to

anticipate fatigue of components and thus replacements were made accordingly.

The safety of the UAS during competition and flight testing is ensured by conducting rigorous pre-flight

checks. These checks have evolved with the team’s experience with failures and ensure that the UAS is

fit to fly prior to mission execution.

Efforts have been made to minimise human errors during mission execution by establishing a

communication protocol amongst the ground crew. The ground crew now follows a chain of command

to ensure that only the right people are making the relevant choices, thus avoiding errors. A pre-

determined mission protocol is in place to ensure that there is no panic in the case of any crisis.

The safety officer is accoutred with a master checklist and is in-charge of overall safety of the mission.

He is second in command, after the flight director to call off the flight in the case of any aberration in

system performance.

A rigorous range check is performed to ensure that the airplane stays under manual control for the

desired airspace boundary and there are no glitches in control surface actuation.

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5.3

Failure Mode-Effect Analysis

The team used the flight testing experience to identify risks and develop multiple contingencies for each

fault. A detailed Failure Mode-Effect Analysis was carried, out and the mission status after each failure

mode is classified as follows:

Failure Mode Indication Effect Primary Response Secondary Response

Telemetry Link

Loss

‘Link’ Indicator

turns red at MCC

terminal, unusual

navigational

response

Link between

60% to 80%

Observe Autopilot

telemetry for link

improvement.

Observe Autopilot

telemetry for link

improvement.

Link less than

60%

Observe Autopilot

telemetry for 15

seconds for link

improvement.

Switch to manual and

troubleshoot

communication link.

Image Acquisition

System Failure

Image

synchronisation

fails or is

unresponsive

Image

processing

possible but

slow

Observe link for 2

minutes for

improvement

Reset router power and

observe link

Image

processing not

possible

Reset router power

and observe link again

Emergency landing to

troubleshoot imagery

subsystem

Mission Control

Centre computer

crashes

Command Centre

hangs or Shuts

down

Autopilot

Navigation

Affected

Shift to R/C,

meanwhile backup

computer brought in

Resume mission after

setting up backup

Autopilot terminal

Imagery Terminal

Crashes

No output on

screen

Image

Processingaffected

Terminal restarted,

backup imageprocessing terminal

brought in

N/A

Avionics or

Propulsion

Battery level

unsafe

Indicated on PCC

plugin

Flight

Endurance

Affected

Emergency landing

within three minutes

Swift battery replacement

and take-off

Motor cut-off Continuously

falling Airspeed

and/or Altitude

Flight Stability

affected

Shift to R/C and

emergency landing

engaged

Swift battery replacement

and take-off

Component

Disintegration

Falling debris,

erratic behaviour

Aircraft

integrityaffected

Shift to R/C and

emergency landingengaged

Quick ground assessment

and take-off if feasible

Unable to hold

altitude/Enters

no fly zone

Altitude or

position error

observed on MCC

Autonomous

navigation

accuracy

affected

Switch to manual,

mission continues;

Adjust the control law

gains

Switch to autopilot and

observe

Code Blue: Mission Continues, Fully Autonomous

Code Yellow: Mission Continues, Manual Override

Code Red: Mission Haults, Emergency Landing

Table 12: Failure Mode Effect Anal sis

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6

Acknowledgements

The Team extends its gratitude towards University Vice Chancellor Prof. P B Sharma for his constant

support and encouragement to the project. The team is indebted to its project advisor Prof.N S Raghava

for his timely guidance and motivation during the course of the project.

Team UAS-DTU would like to immensely thank Lockheed Martin Aeronautics Company for their

mentorship and financial support in the project. The team also acclaims the support of the former team

members who helped the team in preparing flight plans and execution of mission.

The team is grateful for the efforts of Mr. Jasvinder Singh, who was the safety pilot for developmental

test flights of Aarush-M.

DTU 2013

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