technion aerial systems 2015 journal paper for auvsi ... · - 1 - tas – the technion aerial...

- 1 - TAS – The Technion Aerial Systems Team

Technion Aerial Systems 2015

Journal Paper for AUVSI Student UAS Competition

The Technion – Israel Institute of Technology

Faculty of Aerospace Engineering

Faculty of Electrical Engineering

Abstract In preparation for the Student Unmanned Aerial Systems competition of 2015, the Technion Aerial Systems (TAS)

team’s objective was to design, develop, manufacture and verify a new and improved system, surpassing the

capabilities of a system presented last year in the competition. Undergraduate students from both the Aerospace

Engineering and the Electrical Engineering faculties developed the new system, named Tyto. The students designed

and manufactured their own custom aircraft, suited for the competition's missions and integrated it with full

autonomous capabilities, a high quality stabilized imaging payload, and reliable, independent communications to the

Ground Control Station, while keeping safety a foremost priority throughout the entire process.

Figure 1 - Tyto UAS.


Table of Contents

1 System Engineering Approach .......................................................................................................... - 3 -

1.1 Mission Requirements Analysis ................................................................................................ - 3 -

1.2 Design Rationale ....................................................................................................................... - 3 -

1.3 Expected Task Performance...................................................................................................... - 3 -

1.4 Programmatic Risks and Mitigation Methods........................................................................... - 4 -

2 UAS Design ...................................................................................................................................... - 5 -

2.1 Airframe .................................................................................................................................... - 5 -

2.2 Wing Design ............................................................................................................................. - 6 -

2.3 Propulsion System .................................................................................................................... - 7 -

2.4 Autopilot (AP) & Flight Control System .................................................................................. - 8 -

2.5 Payload System ....................................................................................................................... - 10 -

2.6 Ground Control Station (GCS) ............................................................................................... - 11 -

2.7 Data Link ................................................................................................................................ - 12 -

2.8 Development Process of Specific Mission Tasks ................................................................... - 13 -

2.9 Mission Planning .................................................................................................................... - 15 -

3 Test and Evaluation Results ............................................................................................................ - 16 -

3.1 Aircraft Performance and Airworthiness ................................................................................ - 16 -

3.2 Mission Task Performance...................................................................................................... - 16 -

3.3 Autopilot System Performance ............................................................................................... - 17 -

3.4 Payload System Performance .................................................................................................. - 19 -

4 Safety Considerations ..................................................................................................................... - 20 -

4.1 Operational Safety Criteria ..................................................................................................... - 20 -

4.2 Design Safety Criteria ............................................................................................................. - 20 -

4.3 Safety Risks and Mitigation Methods ..................................................................................... - 20 -

5 Conclusions ..................................................................................................................................... - 20 -


1 System Engineering Approach

1.1 Mission Requirements Analysis

Design, development and verification of the Tyto system required a detailed work plan in order to fulfill mission

requirements, meet competition schedule and minimize development risks. Mission tasks were prioritized according

to several parameters:

o Grade – Each task has graded parameters, which indicates its importance.

o Complexity – Assessment of manpower and man-hours required.

o Budget – Evaluating costs for hardware and components necessary.

o Previous Experience – Implementing lessons learned from last year’s team regarding available facilities

and capabilities.

Tasks were divided to three categories: 'Will Accomplish'- proven capabilities developed by last year's team. 'Will

Attempt'- development, integration and testing is required. 'Will Not Attempt'- Allocation of resources inhibits the

ability to perform.

Will accomplish: Autonomous flight, search area, actionable intelligence, emergent target and airdrop.

Will attempt: Interoperability, off axis target, ADLC, SRIC and SDA.

Will not attempt: IR target.

1.2 Design Rationale

A thorough investigation of 2014 system, combined with 2015 mission analysis has yielded several design focus

points:

o Design of an aerodynamically superior aircraft, easier to operate, transport and maintain, while preserving

main characteristics of the previous aircraft to reduce development risks.

o Improved image processing algorithms and reduced localization error.

o Design of new electro-mechanical subsystems for the off axis and airdrop tasks.

o Development of algorithms for the SDA and interoperability tasks together with integration to the autopilot.

These focus points were addressed by the team through several changes in comparison to last year:

o Improved aerodynamics – Semi trapezoidal wing planform and winglets for improved aerodynamic

efficiency and aircraft maneuverability.

o Higher durability – Aircraft structure made of all composite materials to gain a durable yet lightweight

aircraft.

o Modular build – Five modulus parts, which can be disassembled for simple transportation both to the test

field and internationally to the competition site in the United States.

o Easy maintenance – 4 longitudinal elements embedded in the fuselage to allow a larger access panel. The

access panel and removable cover ensure a quick access to each subsystem in the aircraft.

o New On Board Computer (OBC) – The Odroid XU3, a successor of last year’s OBC, allows for faster

image acquisition and processing.

o Software optimization – Complete redesign of algorithms and software to allow for only one

programming language (Python). In addition, the main image processing workload was transferred to the

ground station in order to employ more robust algorithms, nullifying OBC limitations.

o Rotating camera turret – Custom built to address Tyto’s ability to stabilize its camera and track an off

axis target, while minimizing the aerodynamic drag.

o Improved airdrop mechanism – A new mechanism allowing for better accuracy and reliability in the air

drop task.

o Enhanced flight management software – Software modification and customization to handle the

interoperability and obstacle avoidance tasks.

o Simple Graphic User Interfaces (GUI) – User friendly GUIs were designed in order to improve operators'

ability to identify and solve problems during flight.

o Safety – Key emphasis was placed on safety through all design and development stages, resulting in

methodical, checklist based, operational procedures in addition to redundant safety mechanisms.

The team endeavored on these missions while keeping schedule a foremost priority, to ensure all deadlines are met

with sufficient margins to facilitate unknown factors.


1.3 Expected Task Performance

This year's Tyto UAS is a direct descendent of last year's Grey Owl system, implementing major improvements

while preserving main aerodynamics characteristics and system architecture. This allowed the team to evaluate

Tyto’s expected performance during the design phase. A new wing design and propulsion system is expected to

improve flight performance, reflected in better waypoint navigation, 10% decrease in battery consumption and lower

mission time. The target identification process is predicted to improve, due to upgraded categorization algorithms. A

rotating camera turret will enable the team to locate the targets more precisely as well as successfully perform the

off axis task. A redesigned air drop mechanism should allow for better accuracy in the air drop task as well as higher

reliability. Customized software will allow performing the interoperability task, and provide the obstacle avoidance

algorithm with a robust connection to the judge’s server. The system is expected to be able to avoid both stationary

obstacles and moving obstacles, with some difficulty in complex situations. Considering all of the above, the team

expects to accomplish all primary tasks at an objective level and eight of the nine secondary tasks at an objective or

threshold level. Due to high hardware cost, the IR task will not be attempted.

1.4 Programmatic Risks and Mitigation Methods

For successful deployment of the system, a comprehensive risk assessment was carried at an early stage in the

design process. The following table represents main risk factors identified by the team and their corresponding

mitigation methods.

Table 1 - Programmatic risks and mitigation methods

Risk Factor Description Impact Likelihood Mitigation Method

Design and/or

manufacturing

delays

Delays in the new airframe design or

construction, and/or a major design flaw

might prevent the team's plan to present a

new airframe.

High Medium

o 2014's system was maintained as

a substitute vehicle, all systems

were designed to fit both

airframes.

Insufficient

crew training

Schedule delays might prevent the team

from sufficiently training flight crews for

system operation.

Medium High

o Initial training was done using

last year's system to allow for test

flights at an early stage of the

project.

Integration

issues

Since subsystems are developed

concurrently, full system integration is

performed on a later stage. Conflicts

between subsystems could endanger the

full UAS.

High Medium

o Subsystems compatibility was

tested prior to the decision on

each system's incorporation.

o Communication protocols and

subsystems schemes were written

during initial design.

Legal and

Safety issues

The system must comply with the

competition rules as well as those of the

academy of model aeronautics (AMA).

High Low

o A thorough inspection of the

rules and regulations was made

before the design of the airframe.

o A dedicated team member was in

charge of addressing any legal

issues and system's safety.

Crash during

testing

While testing new systems and

algorithms, a crash of the entire system

would endanger the team's ability to

participate in the competition.

High Low

o Two airframes were constructed

and tested to provide redundancy.

o The 2014 aircraft was maintained

as a backup solution.

Incapacitation

of flight crew

Flight crew members might be unable to

participate in the competition, thus

impacting the team's performance.

Low Medium

o Two different team members

were trained for each flight crew

position.

o Different flight crew

compositions were trained

together.


2 UAS Design

Figure 2 - Tyto main dimensions in mm.

Table2 - Tyto main aerial charecteristics

Operational Performance Horizontal Stabilizer Vertical Stabilizer Main Wing

22 Kts Stall Speed NACA

0012 Airfoil NACA 0012 Airfoil

Douglas

LA203A Airfoil

34 Kts Cruise Speed 0.75 m Span 0.35 m Span 2.6 m Span

47 Kts Max Speed 0.17 𝑚2 Area 0.07 𝑚2 Area 0.85 𝑚2 Area

30 m Minimal Turn

Radius 3.4

Aspect

Ratio 1.45

Aspect

Ratio 6.8

Aspect

Ratio

40 min Endurance Propulsion System Figures of merit Aircraft Dimensions

11.5 Kg MTOW 1650W Motor

power 13.5 Kg/m2 Wing

Loading 2.04 m Length

4 Crew 17X10 Prop. Size 143 W/Kg Power

Loading 2.6 m Width

>2 Km Operational

Radius

14.8 V

27 Ah Batteries 3.5

Max Load

Factor 0.69 m Height

2.1 Airframe

The new and improved airframe designed to be portable, lightweight, easily repairable and quickly assembled on

site unlike 2014's tip to tip wing design and one piece long fuselage which proved to be difficult to transport.

Therefore, the airframe is comprised of five main modulus parts: fuselage, large removable top cover, detachable tail

unit and two separate right half and left half wings sections. The new airframe features 2.6 m wingspan, overall

length of 2.04 m and max takeoff weight of 11.5 kg.

Quick access to components was accomplished by a large, easily removable, upper access panel, extending from the

front rib to the tail boom assembly connection. This enabled easy access to the payload bay and system components.

The cover is embedded with a hook fastener at the front, and a strong magnet at the rear, enabling for a quick and

reliable mounting to the fuselage, without the use of screws.

Advanced manufacturing techniques were implemented throughout the building process of each part. The fuselage

was manufactured as a whole and uniform body using a two parts mold. Its strength and durability was achieved by

multilayer and unidirectional carbon fiber reinforced polymer. Two Rohacell strips were embedded between two

layers of carbon fiber, in the bottom edges of the fuselage in order to sustain bending moments. A uni-directional

graphite fiber strip was also embedded at the top edges of the fuselage for the same reason. The mold was designed

using a Computer Aided Design (CAD) software and carved using a Computer Numerical Control (CNC) machine.


Figure 3 - Left: Main structural assemblies. Right: Fuselage with cover removed.

The fuselage is divided into three compartments: forward bay – nose and front landing gear, mid bay – payload,

batteries and flight control, and a rear bay - tail boom connection. Supporting carbon fiber coated wooden frames

were embedded to divide the three compartments, as well as to support loads the fuselage sustains. The camera

turret is connected to the fuselage at the front by an aircraft grade plywood partition. Since the tail can be dismantled

for easy transportation, the aft connecting bay was designed to fit the tail boom and hold it in place. The tail boom

attachment fittings were designed in a CAD software and produced using a milling machine. The connectors were

glued and screwed on carbon-fiber reinforced partitions in the fuselage. Bending and torsion experiments were

performed in order to assess the connectors’ durability and a safety pin was drilled in order to prevent any rotation

or slipping of the boom from the connectors during flight.

The rudder and two elevator segments were made of a Styrofoam core base, coated with soft balsa plates and

connected to a custom made connection mount. The use of balsa in the tail section allowed it to be as light as

possible without affecting the maneuverability of the system.

Figure 4 - Left: Fuselage and supports. Middle: Wing connection overview. Right: Tail boom connection bay.

2.2 Wing Design

Careful analysis of flight conditions and expected performance of the airframe was performed in order to design a

wing strong enough to withstand predicted loads, while keeping wing structure as light as possible. The analysis of

lift distribution and subsequent loads were calculated using analytical methods and validated using commercial

Vortex Lattice Method (VLM) and Finite Element Method (FEM) programs.

Figure 5 - Left: VLM simulation, Right: FEM wing beam loading simulation.

Right wing Left wing

Tail assembly Fuselage Airdrop

system

Batteries

Flight control

system

Payload

Main wing

connection

Secondary

wing

connection

Tail boom attachments

Safety pin

Nose landing

gear support

Main landing

gear support

FWD

frame


The final wing design is a semi trapezoidal planform with a main forward C beam, constructed out of graphite fabric

and uni-directional fibers and held by epoxy resin. The wing's core was made from lightweight Styrofoam and glued

to the forward beam. Kevlar reinforcements were added near the root section covering the wing joiner housing.

Balsa layer cover was added on top to ensure smooth joining followed by graphite/epoxy skin. The outboard part of

the wing not withstanding high loads was left uncovered during this process in order to reduce weight and was later

covered with monocot skin to reduce drag and improve visibility while in the air. Spaces for the actuators and the

speed controllers were then cut and covered with lids. Finally, a custom motor housing was designed and

manufactured by layering graphite and epoxy over a tube and milling the material to its final shape, which was then

glued to the wing and strengthen with graphite and epoxy.

Figure 6 - Left: Root rib and wing connections. Right: Inner wing structure.

2.3 Propulsion System

The aircraft features a twin engine configuration, mounted on the wings. This configuration is the outcome of last

years' experience, which proved efficient and improved the survivability of the aircraft in case of an engine failure.

The team decided to utilize the motors and ESCs used last year for their reliability and their weight-to-motor-power

ratio. ESC's were installed inside the wings to save space in the fuselage for other systems and to gain better cooling

from the propeller wake.

Figure 7 - Left to right – motor, ESC, batteries and propellers.

New XOAR PJP-N-P series propellers were selected. These are Beachwood propellers with metallic coating for

electric fixed wing applications, with a contra rotating configuration that prevents roll moment interference, which

results from the propellers' torque. An empirical based analysis was performed to find batteries required to power

the aircraft. Assuming straight, leveled and constant velocity flight, the team determined motor thrust needed for

cruising at a certain chosen airspeed. This knowledge allows the deduction of motor power consumption.

For size 17X10 propellers, the colored curves of figure 8 (right) represent equivalent airspeed at constant RPM, the

red vertical line represents the stall airspeed, while the blue curve represents the drag which was plotted based on an

adequate drag model which was validated during flight tests.

Foam core

Gr/Ep C beam

Reinforced fuselage

connection area Wing-fuselage

connection

Torque screw Gr/Ep skin

Wooden root rib

Power and

signal channel


Figure 8 - Left: Airspeed vs. propeller efficiency, Right: drag and thrust vs. airspeed.

The required motor power, together with the efficiencies found, allowed the team to assess the output power of the

motors as a function of airspeed. The figures below present output power and current consumption of the propulsion

system as a function of airspeed.

Figure 9 - Left: Airspeed vs. propulsion power. Right: airspeed vs. propulsion current.

The described analysis provided the team with an estimation of the battery capacity necessary in order to reach 40

minutes flight time, which depends on the cruising speed of the aircraft. Using this information and considering

power consumption for the components on board the vehicle, required battery capacity was determined.

Chosen batteries were Tattu 6750mAh 14.8V 25C 4S1P Lipo, chracterized by a low internal resistance, high current

load, good voltage stability, long life span and are relatively lightweight, compared to competing batteries.

2.4 Autopilot (AP) & Flight Control System

In order to incorporate fully autonomous capabilities to the aircraft, while maximizing safety and minimizing

development risks, the autopilot system had to fulfill several requirements:

o Proven Hardware & Software – used worldwide in the UAV community and by TAS teams.

o Pilot's manual override – possible at all stages of flight, directly from the pilot's radio controller.

o Easy sensors integration – "plug & play" air data, altitude & heading reference system (ADAHRS), navigation

and propulsion monitoring sensors.

o Two-way long-range radio communication capable – delivering real time telemetry and receiving flight plan

and commands from the ground operator.


o Open source ground control and airborne software to allow customization for the competition tasks.

o Vast functional capabilities – waypoint navigation, midair re-tasking, autonomous takeoff & landing and

designated mission scripting.

o Small and lightweight airborne hardware, with low power consumption and no special cooling requirements.

o Affordable overall solution.

Thorough examination of various commercial, off the shelf systems led the team to consider two main autopilot

boards: last year’s ArduPilot Mega (APM) 2.6 and its successor, the Pixhawk. The team selected the Pixhawk

autopilot due to significant improvements in its hardware. These improvements enabled the team to customize the

autopilot code, advance to sum ppm technology and include new sensors in the system. The autopilot built-in

capabilities include autonomous takeoff & landing, waypoint navigation including lateral positioning and altitude

control, midair re-tasking and designated mission scripting. In addition, absolute navigation solution with a <3m

Circular Error Probability (CEP), interference resistive magnetometer with <4° compass heading error and an

airspeed sensor (Pitot tube) with <1.5% airspeed error. With this Pitot tube extending from the nose of the UAV,

external 3DR uBlox kit behind the wing alongside its built-in Inertial Measurement Unit (IMU) and barometric

sensor, the autopilot is able to calculate its 3 dimensional position (latitude, longitude and altitude), attitude and

heading in a manner both efficient and reliable for the missions at hand.

For safe and accurate autonomous landing, the SF02 Laser Range finder was installed in the aft fuselage, looking

downward. Connected to the autopilot, this rangefinder is used as a laser altimeter, yielding accurate AGL (Above

Ground Level) altitude data.

The flight control system surrounding the autopilot is described in Figure 10. This flight control system is built to

add redundant safety mechanisms expressed by:

o Software safety switch – arms/disarms the motors on a software level as a part of the Pixhawk AP.

o Hardware signal relay – allows bypassing the autopilot completely in case of a major failure.

o Motor safety plug – upon removal, cuts all power from the motors to prevent injury while team

members are near the aircraft.

Figure 10 – Flight control system architecture.

Servos

RFD900

Telemetry

Servo Power

Switch

Laser Altimeter

FUTABA

8ch

Receiver

Hardware Signal

Relay

Pitot

GPS + Compass

Engines Power

Module

Safety

Plug

OBC Telemetry Pixhawk

Autopilot

Batteries ESCs

Autopilot

Controller Safety Pilot

900 MHz (FHSS) 2.4 GHz (FASST)


2.5 Payload System

The payload system consists of an EO camera, OBC, custom camera gimbal and high speed communication

modulus. The Odroid XU3 OBC, provides robust, in-flight communication and high performance image processing

capabilities. It supports various Linux distributions giving easy access to many of the tools used during development

(MySQL, Python, OpenCV). Utilizing the multicore nature of the OBC allows the target detection algorithm to run

in parallel, using all four available cores, while handling simultaneous high-speed wireless communication and

performing intensive disk read and write accesses.

The Canon Powershot s110 has a 12 megapixel 1/1.7’’ CMOS sensor and uses a 5x zoom lenses with a focal range

of 24-120mm. It is attached to the gimbal and connected to the OBC via USB cable. The Pixhawk autopilot

telemetry port is also embedded in the system and provides real time orientation, GPS coordinates and altitude.

Clock synchronization for the camera-GPS module is an important issue for target localization and performed by

measuring and compensating the average offset between capture time and storage time for every captured image.

Figure 11 - Payload system architecture.

Mission analysis derived two modes of operation for the imagery payload. First, stabilized mode allowing image

capturing and target recognition. Second, off-axis shooting and tracking ability. In order to meet these requirements,

a new camera turret was designed and manufactured. Dual roll-tilt configuration was chosen for minimizing weight

and volume. Consisting of a rotating outer nose cone (roll axis) and an inner camera gimbal mount (pitch axis), the

turret allows for large roll and pitch rotations, guarantying no targets will be missed or captured at the camera

sensor's margins.

The gimbal utilizes two servos, controlled by the autopilot, whose servo output operates at 400Hz, providing high-

resolution control and accuracy. The turret assembly is attached to the front fuselage frame and operated using a ball

bearing. The aerodynamic cover is made of graphite/epoxy and manufactured using molds in a similar method to the

fuselage previously described.

Figure 12 - Tyto's rotating camera and gimbal system.

ODROID XU3 OBC

Ethernet

switch

Gimbal*

S110 Camera Bullet M5

Bullet M2 Batteries

Telemetry from AP

Power

Distribution

Unit

POE

POE

Imagery

GCS

SRIC

server

* Gimbal controlled by AP

2.4 GHz

5.8 GHz

Payload

Power

Switch


For easy integration and detection of hardware malfunctions, full system wiring scheme was prepared. This allowed

the team to better understand failure modes possible, incorporate safety mechanisms and ensure no hardware

conflicts arise during integration.

Figure 13 - Wiring scheme.

2.6 Ground Control Station (GCS)

GCS's design aimed at allowing independent operation and a high level of performance for each of its four main

functions: Flight Management Console (FMC), Mission Display Console (MDC), Imagery Console (IC) and Safety

Pilot. To achieve this, each of the three consoles uses its own computer and operates via different communication

networks (will be discussed further in the datalink section). All consoles are placed adjacently and communicate

with the GCS commander, who maintains communications with the safety pilot via hand-held radio.

o Flight Management Console (FMC)

The purpose of the FMC is monitoring aircraft status, controlling and performing flight-related tasks (e.g., waypoint

navigation) using an RFD900 communication module. The FMC runs the autopilot software, which has been

modified by the team to allow communication with the judges' server (interoperability task). The FMC also

communicates with the Mission Display Console, which runs on a different computer, via Local Area Network

(LAN) to allow for integrated mission display.

A backup computer is preloaded with all mission parameters and controlled by the secondary operator. Its purpose is

to replace the main computer in a case of system failure and create alternative flight plans, thus decreasing work

load on the main operator.

o Mission Display Console (MDC)

The main purpose of the MDC is to satisfy the interoperability display requirement. In addition, the team has

decided to demonstrate a higher level of interoperability and incorporate information from additional mission tasks.

Meaning, the Graphic User Interface (GUI) built was integrated with the SDA task as well as other tasks to display

relevant information while each task is being performed.


o Imagery Console (IC)

The imagery interface includes one computer and has three functions:

o Camera Control – allows the operator to activate the on-board camera and/or change settings.

o Manual Target Identification – downloads all images and related data marked as suspected targets by

the OBC and stores them in a database. The images are displayed in a dedicated GUI, which allows the

operator to examine the images and apply further computer aided analysis, in order to successfully

complete the identification and characterizing process.

o Automatic Target Identification – in parallel to the manual identification process, an image-processing

algorithm creates a list of identified targets, allowing the team to perform the ADLC task.

The Target Identification Console communicates with the aircraft using a broadband Wi-Fi network channel, which

will be further discussed in the data link part below. In addition, another operation ready, backup computer is set

aside to be used if needed.

o Safety Pilot

In any case of an autopilot failure (e.g., communication malfunction), the team includes a safety pilot who uses a

separate communication channel and can take over the plane in a way that completely bypasses the autopilot's signal

and is independent of its power source.

Figure 14 Left: FMC. Middle: MDC. Right: IC.

2.7 Data Link

Tyto’s graphite-epoxy fuselage requires robust communication modules and proper installation design.

For Autopilot telemetry, A RFD900 two-way telemetry module was chosen. With two-antenna design and high-

power level ensuring signal integrity.

For manual control by the safety pilot, a Futaba Advanced Spread Spectrum Technology (FASST) Transmitter and

receiver (TG14SB and SB2608 respectively) were chosen and tested to ensure undisturbed connection up to 2 Km.

For imagery data transfer from the OBC to the GCS, the UBIQUITI Bullet M5-HP, 5.8GHz Wi-Fi (for the UAV),

together with the UBIQUITI NanoStation M5, 5.8 GHz Wi-Fi (for the GCS) are utilized. It provides the ability to

gather satisfying information from the UAV to the GCS for a range of up to 1.5 kilometers.

For the SRIC link, the UBIQUITI Bullet M2-HP, 2.4 GHz Wi-Fi is installed.

Figure 15 - Comunication networks.


2.8 Development Process of Specific Mission Tasks

o Autonomous Waypoint Navigation

Tyto's ability to fly autonomously between waypoints and capture them with minimal navigation error, depended

mainly on its aerial abilities and fine-tuning of autopilot parameters. Therefore, the aircraft was designed to meet

main aerodynamic requirements of maximum climb angle and minimum turn radius seen in prior competitions as

well as an extended flight envelop formulated by the team. After manufacturing the aircraft, the team evaluated its

performance and determined the required PID gain values of the autopilot both on the ground and during flight tests.

o Automatic Take Off and Landing

To minimize risks and damage to the aircraft, this task development process included many flight experiments using

a safety pilot. In addition, automatic landing was first tested on a small RC wooden plane, equipped with the

Pixhawk autopilot and all necessary sensors in order to fly autonomously. The auto landing process relies on the

SF02 laser rangefinder, used as an accurate AGL sensor and connected to the autopilot. The team is currently in the

process of final testing onboard the Tyto system in order to perform this task in the competition.

o Image Processing (search area and ADLC tasks)

Since the downlink channel of the system is limited in bandwidth, initial target identification takes place at the

Imagery Console, which first receives a scaled down image used for the manual target identification. ADLC

required an implementation of initial crop detection done using MSER (Maximally Stable Extremal Regions)

algorithm, written in Python using OpenCV. This algorithm uses gray scale images in order to find regions of pixels

which are prominent (i.e. 'stable'). The MSER algorithm uses specific constraints, such as the predicted size of

suspected target regions based on flight altitude and attitude. The algorithm outputs a list of regions with suspected

targets which are then sent via uplink to the UAV. The OBC sends down only the resulting, full resolution, crops

(see figure 16) from the original images via downlink, thus saving significant bandwidth.

Figure 16 - Examples of image crops which may or may not contain possible targets. Images were taken during

flight tests. Right most image was taken at SUAS 2014.

Next, it is assumed that the suspected target crop contains a maximum of two dominant colors, of both the

alphanumeric character and the background. Since the size of the character may vary and reach up to 90% of the

target itself, the K-Means algorithm is used in order to extract both colors. The outlining target contour is compared

with known contours of geometrical shapes (see figure 17) prepared in advance. If a suitable match is found, it is

classified as a target shape, if not, the crop is discarded as false positive.

Figure 17 - Examples of possible target shapes (left) and letter shapes (right).

The alphanumeric letter is extracted from the target by masking out all colors differing from the color of the letter.

The contour of the letter is then extracted and subjected to the same contour comparison algorithm, while the most

likely letter and its angle relative to the flat bottom of the given crop is extracted. This angle is then used to

determine the orientation of the target. Localization of the detected targets is done by projecting the target’s image

onto global coordinate reference system taking into account the camera's attitude.


o Interoperability

The algorithm for the interoperability task is built on the main loop of the FMC and uses an HTTP protocol, which

operates on top of the TCP protocol in order to send and receive information from the server. In case the server

returns an error, the FMC sends a message box containing the error description, and the process stops. The algorithm

consists of an HTTP GET and POST requests: login, download server time, download obstacle information and

upload UAS position. The last three processes are executed in parallel using multithreading to keep the transmission

and reception rate at an average of 10 Hz.

o Sense, Detect and Avoid The chosen approach for this mission was a variation the team has developed for real time calculations of 3D Dubins

curves. This approach is computationally efficient with a complexity of O(n), where n is the number of obstacles,

meaning the solution is purely geometric with no discretization of the flight space.

The algorithm inspects for obstacles located between its current location and the next waypoint. To calculate a new

path, the algorithm sends an imaginary tangent line starting at the current location of the vehicle and ending at the

obstacle's circumference. It does the same between the obstacle's circumference and the desired next waypoint, all

the while using a safety envelope around the obstacle, to avoid contact. The algorithm generates three possible

solutions: clockwise, counter clockwise and above, after which commences a process of solution elimination. It

eliminates all inadequate solutions, such as those leading outside the fly zone or where the maneuver required

exceeds the UAV designed loads. Once all possible solutions have been determined, the algorithm chooses the

optimal path. Key emphasis was placed on integration with the autopilot system. It was found that for safety

considerations, modularity for future improvements and ease of system integration, guidance will be based on a

series of auxiliary waypoints to maneuver the UAV around the obstacle.

Waypoints

Fly Zone

Moving

Obstacles

Stationary

Obstacles

UAV

Position,

Velocity

UAV

Dynamics

Clockwise

Current WP

Check PathGenerate

Solutions

Not

Clear

Eliminate

SolutionsCCW

Above

Optimal PathPossible

Solutions

Clear

Figure 18 - Obstacle avoidance algorithm scheme.

o Off Axis

Algorithm development included calculations of the nessecery roll and pitch angles required for creating a line of

sight between the camera and the off-axis target. Angles are trarnslated into simple PWM command which is then

sent to the gimbal servoes. Flight experiments focused on fine tuning parameters to achive target tracking as well as

crew member collaboration, since this task requires the MFC and IC to work together.

o SRIC

For this mission, the OBC runs an automated script, continuously attempting to connect to the SRIC network. Once

successful, it downloads data and uploads a real-time imagery. Linux scripts were developed to handle scenarios of

failure, such as link failure during files transfer and IP address conflicts. Flight tests proved that the system is able to

transfer the required data during a fly-by of the SRIC router, eliminating the need for loitering above.


o Air Drop System

The solution for this task is based on the algorithm developed last year. Parameters were updated in order to

improve hit accuracy and a new mechanical ejection mechanism was designed, built and tested. The custom built

drop mechanism minimizes time delay between release command and ejection. In addition the use of a spring

reduces free flight error by providing additional initial velocity towards the ground. The egg loading process was

also improved by designing a top loading hatch.

Figure 19 - Egg-drop mechanism, Left: CAD model. Right: After integration.

2.9 Mission Planning

Mission planning is performed prior to the flight day, several flight plans for the primary tasks are generated

accounting for different weather conditions and executed by the main autopilot operator (colored in blue in Figure

20). Flight plans for the secondary tasks are generated while the system is airborne by the secondary autopilot

operator (colored in turquoise in Figure 20) in consult with the GCS commander. Feedback from the imagery

operator (colored in red in Figure 20) for the relevant tasks (whether successful or not) determines further course of

action. After each secondary task, mission time remaining is evaluated by the team captain (colored orange in Figure

20) who decides on performing additional tasks or landing, after which, additional data processing can be performed

if necessary.

Preflight

Check

Interoperability

Server

Connection

Judges’

Clearance

TakeoffWP Nav

4 min

Search

Area

4.5 min

Primary Task

Threshold

Not Achieved

Emergent

Target

4 min

Achieved

Actionable

Intelligence

Mission Display

Console

Emergent Target

Flight Plan

Off Axis

Flight Plan

Distance to

Tasks

Off Axis

2 min

Air Drop

4 min

SRIC

2 min

Mission

Time

>5 min

LandingData ProcessingResults Submission

<5 min

Figure 20 - Mission planning.


3 Test and Evaluation Results

3.1 Aircraft Performance and Airworthiness

Prior to flying the Tyto as a full system, a series of test were perform to guarantee its airworthiness.

o Wing Load Test

A wing loading test was performed to ensure the wing can support all flight modes and maneuvers up to 3.5g

defined in the preliminary design stage. Flight conditions and lift distribution were calculated using a specially

designed simulation and were validated through a commercially available software. Testing was performed by

gradually applying different weights spread along the wings span, in accordance with the evaluated lift distribution

and subsequent loads. In each step, the total deflection of the tip of the wing was measured. Results confirmed wing

strength, as it returned to its original form, with no residual deflection. These results were then analyzed and

compared to the computations made on the structural simulation during the preliminary design stage.

Figure 21 - Wing loading test.

o Static Thrust Tests

Static thrust experiments were conducted on several motors, comparing them in various categories: weight,

reliability, performance and motor temperatures. Further comparisons were made between static tests results and

manufacturers data, in order to ensure reliability and performance. Each component was evaluated separately during

the experiments and an optimal propulsion configuration of: batteries, speed controller, propeller and motor was

subsequently chosen.

Figure 22 - Static thrust testing configuration.

3.2 Mission Task Performance

Throughout the year, 25 flight days were conducted with an average of 6 flights per day, totaling about 150 flights.

Of the flights carried out, approximately 100 were conducted in order to test and fine tune subsystems and

algorithms and 40 flights to determine the performance of each subsystem once finalized. Finally, 10 flights were

performed as a full competition simulation where the team was judged by external evaluators (faculty staff) that

were informed of competition requirements and grading matrices. Mission tasks performance for the 10 competition

simulations are summarized in table 3.

Weights Wingtip

deflection

Thrust control

Electrical data

Thrust

measurement

Battery

ESC

Motor Propeller


Table 3 - Summary of competition simulation acheivements

Mission task Sub-task Threshold achieved Objective achieved

Flight time n/a 80% (<20 min) 100% (<30 min)

Autonomous flight

Waypoint navigation 100% 100%

Auto takeoff n/a 30%

Auto landing n/a n/a

GCS display 100% 100%

Search area

Localization 95% 90%

Classification 95% 80%

Classification (QRC) 90% 50%

Autonomous search n/a 100%

ADLC

Localization n/a 40%

Classification n/a 30%

Classification (QRC) n/a 30%

FAR n/a 60%

Actionable intelligence Actionable Intelligence 100% 100%

Off axis

Imagery n/a 80%

Classification 80% 60%

Payload autonomy n/a 70%

Emergent target

In-flight re-tasking n/a 100%

Autonomous search n/a 100%

Target Identification 80% 60%

SRIC

Download n/a 90%

Upload Did not attempt* 70%

Autonomous SRIC n/a 90%

Airdrop

Release Did not attempt* 100%

Accuracy 80% 30%

Bull’s eye delivery n/a 0%

Interoperability

Download & display

obstacles/server time 100% 100%

Upload UAS position 100% 100%

Sense, detect and avoid Stationary obstacles Did not attempt* 50%

Moving obstacles Did not attempt* 40%

*The team decided to opt for objective level score without attempting the threshold.

3.3 Autopilot System Performance

Thorough ground testing were carried out, validating system reliability. This included system integration, sensors

accuracy, algorithm examinations and inspection of changes compared to last year's system. Following successful

ground testing, the system was installed on a simple RC model airplane and basic performance were tested. Once

confidence was gained, the AP was fitted to the new airframe and extensively checked: waypoint navigation ability,

altitude & speed tracking, survey performance and system maneuverability. Once final parameters were set, 95% of

flight time was performed autonomously, while the safety pilot takes control only in cases of an emergency. The

next paragraphs specify the autopilot performance and integration process on a task related manner.

o Waypoint Navigation

Nearly all flight tests included waypoint navigation as a sub-test, to ensure the system will comply with the objective

level criteria. The Tyto system has been tested to reach the required waypoint accuracy of 50 ft. Maximum climb

angle of the aircraft was limited to 40 degrees for safety considerations.

o Autonomous Search Area

Maximizing area coverage while taking into account turning radius and loss of height during sharp maneuvers, has

led the team to 'double back' scan method. Once implemented, this method proved highly effective, as the system

was able to scan the area with minimum altitude lost on turns and faster scanning time. To ensure no gaps between


adjacent scanning lines, image overlap ratio of 40% was utilized. The team tested a search area similar in size and

shape as SUAS 2014 and was able to complete the task in an average of 4.5 minutes.

o Interoperability Task

Interoperability required modification to the autopilot source code. For this reason, testing was regarded crucial and

performed repeatedly in simulations to ensure stability and reliability of the system before activation during flight.

Once integrated, the system retained an impressive 97% up time while down-time was related to server connection

errors and did not disturb the performance of the autopilot. The display was separated from the autopilot control and

operated via external GUI, to minimize chances of inference between programs. During flight testing, the system

maintained an objective level upload, download and display rates through full mission time.

o Sense, Detect and Avoid Task

Since this task requires autonomous path calculation, the development process included extensive simulations, both

in Matlab and Hardware In the Loop (HIL) simulations using Flight Gear simulator via the autopilot control

program (Mission Planner). These simulations included electrical emulation of autopilot sensors and actuators, as

well as simulated feedback and autopilot commands. This process helped simulate the physics of the problem at

hand as well as integrate the algorithm to the autopilot guidance. Flight tests were later conducted to validate the

simulations and fine tune parameters affecting UAV and algorithm performance. Tests indicated that the system is

able to avoid stationary obstacles at 50% success rate, moving obstacles were avoided 40% of the time. The relation

between UAV maneuverability and the distance between several obstacles has proven to be the major factor

predicting success of this task.

Figure 23 - Examples of SDA, Left – Flight data from Mission Planner, Right – Matlab simulation.

o Off-Axis Target

As a prelimiary step, the tracking algorithm was tested using a Matlab simulation as well as ground experiments.

Once satisfying results were obtained, flight tests were carried out, initially on 2014's airframe (Left image figure

25), followed by testing on the new airframe. Combinations of different altitudes, speeds and flights paths were

examined for optimal autonomous tracking time and accuracy. 30 flights of the system included off-axis imagery,

testing its abilities for worst case scenario conditions (500 feet distance from the target while flying 250 ft. AGL).

The system accomplished the imagery objective at 80% and payload autonomy objective at 70% success rate.

o Air-Drop Task

Thirty-five attempts were conducted for this mission, 10 of which while confronting the algorithm with unfavorable

wind conditions. Threshold in drop accuracy was secured on 28 attempts, while objective accuracy was acquired on

11 of them.


3.4 Payload System Performance

o Data Transfer

The team tested three different protocols for file downloading: HTTP, ZMQ and FTP. The ZMQ protocol was faster

by about 20% than the FTP and the HTTP during lab testing, however during flight testing, it had downloaded only

70% of the files. Considering this trade-off, FTP protocol was selected, which proved itself during several flights,

where payload communication was lost but had succeeded in re-gaining the connection in a short period of time. Connection between the EO camera, the OBC and the Imagery Console were tested for a smooth operation in the air

and reached 95% up time. Wireless connection was able to provide a strong signal and a satisfactory network speed

(5-50 MB/s), varying on the distance and angle between the UAS and the GCS.

o Search Area Task

This task was tested during 50 flights, with 85% success rate in identifications of different targets. Emphasis was

placed on team work and methodic communication protocol between flight management and imagery personnel.

Trainings were performed to allow a smooth and error free operation. The 'double back' scan method was used in 22

flight experiments. In addition, the GUI was modified several times to accommodate for imagery operator

requirements.

o Image Processing

Lab experiments and simulations were conducted in order to modulate and adjust the target recognition system.

These simulations contributed in discovering algorithm limitations months before Tyto was ready to fly. Basic tests

were conducted to examine connection between the camera and OBC (via USB cable) and between the Imagery

Console (via wireless network). In addition, experiments were conducted in order to test camera performance and

find better parameters for different weather and visibility conditions.

Several test targets were produced, each consisting of different shape, size, color and alphanumeric symbol. Targets

were distributed randomly on the training field and GPS coordinates and orientation were measured. Upon

completion of the flight test, results were compared with this measured data. Camera performance and

characteristics were also examined to find optimal camera parameters.

Figure 24 - Examples of test targets used during experiments.

o Rotating Turret Performance

Testing the rotating turret concept was done early in the design process, to prove its feasibility. A prototype turret

was built and fitted onto the 2014 system, enabling the execution of flight tests prior to Tyto’s manufacturing. Tests

carried out examined gimbal stabilization as well autopilot commands for pointing the camera and tracking specific

locations as required by the off-axis task.

Figure 25 – Left: Testing turret prototype on the Grey owl. Right: Ground test of the turret pointing algorithm.


4 Safety Considerations

In order to successfully develop a fully functional unmanned system, emphasis on safety was essential. During the

design, manufacturing and verification of the aircraft, continuous risk assessments were carried out in order to

minimize possible harm to personnel and equipment. These assessments dictated design restraints and guided team

operational policies, which enabled a safe and reliable operation of the system.

4.1 Operational Safety Criteria

Specific goals and guidelines of each experiment were prepared and reviewed both in advanced and immediately

before each flight. Prior to every flight test day, flight crew inspected the vehicle, with emphasis on airframe and

autopilot systems. Checklists were developed and expanded as needed throughout the year for safety protocols and

pre-flight readiness procedures. Main sections of several checklists include:

o Inspecting the fuselage, wings, tail and landing gear for fatigue and damage and replacing parts as

necessary.

o Inspecting the servo linkages (pushrods, horns, etc.) and control surfaces.

o Inspecting all batteries for damage and ensuring that they are fully charged.

o Inspecting the various payload components of the vehicle and ensuring that they are connected and secured.

4.2 Design Safety Criteria

All structural elements of the airframe were designed and tested with a minimum safety factor twice of that expected

during operational deployment. All electrical systems were designed with a safety factor of at least 1.5 times its

expected load. Repeated testing were performed to ensure structural and electrical performance behave as expected.

4.3 Safety Risks and Mitigation Methods

Careful analysis performed showed main risks revolve around control of the system and during maintenance. To

minimize risks related to loss of control, a safety rely switch (“Battleswitch” single pole – double throw 10 A rely

switch) was integrated in the aircraft’s control system. This safety switch separates the RC receiver and autopilot on

a hardware level, allowing manual control and servo operation in the event of an autopilot failure. The safety switch

also allows the external pilot to bypass the autopilot and take immediate control over the vehicle in case of any

unexpected aircraft maneuvers.

In the event of losing both the RC and the autopilot communications for more than 30 seconds, the system is

programmed to enter RTL mode, in compliance to section 9.3.6.3 of the SUAS rules sheet. In case a loss of

communication signal occurs for more than 3 minutes, the system is programmed to enter a flight termination

protocol, in compliance with section 9.3.6.4 and section 9.6.3.5 of the SUAS rules sheet.

To minimize the risk of any unanticipated engine operation on the ground (during maintenance, preflight checks,

etc.), the aircraft has an integrated safety switch, connected to the autopilot system, which cuts all signal to ESCs on

a software level. In addition a physical plug was added which, once removed, disconnects the ESCs from their

power source on a hardware level. The UAV also integrates a servo cutoff switch and a payload cutoff switch,

allowing the team members to develop and test specific components of the UAV, without the unnecessary operation

of other components and systems which are not required at that moment. Incorporating all of the aforementioned

safety mechanisms ensures a minimal loss of components and a secure flight.

5 Conclusions

This paper summarizes a work done by students from the Aeronautical and Electrical engineering faculties of the

Technion in preparation for the AUVSI SUAS 2015. Throughout the year, the TAS team designed, developed and

tested the Tyto system, which includes a completely new aircraft with a unique rotating turret system, a new

software for payload control and an enhanced software for flight control. Advanced algorithms were written to

accomplish new and challenging tasks while existing ones were improved to score better in core tasks. Redundant

safety mechanisms were incorporated in the system and safety procedures were developed to make sure the system

is safe for competition audience and personnel. Meticulous testing procedures and numerous ground and aerial tests

were conducted to confirm the UAS's capabilities and assured it's excellence in the competition.

technion aerial systems 2015 journal paper for auvsi ... · - 1 - tas – the technion aerial...

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