PENNSTATE
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Project PS 8
Semi-Autonomous Hand-Launched
Rotary-Wing Unmanned Air Vehicles
PI’s: Prof. Lyle Long and Prof. Joseph F. Horn
Tel: (814) 865-1172 and (814) 865 6434
Email: lnl@psu.edu and joehorn@psu.edu
Graduate Students:
Wei Guo, Ph.D. Candidate
Scott Hanford, M.S. Candidate
2004 RCOE Program Review
May 4, 2004
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Background / Problem Statement
Small rotary-wing unmanned air vehicles (RUAV) would be useful for
many military and civilian applications, especially if they were semiautonomous and very small. (e.g. “hover and stare” mission)
Technical Barriers
•Technical barriers for a hand-held semi-autonomous UAV’s:
• Control, Reliability, Ease of operation, Portability, Low cost, Weight,
Power, …
•R/C RUAV’s too difficult to fly, require extensive training
•Semi-autonomous control to minimize training and to allow the operator
to divide their attention
•Small, commercially available, low-cost sensors are desired but tend to
have lower performance and reliability
•Solution: Apply advanced control design and sensor fusion methods to
achieve reliable semi-autonomous operation
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Task Objectives:
Investigate feasibility of hand launched RUAV that are portable,
inexpensive, easy to operate, and capable of performing useful tasks
Approaches:
•Design and build avionics systems for small electric powered quad-rotor
RUAV’s
•Incorporate newest low cost / lightweight processors and sensors
•Investigate the use of both commercially packaged avionics systems and
custom-designed avionics
•Investigate several types of microprocessors and sensor systems
•Apply advanced controls laws / sensor fusion methods for reliable semiautonomous control
•Also investigating avionics systems for gas powered RC Helicopters
Expected Results:
• Small COTS-based inexpensive autopilots
• Flight test results and evaluation of the different systems
Control of a Quad-Rotor UAV
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Motors and
Gear Reduction
Rotors
Power, Electronic
• Four variable-speed motors control
Controller, Payload
each rotor individually
• Front and aft rotors rotate opposite
direction of left and right
• Four-axis Control:
• Roll – vary relative speed of left and
right rotors
• Pitch – vary relative speed of fore
and aft rotors
• Collective – vary speed of all rotors
simultaneously
• Yaw – vary relative speed of
clockwise and counter-clockwise
rotors
• Highly maneuverable, minimal
cross-coupling, simple control
mechanisms
• Low rate damping, requires
electronic stability augmentation
Processors and Software
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We are currently evaluating all of these
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Processor
Manufacturer
(website)
Speed
Memory
Floating
Point
Math?
Ether-net
Power
(mA)
Software
Cost
with
Board
($)
Javelin
Stamp
Parallax
(www.parallaxinc.com/)
(Ubicom SX48AC)
8 KIPS
64
KB
No
No
60
Java
(subset)
89
BASIC
Atom
Pro
Basic Micro
(www.basicmicro.com)
(Hitachi 3664)
100 KIPS
34 KB
Yes
No
80?
Basic
140
PIC
MicroChip
(www.microchip.com)
(PIC18F452)
40 MIPS
1800 KB
Yes
No
80
Basic
or
C
100
TINI
Board
Dallas Semiconductor
(www.ibutton.com/TINI)
40 MHz
1 MB
Yes
Yes
250
Java
100
PC-104
JumpTec
( www.adastra.com )
(Intel PC Processor)
266 MHz
128 MB
Yes
Yes
1000
Basic,
C,
Java
1000
VIA
VIA MicroATX
( www.via.com.tw )
1000 MHz
1 GB
Yes
Yes
1000
Basic,
C,
Java
200
Very fast, can use c, light weight,
inexpensive, …
Others: Motorola, HandyBoard, Atmel,…
Processors & Boards
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PIC
Development
Board
PC-104
Micro ATX
4 in.
(includes
breadboard
for sensors)
3.5 in.
Too heavy,
complicated &
power hungry
7 inches
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Sensors
All of these devices are currently being tested.
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Analog Devices MEMS sensors
ADXL 202/210 2-Axis Accelerometers
Range: ±2g or ±10g
Power: 0.6 mA @ 3-5 VDC
RMS Noise : < 0.002 g for 10 Hz Bandwidth Option
Size: Less than 0.5 gram, 0.05 cm3
ADXRS 150/300 Rate Gyros
Range: ±150 or ±300 °/sec
Power: 6.0 mA @ 5 VDC
°/sec/Hz1/2
Can survive 1000 g shock
Noise : 0.05
Size: Less than 0.5 gram, 0.15 cm3
Crista IMU
Uses Analog Devices MEMS sensors, with 3 gyros and 3 accelerometers integrated into a
single unit with serial interface. Unit weighs 37 grams with enclosure. This is a relatively
expensive item. Very easy to use and integrate.
Honeywell HMR 3100 Digital Compass
One of the smallest and cheapest units available.
Size: < 1.5 grams Power: 0.2mA @ 3 VDC
RMS Accuracy: < 5 deg
Novatel SuperStar II GPS Receiver
At 22 grams, one of the smallest and lightest units available.
Size: 22 grams
Power: < 0.5 W
Accuracy: < 5 m CEP (< 1 m CEP DGPS)
Currently looking into SONAR altimeters
Summary of Quad-Rotor Systems PENNSTATE
Currently Under Development
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1. PC/104 Based System
PC system is easy to program and to integrate with other hardware. Also uses
Crista IMU, and wireless network adapter for communication. Too heavy and
power consuming but will be use for rapid prototyping of control laws.
2. PIC Microcontroller System
Very good performance relative to weight, power consumption, and cost.
Programmable in C. Requires more electronics expertise to hardware.
3. BasicAtom System
Lightweight, low power consumption. Program in BASIC. Can perform
floating point math. Already integrated with MEMS sensors and motors.
4. Javelin Stamp System
Lightweight, low power consumption. Program in JAVA. Limited to
integer math. May not be feasible to implement control laws.
5. Custom-Designed System
Custom-designed board integrates processor and all sensors and servos.
Optimal in terms of weight and power. Requires EE expertise.
PC Based Avionics
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NetGear Wireless
Network Adapter
HMR3100 Digital
(20 grams)
Compass
(1.5 grams)
PC/104 is
relatively heavy
and power
consuming
Crista IMU
(37 grams)
SuperStar II GPS
Receiver (22 grams)
Remove
existing
electronics
Draganflyer III
Photo from www.rctoys.com
Intel 166 MHz
Pentium MMX
PC/104+
(110 grams)
Bench Test Integration of PC
Based Avionics for Quad-Rotor
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Initial bench test integration with
PC-104 on the development board.
• Testing PC-104 system on a
DraganFlyer III airframe
• System is heavy will need external
power source (using tether)
• Using Linux operating system with
256 MB ChipDisk storage
• Wireless network adapter
communication with ground station
• Crista IMU from Cloud Cap
Technology (plug and play)
• SSC board generates PWM signals
• Hobby ESC units control motors
• System is easier to program and
integrate but is too heavy and
power consuming for practical
application.
• Can be used for initial testing of
control laws before transitioning to
lighter microprocessor
PIC Microcontroller Avionics
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MEMS-based ADXL202E Accelerometer
HMR3100 Digital
MEMS-based ADXRS150 Gyro
Compass
Wireless Video Camera
4-channel receiver
PIC Microcontroller or
other lightweight
microprocessor
CMC GPS Receiver
Quad Rotor
Draganflyer III
Photo from www.rctoys.com
Remove Existing Electronics
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BasicAtom Test Platform
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Batteries
Electronic
Speed
Control
Serial
PWM
Controller
280 size
Motor
9 inch
prop
BasicAtom
Processor
Accelerometer
Gyroscope
Close-up of Processor and
Sensor Boards
Gyroscope
Accelerometer
Hitachi
3664
Processor
1 inch
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Accelerometer Output
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X accel (in g's) = 0.000000
X accel (in g's) = 0.035693
X accel (in g's) = 0.014277
X accel (in g's) = 0.963735
X accel (in g's) = -0.513993
X accel (in g's) = 0.064248
X accel (in g's) = 0.021416
Acceleration
in the positive
x-direction
Gyroscope Output
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Z ang velocity (degrees / sec) = 0.000000
Z ang velocity (degrees / sec) = 0.000000
Z ang velocity (degrees / sec) = 14.062715
Z ang velocity (degrees / sec) = 47.266346
Z ang velocity (degrees / sec) = 28.516060
Z ang velocity (degrees / sec) = 4.296940
Z ang velocity (degrees / sec) = -1.171893
Z ang velocity (degrees / sec) = -18.359653
Z ang velocity (degrees / sec) = -26.953535
Z ang velocity (degrees / sec) = -37.109940
Z ang velocity (degrees / sec) = -37.109940
Z ang velocity (degrees / sec) = -36.328678
Clockwise
Rotation
CounterClockwise
Rotation
PSU Custom Designed Autopilot
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Motor controllers
3 EE PhD students from Long’s
UAV course developed a
custom-designed avionics
board using the MEMS-based
sensors. Board was designed,
manufactured, and is now
undergoing testing and
programming. Should be
completed Summer 2004.
Gyros
Atmel Processor
Quad-rotor helicopter
Accelerometer
Control Design
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Incremental Approach
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1.Stability augmentation using classical control laws (PID Control)
• Initially use rate gyros only
• Rate command response type
• Goal: Improve handling qualities over existing Draganflyer III
2.Enhanced stability augmentation
• Incorporate 3 linear accelerometers
• Achieve attitude command response type
• Goal: Inexperienced pilot can learn to fly in a couple of days
3.Semi-autonomous mode
• Add GPS receiver, digital compass, altimeter
• Translational rate command response type
• State estimation algorithm needed (Extended Kalman Filter)
• Goal: Anyone can fly with very limited training
4.Reliable autonomous flight
• Way point navigation
•Additional sensors for fault-tolerant control
• Goal: Reliable autonomous operation
Quad-Rotor Dynamics
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• Simplified non-linear model of quad-rotor
dynamics near hover (ref. Huzeman et al)
• Can identify empirical damping constants
from flight test data
• Inertia parameters identified from swing
test
• Dynamics are simulated in SIMULINK
• Developing preliminary design of simple
PID controllers
• Autonomous control will require accurate
state estimator
Control Law Design
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SIMULINK Diagram of Simple PID Controller
KDroll.s
Roll Contoller
1
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MIXING
0.1s+1
KCMDroll
KProll
Kvolt
Lateral Input (%)
1
Motor #1 - Aft
(Volts)
2
10*KIroll
Roll Rate
(deg/sec)
10s+1
KDpitch.s
Pitch Contoller
0.1s+1
Kvolt
2
Motor #2 - Right
(Volts)
3
KCMDpitch
KPpitch
Long. Input (%)
4
10*KIpitch
Pitch Rate
(deg/sec)
10s+1
Kvolt
3
Motor #3 - Fwd
(Volts)
KDyaw.s
Yaw Contoller
5
0.1s+1
KCMDyaw
KPyaw
Kvolt
Yaw Input (%)
4
Motor #4 - Left
(Volts)
7
6
10*KIyaw
Yaw Rate
(deg/sec)
10s+1
KCMDvert
Collective Input (%)
Heave Contoller (Open Loop)
Preliminary Observations on
Control Law Design
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• Small microcontrollers should have sufficient processing power to
implement PID control
• Linear dynamics of quad-rotor in hover are very simple
• Minimal damping, behaves like inertial system
• Minimal cross-coupling
• Can use classical control design methods
• Main issue with MEMS inertial sensors is bias and drift
• Cannot use pure integral action, need to wash out integral signal
• Accurate Attitude estimation is not feasible with inertial measurements
alone
• Not a major issue with pilot-in-the-loop control
• Autonomous flight will require state estimation with assistance from GPS,
Digital Compass, and possibly altimeter. Plan on using Extended Kalman
Filter.
• Can small microcontrollers perform accurate state estimation in real-time?
• Can we achieve sufficiently accurate state estimation with these sensors to
achieve autonomous flight?
Fault-Tolerant UAV Flight Control
Long term objective
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Autonomous UAV’s are vulnerable to the failure or degradation of sensors
Incorporate multiple inexpensive accelerometers and take advantage of
kinematical coupling to derive rate and attitude information
Use an Extended Kalman Filter (EKF) to fuse sensor data
Flight Control
Computer
Covariance
Projection
Kalman Gain
Calculation
Covariance
Update
A
H
R-50 Non-linear
Model
Sensor Model
EKF Non-linear
Model (lower order,
10% error in aero
coefficients)
Control
Input, u
Accelerometers
Projected State
Estimate, x-(tk)
Non-linear
Measurement
Model
FCC
+
+
z
+
-
Measurement noise
covariance estimation
R
Updated State
Estimate, x+(tk)
New UAV Course
PENNSTATE
PSU Funded, Taught by Prof. Long, www.personal.psu.edu/lnl/uav
Fall 2003. 31 students
Every student built and flew
airplanes. Guest lectures on
UAVs.
1 credit.
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Spring 2004. 28 students
Students worked in teams of 5 to build
large 80 in. span aircraft. Installed
wireless video cameras, onboard flight
data recorders, performed flight tests.
2 credits.
Onboard Wireless Video Camera PENNSTATE
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View of area around flying field.
At approximately 400 feet altitude.
Cars
Us
Landing strip
Onboard Flight Data Recorder
Altitude (ft) Speed (MPH)
(Crash in UAV course, April, 2004)
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time, seconds
100
90
80
70
60
50
40
30
20
10
0
400
405
410
415
420
425
405
410
415
420
425
600
500
400
300
200
100
0
400
300
250
Pilot Input
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200
150
100
50
0
400
405
410
415
420
425
Elevator lost, but pilot still trying…
Onboard “blackbox” used for
flight testing, but also contained
crash data. (in high-speed dive
the elevator failed and pilot lost
control)
Commercial R/C Autopilot
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Piccolo Autopilot
by Cloud Cap Technology
Manufacturer of this autopilot system
($15,000) very interested in our RCOE center
and our UAV course. (gave us a free system
for the course for next year)
We hope to work with them to make this
suitable for rotary wing UAV’s
Moving map displays from ground station
Can be coupled to flight simulator
•
•
•
•
•
•
•
Weighs 212 grams, consumes about 300 mA at 12V
Sensors include IMU, GPS, Pitot and static pressure ports
Can control all primary servos plus additional servos
UHF communication link with ground station
Some user programmability, set up for PID control
We have tested units in Hardware-in-the-loop simulation
Test initially on fixed-wing then transition to helicopter
Accomplishments
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2003 Accomplishments
• Program awarded in Fall of 2002
2003 Accomplishments
• Evaluated wide range of processors and sensors
• Coupled and programmed MEMS-based gyros, accelerometers to microcontroller
• Demonstrate feasibility and document limitations of systems
• Acquired commercial autopilot system and began evaluation
• Developed and taught popular new UAV course
Planned 2004 Accomplishments
• Will concentrate on incorporating PIC processors
• Will couple all components (rotors, sensors, processors, …)
• Will develop PID control software using C language
• Will demonstrate feasibility and document limitations of system and
sensors
• Will also attempt to couple systems to traditional R/C helicopter
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Technology Transfer Activities:
• Began interacting with CloudCap Technology.
• Prof. Long is on organizing committee for 1st AIAA Intelligent
Systems Conference (Chicago, Sept., 2004)
• Prof. Long is Editor-in-Chief of Journal of Aerospace Computing,
Information, and Communication (JACIC) ( www.aiaa.org/jacic )
Leveraging or Attracting Other Resources or Programs:
Received $ 50K to develop R/C aircraft course.
( http://www.personal.psu.edu/lnl/uav/ )
2002 DURIP grant includes $20K for UAV research
Recommendations at the 2003 Review:
None.