The Unmanned Systems Lab focuses on developing autonomous aircraft for various applications such as precision agriculture, 3D reconstruction, and disaster response. The lab has 8 graduate students, 30 undergraduates, and 1 technician working on projects involving fixed-wing, rotary-wing, and morphing wing aircraft carrying payloads like cameras for imaging and radiation detectors. Some key projects include using imaging from fixed-wing aircraft to monitor crop health for corn and tobacco, 3D reconstruction of terrain for applications like path planning, and conceptualizing the use of a 90kg helicopter to conduct radiation monitoring and sampling after disasters.

1. Unmanned aircraft design, development and
applications at the Unmanned Systems Lab
Kevin Kochersberger
Mechanical Engineering
kbk@vt.edu 540-231-5589
1

2. Outline
Lab overview
Morphing wing aircraft
Precision agriculture
Image-based 3D reconstruction
Theory
Applications
Robotic collection
IED-detect and path planning
Terrain feature mapping and classification
Organic VTOL disaster response
2

3. Brief overview of the lab
The Lab consists of a 3,000 sq. ft. of high
bay area, graduate student office space,
and mechanical and electrical shop
resources
8 graduate students
30 undergraduates
1 lab technician
3

4. The Unmanned Systems Lab
The Lab’s focus is in autonomous aircraft
Sensing payloads carried by fixed and rotary
wing aircraft that weigh 1 – 90 kg
Novel aircraft design
Novel control strategies
4

5. Wing morphing is based on
piezoelectric actuator technology
Macro Fiber Composite (MFC) actuators show large strains
compared to competitive piezoelectric actuators
Note that these operate at high
voltages ~ 1500 V
Maximum Strain-Stress Outputs from
several active materials. [Williams]
5

6. Morphing concept
In a bi-morph configuration, substantial bending
deflection can be obtained with the MFC actuators
This bending behavior is suitable for aerodynamic
control
Positive Actuation
Negative Actuation
6

7. The original morphing aircraft
design proved the concept
The 2007 aircraft design was originally created to explore
both pitch and roll control using MFC technology
Goal was to achieve high efficiency and high bandwidth
It flew, but barely!
7

8. A study was conducted to determine
better morphing configurations
2-D wind tunnel testing was conducted on a variety of morphing
configurations to find an optimal design
One thick wing design is a thin morphing
surface with a bottom wiper that maintains
contact with the top surface
8

9. Top and bottom bimorphs (4
actuators) allow a CL range > 1.2
9

10. 2-D wind tunnel results: higher delta
CL and lower drag with morphing
10

11. Hysteresis Modeling
Inverse Hysteresis Operator (IHOp) used to provide feed-forward
control for MFC operation
It basically curve fits the forward and backward linear functions to
compensate for MFC behavior
Without IHOp the pilot has a difficult time controlling the aircraft
Command
11

12. High voltage conversion
Custom electronics used to power the MFC actuators were developed via a
Phase II SBIR with AVID LLC
The high-voltage system is designed to power 2 bi-morphs
Command
IHOp
compensation
2-bimorph HV converter
12

13. Full wing morphing
UAV applications in targeting can benefit from high bandwidth,
agile handling
Servo driven ailerons lack reliability and can be difficult to integrate
into a thin wing
What if we could simultaneously achieve camber change and a
delta angle of attack to achieve higher roll rates?
Conventional controls
Wing morphing achieved with
piezoceramic actuators
13

14. Full wing morphing
Entire wing planform morphs instead of discrete control surfaces
Simulations in FEA performed to determine best configurations
Performance confirmed in flight test with roll doublets
14

15. Flight Comparison: Roll
Coefficient
Measure Roll Rate: 80% Amp, 0.2s Duration
MFC roll coefficients from
simulation verified in flight test:
300
P(deg/s)
200
100
0
Flight:
Simulation:
-100
-200
-300
0
0.2
0.4
0.6
1
0.8
1.2
1.4
Croll = 0.036
Croll = 0.055
1.6
t(s)
input signal
100
Servo
MFC
MFC Thin Wing
50
0
-50
-100
0
0.2
0.4
0.6
1
0.8
1.2
1.4
1.6
1.8
t(s)
From Ohanian, O, David, B., Taylor, S., Kochersberger, K., Probst, T., Gelhausen, P.,
Climer, J., “Piezoelectric Morphing vs Servo-Actuated MAV Control Surfaces, Part II:
Flight Testing,” to be presented at the AIAA ASM 2013 Conference, Grapevine, TX, Jan 9,
2013. Images courtesy of AVID, LLC
15

16. Frequency Response:
MFC vs. Servo
From Ohanian, O, David, B., Taylor,
S., Kochersberger, K., Probst, T.,
Gelhausen, P., Climer, J.,
“Piezoelectric Morphing vs ServoActuated MAV Control Surfaces, Part
II: Flight Testing,” to be presented at
the AIAA ASM 2013 Conference,
Grapevine, TX, Jan 9, 2013. Images
courtesy of AVID, LLC
Dramatic increase in bandwidth with MFC
MFC has small phase lag in comparison to servo-driven controls
16

17. Swashplate-less rotorcraft
flight control
Use blade torsional harmonic
excitation via MFC to phasecontrol asymmetric lift and
produce roll and pitch
moments
The blade torsional frequency
must be tuned to the 1 per rev
frequency
(Of course) It allows trimming
in forward flight

18. UAS for the Monitoring of Crop Status and Stress
Donnie Rogers
Graduate Research Assistant
Virginia Tech – Mechanical Engineering
Unmanned Systems Lab
18
Copyright 2013, Donald Rogers

19. Overview of ag work
The literature review focused on three main topics as they relate
to the crop monitoring project:
1. Spectral Reflectance of Vegetation
 Reflectance data is a primary tool for
agronomic remote sensing.
 Led to the formation of ratios of
reflectances, aka vegetation indexes.
Most commonly NDVI.
 NDVI suffers from soil background
interference.
RNIR = NIR reflectance,
RRed = Red reflectance
2. Advantages of UAS in Crop Monitoring
3. Remote Sensing of Corn and
 Low altitude flights results in high resolution
Tobacco
data.
 Can collect data more frequently than
manned aircraft or satellite.
 Affordable and multi-purpose.


Strong correlation between NIR
and Green reflectance for Corn
nitrogen fertilizer status.
Strong correlation between NDVI
and tobacco nutrient status.
19
Copyright 2013, Donald Rogers

20. Imaging Payload
The payload developed for the project uses a multi-spectral
camera to gather images in both the Visible and Near-Infrared
bands.
Payload
Visible
NIR
On-board
Computer
Camera
(JAI AD080 GE)
Switch
Images from both spectrums will be combined with software to estimate crop health.
20

21. Flight tests at Kentland Farms
The purpose of the flight was to determine the applicability
of unmanned aircraft for crop health monitoring
Corn was chosen as
the crop for initial
testing
Yamaha RMAX
carries EO and NIR
cameras
21

22. Ground Image Segmentation &
Results
A two stage segmentation process was used to isolate the corn from
background, and then NDVI was used to examine the N effect on yield
GNDVI
Mask
NDVI
Final Image
22
Copyright 2013, Donald Rogers

23. Statistical Results
A Least Significant Difference (LSD) test has shown that three of the four
Nitrogen treatments are detectable by the system.
170
Mean Pixel Intensity (MPI)
160
150
Each treatment was sampled 90 times
from the flight images.
140
130
The LSD test was conducted with a 5%
margin of error.
120
110
100
90
N Treatment Means
Best Fit Curve, R2=0.97
80
70
50
100
150
200
Nitrogen Rate (kg/ha)
250
Our testing has shown diminishing returns as Nitrogen Rate exceeds 150 kg/ha
23

24. Grain Yield Correlations
Grain yield totals were measured for each N rate treatment after
harvest and compared to the calculated average NDVI.
A correlation is observed with
NDVI suggesting yield
estimation is possible for HR
hybrid
Weaker correlation with AM
hybrid due to on outlier
The system has the potential for estimating grain yield
24
Copyright 2013, Donald Rogers

25. Tobacco Imaging Study
This series of flights performed in September on late season
tobacco were intended to explore the use of several sensors:
Ultraviolet (300 – 400 nm)
Visible (400 – 700 nm)
Near Infrared (700 – 1000 nm)
Long wave IR (8000 – 12,000 nm)
Flight tests were in partnership with
American Aerospace Advisors, Inc.
RMAX flown near South Hill, VA
under COA authorization
25
Copyright 2013, Donald Rogers

26. Tobacco Survey Results
Two neighboring 8-row blocks appeared to have different levels of
growth (most likely, different hybrids)
NDVI for the 1st 8 rows = 0.209
NDVI for the 2nd 8 rows = 0.195
Although the NDVI levels are overall low (as expected due to a large
number of bleached leaves), the results still show a significant difference.
Visible
NDVI
26

27. Bleached Leaf Segmentation
A segmentation method was developed to isolate both the green
tobacco leaves and the bleached leaves from background noise.
Segmentation is possible using the four
primary content groups in the visible image
Group
Green
Tobacco
Bleached
Tobacco
Bare Soil
Shadowed
Soil
The segmentation should output two images:
1st – Green Leaves Only
2nd – Bleached Leaves Only
RGB Value
Distinguishing
Feature
94, 95, 67
Green pixel value
significantly greater
than blue value
148, 156, 166 Brightest pixel values
in image
58, 55, 56
Low overall pixel
intensity
14, 16, 15
Low overall pixel
intensity
Set a Green Level Threshold
Set Bright Level Threshold
“Noise Floor” established
The distinguishing features of each group make simultaneous segmentation possible.
27
Copyright 2013, Donald Rogers

28. Leaf Segmentation Results
The leaf segmentation method was successful in isolating both
groups of tobacco leaves from each other and background noise.
Original Image
Green Leaves Only
Bleached Leaves Only
200628 pixels
49362 pixels
Trial segmentation results provided us with a
estimate of bleached leaf concentration –
useful to determine time of harvest
28
Copyright 2013, Donald Rogers

29. UV and LWIR Imaging Results
Data Processing:
– UV and IR JPEGS appear to have been distorted by motion blur caused
by helicopter vibration
– Individual visible wavelength JPEGS were clear, but adjacent sequential
frames showed differences in magnification and orientation which
affects stitching
Ultraviolet
Visible
2013 American Aerospace Advisors Inc. All Rights Reserved
Long Wave IR
29

30. Image-based 3D reconstruction
•
Image-based 3D reconstruction is ideal for robotics
•
Cameras are lightweight payloads
•
Cameras can be easily attached to robotic platforms (e.g.
quadrotors)
•
Several vision techniques can process the images
•
Reconstructions include colors and camera locations
Kentland farm equipment shed
PMVS meshed model
30

31. Stereovision Basics
Stereovision is 3D mapping technique that utilizes two or more
cameras, which are separated by some distance, to determine
point correspondences in 3D.
Image from Bradski and Kaehler
31

32. Stereovision Basics
The vertical resolution of the 3D points is directly related to the
distance between the cameras and the distance from the
cameras to the observed objects.
Resolution for 1.5 m baseline and focal
length of 8mm for 1600x1200images at
the center of image.(X = Y = 0)
While geo-referencing a single pair of
images is easy, area scans pose a challenge
32

33. Application
• A 5’ baseline system is currently
used for aerial imagery on the
RMAX
• Methods developed to correct for
vibration-induced errors
• Images are acquired with 80%
overlap to generate 3-D terrain
mosaics
33

34. Stereo examples
34

35. Structure-from-Motion (SfM)
methods
Bundler SfM output
PMVS output
Bundler and PMVS output
Improved accuracy over stereovision
35

36. Some examples
Stockpile mapping
Volume estimation
Structural defect detection
36

37. Anomaly detection in roadway via
aerial imagery and 3D reconstruction
Three disturbed areas were successfully identified and mapped using the Unmanned
Systems Lab Yamaha RMAX helicopter equipped with the USL dual camera imaging
boom. The gravel piles ranged in height from 3” to 10”. Two sets of 3D reconstructions
were used for anomaly detection.
9’
Gravel piles detected
as anomalies
37

38. UAV path planning for ground
vehicles
Area traversal of ground vehicles could
be greatly improved if coupled with a
UAV imaging system
Weighted A* or RRT* methods are applied to 3D
maps from aerial imagery to find optimal paths in
unstructured environments
Roadway detection algorithms used with
learning methods to find obstructions for
optimal routing
Mosaic
map
K-means
segmentation
Pre-erosion
Final connected
K-means
paths
membership
38

39. 3-D Scene Understanding
Challenge: classify 3D environments efficiently for
damage assessment, threat assessment,
ecosystem monitoring, etc
Original research is focusing on urban environment
classification, however any environment of interest
can be used to train for classification:
Hurricane damage
Tornado blowdown
Vehicle disruption
39

40. 3-D Scene Understanding
The lab is using a hexacopter to carry a 48” wide baseline imaging
system to collect nadir and side view imagery
Performance metric
Value
Endurance
Weight (with camera boom)
Max speed
Autopilot
Arduino
Stereo broom description
Info
Cameras
Canon A810
Weight
Max frame rate
Max resolution
Control
Arduino
40

41. Training and classification
Use of k-means clustering breaks the scene
into 3D “super voxels” based on location only
Further classification occurs on the super
voxels for point-ness, linear-ness and
surface-ness
Height is added as a feature
A conditional random field (CRF) is used as
the framework for training
Supervoxels are then grouped from training
into the following classes:
Poles
Ground
Buildings
Vehicles
41

42. The resulting classification
has many uses
Provides spatially relevant data to support other analyses
Ecosystem health
monitoring
Number of damaged buildings,
roads post-hurricane
42

43. Organic Disaster Response
Mid-level damage assessment can be facilitated with a 90 kg – class
autonomous helicopter
1
2
Uncertain terrain
or topology
3
Unstable structures
4
4
Blocked ingress and
egress routes
Health concerns
(particulates)
5
3
1
5
2
3
Radiation
43

44. Flight Operations
A mission architecture based on a 90 kg helicopter / 18 kg payload is
designed to maximize information while keeping flights to a minimum
Mapping 1
• High Altitude Image Capture Flight
• Flight of total target area used to collect high level information
• 80 meter flight altitude
Mapping 2
• Combined Low Altitude Image Capture and Radiation Scanning Flight
• Flight of specific target area used to collect more accurate information
• 40 meter flight altitude
• Also gathers radiation spectra and gross gamma ray event counts
Localizing
• Radiation Source Localization Flight
• Constant radiation intensity following to find radiation contours
• 40 meter flight altitude
• Post-processing to determine best estimate of radiation source location
Sampling
• Sample Collection Mission
• Lower Ground Sampling Robot into point of interest
• Use Tele-operated Ground Sampling Robot to locate and collect samples
• Retract robot and return to base for sample analysis
= Optional
6
44

45. Radiation detection capabilities
• Oct, 2009 test demonstrated detector
function with a single, collimated
source
• June, 2010 test with uncollimated
sources
GPS coordinates, rad counts and spectral data
transmitted real-time to the ground control station
Scan Path
37.1974
37.1972
Latitude (deg)
Background-Subtracted Gamma Spectrum
600
500
Channel Counts
•
400
300
37.197
37.1968
37.1966
37.1964
37.1962
200
37.196
100
37.1958
0
0
100
200
300
400
500
600
Channel No.
700
800
900
1000
-80.582
-80.5815
-80.581
-80.5805
-80.58
Longitude (deg)
45

46. Radiation detection mission
June, 2010 test: Successfully mapped single
and multiple omni-sources at Savannah River
National Labs at 40 m and 60 m AGL
The RMAX flew 6 hours
in three days of
mapping in 98º F heat –
only a single software
update had to be made
during the radiation
mapping missions
which resulted in a 20
minute delay
Single and dual radiation source maps (Sandia NL)
46

47. Use of spatially variant deconvolution
for source localization
Radiation intensity measurements are correlated with height above
ground using a laser rangefinder
Maximum likelihood estimation (MLE) used in deconvolution
The ability to resolve close proximity strong and weak sources was
demonstrated in flight tests
0.85 Ci Ir source and a 0.03 Ci Co source intensity plots. The
sources are spaced 20 m apart
47

48. Radiation detection mission
An optional radiation localization flight can be performed to
more accurately locate a source of radiation
PID-implemented contour
following to localize sources
Particle filter method used to
localize a single source
12
48

49. Ground sample collection
robot
A helicopter-deployed ground sampling robot was developed to retrieve
radioactive samples
Both chunk and particulate samples have been considered for collection
The system (robot and winch) weigh 10 kg
Tether deployment from the helicopter allows pinpoint
delivery of sampling assets
+
=
49

50. Remote ground robotic
operations using a virtual display
Map 3D terrain
with vision system
Plan traversability
path using A*
Track robot
Robot tracking occurs by
referencing the robot to
features which are
mapped to 3D terrain
Teleoperator sees an accurate
virtual 3D environment during
operation
50

51. Intelligent radio repeating
using mapped terrain and A*
Note: The blue lines denote the strongest radio link. Notice how the best link is the radio repeating
link between the ground robot, helicopter, and ground robot.
51

52. Intelligent radio repeating
using mapped terrain and A*
Note: The blue lines denote the strongest radio link. Notice how the best link is the radio repeating
link between the ground robot, helicopter, and ground robot.
52

53. Test data – Intelligent radio
repeating
53

54. Conclusions
The positive uses of UA will be adopted by
a suspicious public (as all promising
technologies have been adopted in the
past)
UA toys will outrun any other attempt at
promotion of the technology
UA have become the future of aviation and
can co-exist and support manned flight
operations

55. Questions?