demining robotics case study - metr 4202 advanced control & robotics - l13-casestudy.v2
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Demining Robotics Case Study
METR 4202: Advanced Control & Robotics
Drs Surya Singh, Paul Pounds, and Hanna KurniawatiLecture # 13 Part 2 October 22, 2012
http://itee.uq.edu.au/~metr4202/ 2012 School of Information Technology and Electrical Engineering at the University of Queensland
RoboticsCourseWare Contributor
First Off: Bang Bang Control!
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Smart for one, dumb for all
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Landmines:Worldwide Problem
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Gryphon: Mine Scanning Robot
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Generalized Mine & Placement
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(Antipersonnel) Landmines are Challenging
Terrain Diversity
Counter-thwart mechanisms False Positive Rate >100:1
Variable & Changing
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Ex: PMN-2 [1]
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Ex: PMN-2 [2]
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Ex: PMN-2 [3]: Mechanically Intricate
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Land Mines: Highly Variable
Little metal
High-sensitivitydetectors / instruments
Highly Variable(Example: PMN-2):
3-stage detonation
Anti-thwart
All mechanical
Poor constructiondetectors / instruments
Focus on manipulatingsensor instead ofcomplex sensing ???,,,
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Clearance & Breeching
Breeching: Line
Demining: Area
International Mine Action Standards (IMAS)
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Humanitarian Demining Process
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Humanitarian Demining Process
1. Level 1: Specification
Rough minefield location
2. Level 2: Clearing
Heavy machines(e.g., flails, grinders, rollers, ploughs, and sifters)
~ 90% clearance
3. Level 3: Confirmation
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Level-3: Confirmation
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Sensor Mobility Is Critical
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Back to Gyrphon
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Cleared area
Minefield
All terrain vehicle
Counter-weight
Stereo vision camera
Optional ground-penetrating radar
Metaldetector
Network camera
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Gryphon Schematic
Wrist joints
z
x
Manipulator
SensorTerrain
Camera
Ground frame FG
Joint1 (yaw)
Joint2
Joint3
Compliant base
Manipulator frame FMCounter-weight
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Gryphon: Comparison to other tracked robots
Mechanical Robustness
ControlRobustness(Au
tonomy)
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Demining Terrain Not Always Rough
Unexploded BLU-97
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Remote Terrain: 30% > 30 Grade
Source: US Army, Logistcal Vehicle Off-Road Mobility, 1967 (c/o Prof. K. Waldron)
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Multiple Inaccuracies
Sensing:
ATV Suspension:
Screenshots courtesy M. Freese, Tokyo Tech.
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Operational Overview
Heightmap DetectingTerrain
scanning
Screenshots courtesy M. Freese, Tokyo Tech.
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Terrain Modeling & Following Overview I. Terrain Mapping II. Terrain Model
III. Path Generation IV. Scanning
Conditional PlannerFiltering & Stitching
Height map expansion& Path generation
Input-shaping
V. Evaluation& Marking
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Terrain Mapping
Stereo depth maps (Pont Gray Bumblebee)
Kinematic calibration corrections
Ex1: Grassy areaEx2: Grassy areawith hill or bump
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Terrain Geometry Model: Heightmap Expansion
Scanning gap
x
z
x
z
As Surface Normals:
Raw Map Raw
Model
Filtered
ModelExpanded
and Offset
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Terrain Geometry Model:
Conditional Planar Filter
Planarity: Found from plane eq. residuals for a surface
patch
Filter type and strength varied based on this
Goal: Reduce noise without feature degradation
Planarity measure (for local patch)
Gaussian-smoothed value
Median value *
*
+1
+Filtered
value
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Terrain MapModel: Conditional Planar Filter
ComputeNormals
Map:
TerrainMesh
Model
Applyfilter(s)
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MapModel (II): Height Map Expansion
Envelope expansion:
Fenv = Fterr + scanning gap
Performed along the normals, more than vertical axis
addition:
Scanning gap d
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MapModel (III): Height Map Expansion
Terrainenvelope
Model of
terrainScanning gap
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xz
y
Calibration Model
Height (z) Calibration:
Plane (x-y) Calibration
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Effect of Overall Calibration Matrix
-10
-5
0
5
10
15
20
25
30
0 0.5 1 1.5
Circular distance [m]
Deviation
from
idealpath
[mm]
With Ove rall Calibration Matrix
correction
Without Overall Calibration Matrix
correction
Scanning speed: 100 mm/s
Scanning gap: 100 mm
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Path Generation
x-y: Scanning Scheme
Joint-space/Work-space?
Reduce excess work
z: Terrain Sampling (z)
Sample corresponding
point based on the local
patch & normals
ATV
Manipulator Detectorzpath fenv(xpath,ypath )
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Path Generation (II)
Orientation: Advanced Terrain Following
(a) (b) (c)
(d) (e)
Controlpoints
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Contour Following
DetectorEnvelope
Terrain
Potential collisions
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Robust Control:
Command Shaping for Vibration Reduction
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Command Shaping
Original velocity profile
Input shaper
Command-shaped velocity profile
Velocity
Velocity
Time
Time
Time*
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Command Shaping in Position Space
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Command Shaping:
Zero Vibration and Derivative
2,1i
For Gryphon: At0=1.5 [m] At1=3.0 [m]
2.32 1.81Axis 1
0 0
3.3 3.0Axis 2 & 3
0 0
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Terrain Modeling:Find a good model to characterize
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Scanning on ~ Level Terrain - Measurements
-10
-5
0
5
10
15
20
0 0.5 1 1.5
Circular distance [m]
Deviation
from
idealpath
[mm]
Unfiltered
Gaussian filtered
Conditional Planar filtered
Scanning speed: 100 mm/s
Scanning gap: 100 mm
Manipulator
Laser range
finder
Scan pass
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Scanning on Rough Terrain - Measurements
-40
-30
-20
-10
0
10
20
30
0 0.5 1 1.5
Circular distance [m]
Deviation
from
idealpath
[mm]
Unfiltered
Gaussian filtered
Conditional Planar filtered
Obstacle location
50cm
20cm
Scan passRough terrain
obstacle
~70 slope
Manipulator
Laser range
finder
Scanning speed: 100 mm/s
Scanning gap: 100 mm
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Command Shaping Tests: Step-Response
Reduced Joint
Encoder Vibration Reduced Tip
AccelerationJoint 1 (ATV Yaw) Encoder:
Joint 3 (Arm Extend) Encoder:
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High-Level Control Software
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Detector Imaging
Targets
Imaging
Target# Target type Depth [cm] MD GPR
1 PMA-2 5 Yes No
2 PMA-1A 12.5 Yes Yes
3 PMA-1A 12.5 Yes Yes
4 PMA-1A 12.5 Yes Yes
5 Fragment 5 Yes No
6 Stone ~10 No Yes
PMA-2PMA-1A Fragment
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Extensive Field Tests
2005: Kagawa, Japan
2007: Siem Reap, Cambod ia
2006: Benkovac, Croatia
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Gryphon: Field Tests in Croatia & Cambodia
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February 2006: Tests in Croatia
Metal detector imaging
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Terrain & Estimation
IF we know terrain Triangulation
IF we know depth SNR gives terrain characteristic
Estimate both simultaneously ( solution up to scale)
Detector
Terrain
t0 ti
Target
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End on a Bang, Bang
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A Better (Controlled) Bang Bang
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Robotics, State-Space and The Meaning of Life
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Seeing is forgetting the name of the thing one sees
What is it?
A Coca Cola Can
1122q3134234232342
(SIFT descriptor in HEX)
(The PHOTID) (The Filename)
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Robotics & Uncertainty
University:
Critical questioning
Open exchange of ideas
Among equal participants.
Corporatocracy:
Ruled by control freaks Behavior modification
works best on dependent,
powerless, infantilized, boredand institutionalized people(which is almost inimical to )
Be careful of being overworked
Et Tu?
There are peddlers of
uncertainty
They want you to be
afraid
Thus, they sell fear
Yes, They live for your
schadenfreude
Insurance lets you riskand balance uncertainty
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UQ Robotics: Dynamic Systems in Motion
PlanningMechanics
of motion
Aerial Systems
Bio-inspired
Systems
Hanna Kurniwati
(NUS/MIT)
Paul Pounds
(ANU/Yale)Surya Singh
(Stanford/Syd)
Diverse international
research group
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