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Recent Research
Biologically-inspired Visual Landmark Navigation for Mobile Robots
Collaborative Work with Bianco
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Mobile Robot Navigation
Robot Navigation relies answering the questions:
–Where am I?
–Where are other places relative to me?
–How do I get to other places from here?
Possible answers:
–Classical robotic techniques
–New trend: biologically-inspired methods
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Biological-inspiration
Animals and insects are proficient in visual navigation (Papi 1992)
The use of natural visual landmarks by insects for navigation have been well documented (Wehner 1992)
Strategies for the selection of natural landmarks by insects has been reported (Lehrer 1993, Zeil 1993)
Many models have been introduced without formal methods (Trullier et al. 1997)
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Related Issues
Navigation can be considered as a four-level hierarchy
–guidance
–place recognition-triggered response
–topological navigation
–metric navigation
We perform guidance: the agent is guided by a spatial distribution of landmarks
–we do not use maps
–we do not know our position in reference co-ordinates
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Acquiring Visual Landmarks
“A landmark must be reliable and landmarks which appear to be appropriate for human beings are not necessarily appropriate for robots because of the different sensor and matching apparatus.”
Matàric 1990, Thrun 1996
If we can establish what is meant by reliability for given sensors and matching schemes then the problem of landmark selection is automatically solved!
Reliability depends on sensor and matching scheme
– Sony NTSC camera and Fujitsu Tracking card (TRV) Landmarks are based on the image correlation concept.
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Definition of a Landmark
A landmark is a region within a whole image
– TRV performs 16 x 16 SAD correlation
– A correlation matrix is generated
(ox,oy)
(ox-8*mx,oy -8*my)
(ox+7*mx,oy +7*my)
16*mx
16*my
Template
Frame
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5000
Uniqueness of Landmarks
Different landmarks have different correlation matrices
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Reliability of Landmarks
We define the reliability of a landmark as
g
gr
′−=1 where g’ is a local minimum
found in the neighbourhood of g, the global minimum.
Maximising r, coupled with different template sizes, we can select unique landmarks.
Selecting Landmarks
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Magnification size = 3 & 4 Magnification size = 5 & 6
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How “dynamically” reliable are our landmarks?
Through a phase directly inspired by wasps and bees, the robustness of statically chosen landmarks is tested.
Turn Back and Look
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The robot moves with stereotyped movements The camera continuously points toward the goal
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Two frames from a typical TBL phase
Turn Back and Look
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Numbers show the reliability factors of the landmarks
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The reliability r is constantly monitored for each landmark during TBL.
Only those landmarks whose r is above a threshold e are considered.
Small perturbations (light, position, etc.) are produced with TBL and this represents a framework for testing the reliability of real navigation tasks.
“Only strong individuals can survive through a selection phase” (Murray Gell-Mann, The Quark and the Jaguar, 1994)
Turn Back and Look
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TBL produces small perturbations: light, perspective, size...
Perturbations
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The underlying principle is based on the model proposed by Cartwright and Collet to explain bee behaviour.
It mimics the behaviour of a bee quite well BUT a 2D extension is required
Key Point for the extension:
– A landmark is attracted toward its original position and size
Landmark Navigation
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Displacement from the original position and size is suitable for extracting navigation information from landmarks.
Landmark Navigation
Original position and size New position and sizeQuickTime™ and aPhoto - JPEG decompressorare needed to see this picture.
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Let be the difference between the original and present positions of the landmarks.
Let be the weight for size difference of the landmarks.
The landmark attraction vector is given by:
Landmark Navigation
ldr
lW
lW =
Mxy
mxy
if Mxy
mxy
>1
−mxy
Mxy
otherwise
⎧
⎨ ⎪
⎩ ⎪
r v l =
r d l ⋅Wl
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l
r v
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By fusing all the landmark attraction vectors
through weighted averaging:
we obtain the final
navigation vector
Landmark navigation
r V = Vx Vy[ ]=
r v l ⋅s(rl )
l=1
L
∑
s(rl)l=1
L
∑
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Typical image input frame
r V
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Images from a Navigation Experiment
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A navigation vector field: for each (x,y) can be computed
Landmark navigation
2165
6
5234
2294
3742
238
5623
4899
1542
2774
95
193
207
134
405
17
196
72
1340
121
192
192
3362
909
5358
4431
4551
1076
4032
3450
1017
5733
1317
2 4717
5023
5702
655
1025
1155
6824
1677
7
5596
1686
31532
3
1400
1
1452
612
444
3503
7588
5228
8002
5210
9118
981
2263
2047
2240
2341
3768
1271
7
1853
4243
8675
5208
5934
6891
1225
1351
2
1185
7
7510
3006
2134
1307
2413
2200
1
1020
cm 60
cm
60 cm
GOAL POSITION
720 cm
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There is evidence of a potential field when biologically-based navigation is considered (Voss 1995, Gaussier 1998)
In this case, a potential function U(x,y) such that
can drive the movements of the robot. A necessary and sufficient condition for U to exist is that the vector
field is conservative, that is:
or, alternatively
Visual Potential Field
r V = Vx Vy[ ]=
∂U∂x
∂U∂y
⎡
⎣ ⎢ ⎤
⎦ ⎥
Vr
∂Vx
∂y =
∂Vy
∂x
r V od
r r =0
c∫
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Partial derivatives
and
are computed numerically.
Computation of Partial Derivatives
∂Vx
∂y ∂Vy
∂x
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TBL affects the conservativeness according to different thresholds Plotting for different threshold values of e yields:
TBL affects Conservativeness
∂Vx
∂y −
∂Vy
∂x
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e=0 e=0.1
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As e -->1 the vector field becomes conservative and computation of the potential field can be possible
TBL affects Conservativeness
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e=0.2 e=2.5
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Different Potential Fields U can be generated from values of e.
Computation of the Potential Field
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e=0 e=0.1
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As e -->1 the potential field is suitable to drive navigation
Computation of the Potential Field
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e=0.1 e=0.25
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When a smaller template size is considered, the potential field basin has a different shape:
– deeper at the goal position.
– a reduced basin of attraction. Example size 4, e=0.2
Visual potential field
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• Basic navigation rule:
if then continue using the last navigation vector
else give the robot the currently computed navigation vector
Equipment– Nomad200
– Sony EVI-D30
– Fujitsu Colour TRV
Experimentation
Var(r V ) ≥σ
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The Environment
102
0 cm
CUPBOARD(h=210cm)
TABLE (h=70cm)
60
cm
60 cm
GOAL POSITION
COLUMN
720 cm
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Size = 6 Threshold e = 0
Experiment A
1020
cm
CUPBOARD(h=210cm)
TABLE (h=70cm)
60
cm
60 cm
GOAL POSITION
COLUMN
720 cmWALL
1
G1
2
3
4
G4 5
G5
6
7
8
G8
9G9
10G10
11
G11
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Size = 6 Threshold e = 0.2
Experiment B
1020
cm
CUPBOARD(h=210cm)
TABLE (h=70cm)
60
cm
60 cm
GOAL POSITION
COLUMN
720 cmWALL
1
G1
2
G2
3
4G4
5
G5
6
7
8G8
9
G9
10
G10
11
G11 G6
G7
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Experiment C
1020
cm
CUPBOARD(h=210cm)
TABLE (h=70cm)
60
cm
60 cm
GOAL POSITION
COLUMN
720 cmWALL
1
G1
2
G2
3
4
G4
5
G5
6
7
8G8
9
G9
10
G10
11
G6
G7
Size = 5 Threshold e = 0.2
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Major results– Self-selection of natural landmarks
– Theory of visual potential
– Landmark definition based on reliability
– Landmark navigation can been formalised as driven by a potential field
– Invariants or Transformations are not needed
Most importantly– TBL affects the conservativeness of the vector field
– strong landmarks = conservativeness = potential field
– Biologically-inspired navigation methods are effective
Conclusions
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