Robotic Sensor Networks:
from theory to practice
Sameera Poduri
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CSSE Annual Research Review
03.17.09
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oil spill Roomba
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Ecological
macroscopes
Adaptive sampling
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Networked Infomechanical systems
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keep warfighters or first responders
covered with communications
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Problem
Design motion controllers for a robotic sensor network
Objectives:
1.
communication network is connected
2.
sensing coverage is maximized
3.
intruder pursuit time is minimized
4.
field estimation error is minimized
Challenge: global objectives using local sensing and control
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Problem
Design motion controllers for a robotic sensor network
Objectives:
1.
communication network is connected
2.
sensing coverage is maximized
3.
intruder pursuit time is minimized
4.
field estimation error is minimized
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Network Connectivity
Given a large network, find local conditions that guarantee global
k-connectivity.
S. Poduri, S. Pattem, B. Krishnamachari, G. S. Sukhatme. "Using Local Geometry for Tunable Topology Control in Sensor Networks". In
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IEEE Transactions on Mobile Computing, Feb 2009
Neighbor-Every-Theta Condition
NET Condition: A neighbor
in each θ sector
Qu i ck Ti m e ™ an d a
TI F F ( LZ W) d ec om pr es so r
ar e n ee de d t o s ee t h is pi ct u re .
Qu i ck Ti m e ™ an d a
TI F F ( LZ W) d ec om pr es so r
ar e n ee de d t o s ee t h is pi ct ur e .
NET Graph: A graph in which every
non-boundary node satisfies NET
condition
Boundary nodes
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Connectivity of NET graphs
Edge connectivity of a NET graph is at least  2  for   
  
 single parameter, tunable
 general irregular communication
model
[Ganesan, et al., UCLA/CSD-TR’02]
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Fi
Fi
NET
obs
 K cov   xi  x j 
  

2
(x

x
)
x

x


j nbd (i )
i
j
 i
j 
 K NET   xi  x j 
  

2
j NET (i )  (xi  x j )   xi  x j 
 K obs   xi  x j 
  

2
(x

x
)
x

x
 i
j obstacle(i) 
i
j
j 
Fi cov
distance
Virtual force
Fi
cov
Virtual force
Potential Fields based Controller
Fi NET
Fi cov
distance
 Fi cov  Fi NET  Fi obs   x&
xi  

m

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Simulation results
  2 / 3
  2 / 5
  2 / 6
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Robot experiments
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QuickTime™ and a
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K. Dantu, P. Goyal, and G. Sukhatme, "Relative Bearing Estimation from Commodity Radios", To appear in IEEE International
Conference on Robotics and Automation, Sep 2009
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Minimal sensing
ordering information is sufficient to construct a loop
[
]
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Problem
Design motion controllers for a robotic sensor network
Objectives:
1.
communication network is connected
2.
sensing coverage is maximized
3.
intruder pursuit time is minimized
4.
field estimation error is minimized
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Coverage optimization
A. Deshpande, S. Poduri, D. Rus and G. S. Sukhatme,”Coverage Control with Location-dependent Sensing Models”, ICRA 2009
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Data-driven approach
• pilot deploy at 14 locations
• measure sensing coverage
• compute optimal locations
Uniform deployment
11 cameras
Optimized deployment
9 cameras
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Camera Model
f  p  q , p 
k1 ( p)
p  q  k2 ( p)
2
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Problem
Design motion controllers for a robotic sensor network
Objectives:
1.
communication network is connected
2.
sensing coverage is maximized
3.
intruder pursuit time is minimized
4.
field estimation error is minimized
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Pursuit evasion
How should robots move to capture all evaders?
M. Vieira, R. Govindan, and G. Sukhatme, "Scalable and Practical Pursuit-Evasion", To appear in International Conference on Robot
Communication and Coordination, Mar 2009.
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Setup
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#pursuers >> #evaders
1
Stargate
2
tmoteSky
3
MicaZ
localization as a service
4th fl.
opponent strategy is known
same speed
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Results
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Problem
Design motion controllers for a robotic sensor network
Objectives:
1.
communication network is connected
2.
sensing coverage is maximized
3.
intruder pursuit time is minimized
4.
field estimation error is minimized
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Observing marine ecosystems
Mapping and sampling of hydrographic features pertinent to aquatic microbial populations
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Adaptive Sampling
Reconstruct a scalar field (temperature, chlorophyll, etc.)
Unlike conventional mobile robotics mapping
Sensor reading are only valid locally
Correlation between sensors decreases rapidly
with distance
Intuition: the more data near the locations where a field
estimate is desired, the less the reconstruction error
The spatial distribution of the measurements (the
samples) affects the estimation error
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http://robotics.usc.edu/~sameera
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surveillance
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