robotic sensor networks: from theory to practice
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Robotic Sensor Networks: from theory to practice. Sameera Poduri. CSSE Annual Research Review 03.17.09. oil spill Roomba. Ecological macroscopes. Adaptive sampling. Networked Infomechanical systems. keep warfighters or first responders covered with communications. - PowerPoint PPT PresentationTRANSCRIPT
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Robotic Sensor Networks: from theory to practice
CSSE Annual Research Review 03.17.09
Sameera Poduri QuickTime™ and aTIFF (Uncompressed) decompressor
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oil spill Roomba
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Ecological macroscopes
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Adaptive sampling
Networked Infomechanical systems
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keep warfighters or first responders covered with communications
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1. communication network is connected
Challenge: global objectives using local sensing and control
Design motion controllers for a robotic sensor network
2. sensing coverage is maximized
3. intruder pursuit time is minimized
4. field estimation error is minimized
Problem
Objectives:
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1. communication network is connected
Design motion controllers for a robotic sensor network
2. sensing coverage is maximized
3. intruder pursuit time is minimized
4. field estimation error is minimized
Problem
Objectives:
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Given a large network, find local conditions that guarantee global
k-connectivity.
Network Connectivity
S. Poduri, S. Pattem, B. Krishnamachari, G. S. Sukhatme. "Using Local Geometry for Tunable Topology Control in Sensor Networks". In IEEE Transactions on Mobile Computing, Feb 2009
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Neighbor-Every-Theta Condition
NET Condition: A neighbor in each sector θ
Boundary nodes
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NET Graph: A graph in which every non-boundary node satisfies NET condition
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Connectivity of NET graphs
Edge connectivity of a NET graph is at least for θ < π2πθ
⎢⎣⎢
⎥⎦⎥
single parameter, tunable
general irregular communication model
[Ganesan, et al., UCLA/CSD-TR’02]
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Potential Fields based Controller
Ficov =
−Kcov
(xi −xj )2
⎛
⎝⎜
⎞
⎠⎟
j∈nbd(i )∑
xi −xj
xi −xj
⎛
⎝⎜
⎞
⎠⎟
&&xi =
Ficov + Fi
NET + Fiobs −υ &x
m⎛
⎝⎜⎞
⎠⎟
Fiobs =
−Kobs
(xi −xj )2
⎛
⎝⎜
⎞
⎠⎟
j∈obstacle(i )∑
xi −xj
xi −xj
⎛
⎝⎜
⎞
⎠⎟
distance
Vir
tual
fo
rce
Ficov
FiNET =
KNET
(xi −xj )2
⎛
⎝⎜
⎞
⎠⎟
j∈NET (i )∑
xi −xj
xi −xj
⎛
⎝⎜
⎞
⎠⎟
distance
Vir
tual
fo
rce
Fi
cov
FiNET
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θ =2π / 3 θ =2π / 5 θ =2π / 6
Simulation results
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Robot experiments
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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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1. communication network is connected
Design motion controllers for a robotic sensor network
2. sensing coverage is maximized
3. intruder pursuit time is minimized
4. field estimation error is minimized
Problem
Objectives:
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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
Uniform deployment 11 cameras
Optimized deployment 9 cameras
• pilot deploy at 14 locations• measure sensing coverage• compute optimal locations
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Camera Model
f p −q , p( ) =k1(p)
p−q 2 + k2 (p)
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1. communication network is connected
Design motion controllers for a robotic sensor network
2. sensing coverage is maximized
3. intruder pursuit time is minimized
4. field estimation error is minimized
Problem
Objectives:
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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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4th fl.
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1101
102103104
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Stargate
tmoteSky
MicaZ
1
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#pursuers >> #evaders
localization as a service
opponent strategy is known
same speed
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Results
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1. communication network is connected
Design motion controllers for a robotic sensor network
2. sensing coverage is maximized
3. intruder pursuit time is minimized
4. field estimation error is minimized
Problem
Objectives:
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Mapping and sampling of hydrographic features pertinent to aquatic microbial populations
Observing marine ecosystems
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Reconstruct a scalar field (temperature, chlorophyll, etc.)
Unlike conventional mobile robotics mappingSensor reading are only valid locallyCorrelation 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
Adaptive Sampling
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http://robotics.usc.edu/~sameera
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surveillance
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