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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Stargate

tmoteSky

MicaZ

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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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