Robotic Sensor Networks: from theory to practice CSSE Annual Research Review Sameera Poduri

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3/28 oil spill Roomba

4/28 Ecological macroscopes Adaptive sampling Networked Infomechanical systems

5/28 keep warfighters or first responders covered with communications

6/28 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:

7/28 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:

8/28 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

9/28 Neighbor-Every-Theta Condition NET Condition: A neighbor in each sector θ Boundary nodes NET Graph: A graph in which every non-boundary node satisfies NET condition

10/28 Connectivity of NET graphs Edge connectivity of a NET graph is at least for  single parameter, tunable  general irregular communication model [Ganesan, et al., UCLA/CSD-TR’02]

11/28 Potential Fields based Controller distance Virtual force distance Virtual force

12/28 Simulation results

13/28 Robot experiments 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

14/28 Minimal sensing ordering information is sufficient to construct a loop [ ]

15/28 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:

16/28 Coverage optimization A. Deshpande, S. Poduri, D. Rus and G. S. Sukhatme,”Coverage Control with Location-dependent Sensing Models”, ICRA 2009

17/28 Data-driven approach Uniform deployment 11 cameras Optimized deployment 9 cameras pilot deploy at 14 locations measure sensing coverage compute optimal locations

18/28 Camera Model

19/28 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:

20/28 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.

21/28 Setup Stargate tmoteSky MicaZ #pursuers >> #evaders localization as a service opponent strategy is known same speed

22/28 Results

23/28 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:

24/28 Mapping and sampling of hydrographic features pertinent to aquatic microbial populations Observing marine ecosystems

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26/28 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 Adaptive Sampling

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29/28 surveillance