Robotic Sensor Networks: from theory to practice

1. Robotic Sensor Networks: from theory to practice Sameera Poduri CSSE Annual Research Review 03.17.09

3. oil spill Roomba

4. Ecological macroscopes Adaptive sampling Networked Infomechanical systems

5. keep warfighters or first responders covered with communications

6. Problem Challenge: global objectives using local sensing and control Design motion controllers for a robotic sensor network 1. communication network is connected Objectives: 2. sensing coverage is maximized 3. intruder pursuit time is minimized 4. field estimation error is minimized

7. Problem Design motion controllers for a robotic sensor network 1. communication network is connected Objectives: 2. sensing coverage is maximized 3. intruder pursuit time is minimized 4. field estimation error is minimized

8. 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 IEEE Transactions on Mobile Computing, Feb 2009

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

10. 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. Potential Fields based Controller Virtual force distance Virtual force distance

12. Simulation results

13. 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. Minimal sensing ordering information is sufficient to construct a loop [ ]

15. Problem Design motion controllers for a robotic sensor network 1. communication network is connected Objectives: 2. sensing coverage is maximized 3. intruder pursuit time is minimized 4. field estimation error is minimized

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

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

18. Camera Model

19. Problem Design motion controllers for a robotic sensor network 1. communication network is connected Objectives: 2. sensing coverage is maximized 3. intruder pursuit time is minimized 4. field estimation error is minimized

20. 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. Setup 32 4 31 38 42 41 40 39 37 36 35 34 33 5 6 3 30 5 105 106 1 29 101 6 2 21 20 7 1 28 27 25 19 18 16 14 13 12 9 8 26 4 24 23 22 3 15 2 11 17 10 104 103 102 56 55 43 48 54 44 53 8 49 7 108 107 45 47 52 51 50 4th fl. 46 Stargate tmoteSky MicaZ #pursuers >> #evaders 1 2 localization as a service 3 opponent strategy is known same speed

22. Results

23. Problem Design motion controllers for a robotic sensor network 1. communication network is connected Objectives: 2. sensing coverage is maximized 3. intruder pursuit time is minimized 4. field estimation error is minimized

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

26. 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

28. http://robotics.usc.edu/~sameera

29. surveillance