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Adaptive sampling in environmental Robotics Mohammad Rahimi, Gaurav sukhatme, William Kaiser, Mani Srivastava, Deborah Estrin

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Motivation… NIMS: Networked Infomechanical Systems

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Internet Telephone/ISP Data Base WRCCRF Crane Site Dry Shack 84 91 NIMS Node Met Node (Ta, RH, PAR) Solar Cell Battery Pack Power Distribution Cable Visible Imager With Pan/Tilt Actuator 47 m 50 m

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Science Objectives C H2OH2O Q C 13 /C 12 T, RH, Wind Growth

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NIMS Prototype Deployment

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Problem Creating a dynamic Map of the environment Based on the carrying sensors (attributes)

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Approach The robot (shuttle) is an agent gather Geostatistics information Refresh those statistics as fast as possible

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Digitizing robots world 0,0 Cell size that we call it a pixel is a x*x. pixel is the distance that shuttle moves atomically Obstacles Shuttle Patrol Area

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Assumptions Shuttle is certain about the location Sensor reading error is zero Environment is static in circuit convergence time Warning to the user to reduce coverage or expected accuracy otherwise

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Sampling Policy Stratified Sampling Divide the population into subpopulations Extremely better performance with some degree of apriority domain knowledge Random sampling Mean proportional to cell size

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Feedback Using variance of data to classify a region Vaiance/Mean < Expected error or Variance < Sensor Noise

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Divide and Conquer Stratify the current cell into four μ = α * cell size (μ is mean of step size) Collect data in current cells (Random) Calculate the variance Iterate until variance is below threshold

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Closed Loop System Estimation error Stratification Policy + - Map Acceptable error Reading points

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Result Of the Algorithm n Log (n) n Quad-tree Map of the variance of the environment Shuttle step-size is random but proportional to how deep in the tree it is

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Initial Results

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Wish List Adding time domain Static sensors as sample support

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