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

Motivation… NIMS: Networked Infomechanical Systems

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

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

NIMS Prototype Deployment

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

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

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

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

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

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

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

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

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

Initial Results

Wish List Adding time domain Static sensors as sample support