Lesson 4a Mobile (Robotic) Sensing Spatial and temporal simulations to inform sensor system design

Transcription

1 Lesson 4a Mobile (Robotic) Sensing Spatial and temporal simulations to inform sensor system design We have discussed static sensors and handheld (participatory) Sensing Robotics deliver mobility: Capacity to measure continuously in space IITR-UCM-NI Water Sustainbility & Sensor Networks Course

2 Mobile Aquatic Sensing Mobile sensing involved loading sensor systems onto mobile or robotic devices Tethered sensor systems have 1 or 2 degrees of freedom the most common is a vertical (upand-down) profiler Free swimming systems can move in 2D (surface vehicles) or 3D (subsurface) Autonomous underwater vehicles (AUVs) or surface vehicles (ASVs) follow prescribed or adaptive sampling missions Human-controlled (radio, etc.) IITR-UCM-NI Water Sustainbility & Sensor Networks Course

3 Vertical profiling sensor systems There are usually multiparameter sensors (called sondes ) mounted on a buoy A mechanism raises and lowers the sensor Pre-programmed depths Adaptive sampling (example: focus sampling on the thermocline) Sunlight (radiation) varies with depth temperature, algal growth, etc. Here s one in action: IITR-UCM-NI Water Sustainbility & Sensor Networks Course

4 Profiler data discussion what do you see? IITR-UCM-NI Water Sustainbility & Sensor Networks Course

5 Autonomous Surface Vehicles (ASVs) Small (1-2 m long) boat carrying water quality sensors and GPS Pre-programmed routes or adaptive sampling Where sources enter a water body (rivers entering a harbor or lake) there may be variability across the surface 2 m winch system WQ sonde IITR-UCM-NI Water Sustainbility & Sensor Networks Course Hybrid version: ASV with vertical profiling capability

6 Static & Mobile Adaptive Sampling Static sensor buoy CENS Networked Aquatic Microorganism Observation System (NAMOS) (Gaurav Sukatme, Dave Caron U. Southern California)

7 Static-mobile adaptive sampling winch system 2 m WQ sonde = positive fluorometer readings

8 Adaptive Sampling: Second Run

9 Sukhatme et al. Environmental Engineering Science, 24(2), 2007 Overall fluoresence map

10 Autonomous Underwater Vehicles There are many types, but a popular one is this glider Internal weight displacement and bladder changes its buoyancy so up and down is power free Turning is the main power consumption Localization and data communications happen at the surface These need more space (large lakes or ocean) IITR-UCM-NI Water Sustainbility & Sensor Networks Course

11 Example: Gliders mapping bathymetry in Monterrey Bay, California Work by the Monterrey Bay Aquarium Research Institute (mbari.org) Major ocean observatory effort there Check out representation of the data here: /default.htm IITR-UCM-NI Water Sustainbility & Sensor Networks Course

12 Example: Managing water quality with reservoir releases Merced R San Joaquin R Objective: Develop strategy for rapidly identifying reservoir release flows to achieve adequate mixing downstream

13 Tethered mobile sensor: 2D vertical crosssections of the river Sensors delivered to transect locations in a reproducible manner (within a few cm); programmable

14 Precision river water quality (micro-habitat) observations Same transect: water temperature afternoon

15 Precision river water quality observations Same transect: morning vs afternoon dissolved oxygen

16 High Resolution River Observations Enables Better Environmental Modeling Safely assess flow and mixing conditions without entering the water Excellent for model calibrations (model results shown here) QUESTION: How might we validate this model after we calibrate it? Observed in/out velocity fields

17 Human-assisted mobile sensing Synch sensor readings with GPS Output (below) is a trace of the chemistry: shape is the river; color is the concentration What can we tell from these traces? IITR-UCM-NI Water Sustainbility & Sensor Networks Course

18 Summary Mobile Sensing Spatial and temporal simulations to inform sensor system design Mobile sensing provides much richer spatial data than possible from stationary sensors Such data sets are extremely valuable for calibrating and testing the environmental models However, we cannot afford to robotic systems all the time and everywhere Therefore, stationary ground-based sensors are still important because they they offer continuous temporal data IITR-UCM-NI Water Sustainbility & Sensor Networks Course