1 / 17

Marine Robotics

Marine Robotics. Ari Requicha Carl Oberg Gaurav Sukhatme Beth Stauffer David Caron Amit Dhariwal Deborah Estrin Eric Shieh Chongwu Zhou Bin Zhang. Fundamental biological questions (driving forces behind the work).

carlton
Download Presentation

Marine Robotics

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Marine Robotics Ari Requicha Carl Oberg Gaurav Sukhatme Beth Stauffer David Caron Amit Dhariwal Deborah Estrin Eric Shieh Chongwu Zhou Bin Zhang

  2. Fundamental biological questions (driving forces behind the work) • What are the spatial/temporal distributions in nature of microorganisms of interest to ecosystem or human health? • What factors (environmental or otherwise) explain the distributions of these microorganisms? • How can we most effectively and efficiently address these issues? • Not simply a question of ‘more data’ (although that also helps). CENS approach will provide the ability to characterize the chemical, physical, biological community on temporal and spatial scales that are pertinent to the organisms.

  3. Typical Scales of Ocean Sensing/Sampling 3 meter discus buoy (Autonomous Aerosol Sampler; Sholkovitz et al., WHOI) CTD rosette (Ross Sea, 1999)

  4. What are the problems with these approaches? *** Scale of Measurements: Six orders of magnitude between scales of observation and scales of microbial interactions (we do meters - need to do micrometers) Spatiotemporal Coverage: “One ocean, one instrument” approach (workable sea state, instrument cost) Instrument Capability (biosensor limitation): Chlorophyll (phytoplankton only; not specific) *** ***

  5. Examining small-to-microscale distributions of aquatic microorganisms. What factor act to establish these patterns? What do the patterns mean? How can we investigate these features? East Sound, WA (Donaghay) (http://www.gso.uri.edu/criticalscales/about/index.html)

  6. Validation • Create a thermocline in the tank • Place sensors so as to find the thermocline accurately and with a small part of the total network • Acquire water samples at strategically-located points • Determine microorganism content (offline) • Analyze the data to correlate algal behavior with thermocline.

  7. Thermocline Localization The search space is 1D, and is divided into regions Each node explores one region with binary search Each node tries to persuade others that the thermocline is within its search region A process of data aggregation is enacted on the route from each node to the edge node (the one at the top of the tank)

  8. The Experimental Setup • A tethered system of small robots with radios (limited range) • A small number of mobile underwater robots • Initially focus on temperature measurements • Collect water samples for offline analysis

  9. Improving Energy Efficiency with a Data Mule • Motivation: • After first several steps, most nodes become inactive • However, many of them have to be awake to forward the messages from the active nodes to root • Solution: • Create shortcuts from active nodes to root with a robotic submarine • Assumption: • Submarine can be recharged

  10. The Goal: Pattern-Triggered Data Collection Biosensors: Correlatiing small-scale spatial distributions with chemical and physical structure Sensor network: Defining small-scale physical structure

  11. Improving regional scale characterization of temporal/spatial distributions How to characterize and sample features of biological interest in aquatic ecosystems? What we do now. (large uniform ‘grid, occupied sporadically)

  12. What we want to do. Aggregation of sampling nodes at feature(s) of interest based on sensing-directed movement

  13. Marine Microorganism Monitoring Subsystems

  14. Research Implications • High spatial density (cm-mm), small sensors (cm-nm) of small size and limited capability. • Sensor-coordinated mobility and actuation, -deploy sensors and sample collectors where and when they are needed. -data processing inside the network, e.g., to direct sensing and/or sampling. • Rapid microorganism identification in-situ  New sensing techniques

  15. Adaptive Sampling • How to aggregate sensors where they are needed • Within CENS there are several parallel strategies being pursued: • Statistically rigorous adaptive sampling (NIMS) • Event-aware (triggered) sampling (NIMS) • Bacterial motion-inspired swarming

  16. Bacteria move by interspersing propulsion with random turns (tumbles) • Taxis is achieved by varying the duration of propulsion • This is in effect, a biased random walk Bacterial Motion

  17. Summary/Conclusions • Lab-based experimental application of sensor-actuated microbial sampling. • Movement from lab-based ground-truthing into the real world. • Small-scale coordinated sensor nodes (mobile & stationary working together) • Multiple mobility modes • ‘True’ biosensor development

More Related