About the paper USC CINAPS Builds Bridges Observing and Monitoring the Southern California Bight. In the presentation we also talk about the importance of robots in response to the BP Oil disaster, also knows as Deepwater Horizon oil spill.

1. P R E S E N T E D B Y
G I O V A N N I M U R R U A N D A R T E M I S S H A H
About the paper USC CINAPS
Builds Bridges
Observing and Monitoring the Southern California Bight

2. Abstract
— Importance of implementing a monitoring for ocean
processes
— Observing and monitoring the Southern California
Bight, through the CINAPS network
— Description of the network hardware infrastructure
— Comparison with the robots used in the Gulf of
Mexico disaster.
— Presentation of the algorithms and software used in
the CINAPS network
— A look to the data retrieved with the CINAPS
network

3. The importance of monitoring the ocean
— More than 70% of our earth is covered by water, but
we have explored less than 5% of its environment
— Ocean is a vast and vital resource for the future of
the mankind, regulating the earth’s climate.
— Hence it is important to observe anthropogenic
effects (e.g. urbanization influence, disasters caused
by humans) and climate variability on the ocean
processes.
— Discovering the reasons which affects ocean
processes.

4. The ORION project
— National Science Foundation proposed to build an
integrated ocean observatory network, spending
more than half of a billion of dollars.
— This project will be managed by the Ocean Research
Interactive Observatory Network (ORION) team.
— ORION is currently contracting with oceanographic
institutions and companies to build regional
monitoring systems, such as CINAPS, a observing
network located in the Southern California Bay.

5. The CINAPS Network (1/2)
— CINAPS involves a center for integrated network aquatic
platforms located at the University of Southern California
(USC).
— CINAPS is focused on analyzing the periodic evolution of
harmful algal blooms (HABs) and water quality for
research purposes.
— During October of 2003 and June of 2005, red tide (an
algal bloom) happened in King Harbor and caused
massive fish kills due to a lowering of dissolved oxygen in
the water.
— In response to this event a monitoring program was
developed for King Harbor. CINAPS was part of this
effort.

6. The CINAPS Network (2/2)
— An embedded sensor network to monitor the
Southern California Bay (SCB) was developed by
CINAPS.
— SCB is a highly urbanized area, so it is a good test
field for understanding the changes in the ocean
processes caused by humans.
— The main idea behind the CINAPS was to build a
network of robotic sensors, capable of moving in the
aquatic environment, either using autonomous or
agent-based control.

7. Robotic Sensor Networks
— Computational Systems capable of interacting in
significant ways, combining perception,
communication and control.
— The ability to move and adapt the sensors according
to their feedbacks, gains a more flexible perception
than the static network.
— Ability to self-organize to best match the network
topology.
— Competence in recovering the fault situations.

8. Through the hardware core
— CINAPS is a heterogeneous system, consists of
different kinds of surface and underwater robots,
ground stations, static floating buoys.
— The deployed platforms are equipped with a large
amount of sensors able to measure meaningful
values like temperature, salinity, chlorophyll
fluorescence…
— The nodes of the network can be static or dynamic,
and they are all able to communicate and exchange
data.

9. Static Sensor Nodes (1/2)
— The static nodes in the CINAPS network are marina
buoys and coastal moorings.
— Marina Buoys are assembled by CINAPS
team.
— Intel 400 MHz CPU
— Fluorometer (used to measure the
concentration of chlorophyll a, an
indicative of the density of phytoplankton, a
photosynthetic microorganism which can
be found in the surface of the ocean)
— Array of 6 Thermistors (to measure water
temperature accurately at 0.5 and 2.5 m)
— Local Storing data of sensors and
transmission via wireless technology to USC
— Rechargeable battery by a solar panel.

10. Static Sensor Nodes (2/2)
— The coastal moorings continuously monitor physical
and biological properties of the SCB coastal waters.
— They are composed of:
— Rechargeable battery by a solar panel.
— Sensors at 1 and 13 m of depth:
— Salinity
— Temperature
— Oxygen Saturation (% of dissolved
oxygen in the water, if too high can
be harmful for aquatic organisms)
— Chlorophyll Fluorescence
— Turbidity

11. Monitoring Surface Currents
— 4 Coastal Ocean Dynamics Application Radar High
Frequency Radars (CODAR-HFR) are used to measure ocean
surface current, using continuously transmitted/received
radio waves.
— Each site produces measures that are combined together to
realize a vector map of the surface current for the entire area.
— The data is sent through a low-cost Freewave radio modem,
antenna and internet-connected computer between the
CODAR-HFR sites and towards the Jet Propulsion
Laboratory, located in the California Institute of Technology
(CIT).
— These stations are located at Malibu Beach, Dockweiler Beach,
Point Fermin, and Santa Catalina Island.

12. Area of CINAPS Network

13. Dynamic Sensors Nodes
— Satellite Modem and 2-way RF modem
— Suite of several sensors
— Navigation with compass, and altimeter
— It periodically surfaces and acquires its
position via GPS
— CINAPS team uses 2 Webb Slocum autonomous underwater gliders.
— 1.5 m (length) by 21.3 cm (diameter), 52 kg
— Driven entirely by a variable buoyancy system (no
propeller)
— With adjusting its volume-to-weight ratio and use of 2
wings it can move in a saw-tooth pattern.
— High resolution spatial and temporal data
— 30 days, range of 1500 km.
— Average speed of 0.4 m/s, Max Depth of 200 m
— They do not require a mother-ship, and autonomously
collect data.

14. Dynamic Sensor Nodes
— CINAPS also uses 2 Q-Boats, Autonomous Surface
Vehicles (ASV).
— They can use sonar and stereovision
systems to create maps of the
Ocean’s depths
— Winch system to control the position
of aquatic sensors at different
depths (3-dim sampling capability)
— Monitored and supervised via a PC-based front end
— Possibility to remotely operate the vehicle, or create
missions or retrieve its data.

15. The BP Oil Disaster
— Also known as Deepwater Horizon
oil spill.
— The 20th of April, 2010, an
explosion in the Deepwater
Horizon (a semi-submersible
drilling unit) killed 11 workers and
injured 17.
— This event caused a massive ongoing oil spill in the Gulf
of Mexico, resulting in a large-scale ecological disaster.
— In order to fix the spill and to monitor its progress and
development, scientists used ROV and AUV robots.

16. The Use of Gliders in the BP Disaster
— iRobot Seaglider, an Autonomous Underwater Vehicle is being deployed to
gather information on the presence of oil in the Gulf of Mexico’s waters.
— The Seaglider can go up to 1000 meters of depth and it can operate for up
to 10 months.
— It can send data via satellite several times a day.
— As the Webb Slocum, used by CINAPS, iRobot Seaglider is driven with a
variable buoyancy system, rather than a propeller.
— Researchers plan to use the
device to find and monitor
clouds of dispersed oil droplets,
deep underwater.
— iRobot said it has sold 120
Seagliders to the U.S. Navi,
government agencies, and
researchers.

17. The Use of Gliders in the BP disaster
— Another AUV used to help in the monitoring related to the
BP disaster is the Waldo Robot.
— However Waldo had
some problems
communicating with
researchers via satellite,
and it has been replaced
with another AUV,
namely the UD134
Robot.

18. Remotely Operated Vehicles (ROVs)
— Oceaneering is a firm that
maintains a world leadership
position in providing deepwater
work class ROVs to the oil and gas
industry.
— BP used the ROVs
produced by
Oceaneering to place a
hat cap over the spill.
— The last hat cap has
been installed on the 11th
of July and it is able to
capture up to 80.000
barrels per day.

19. ROV new Top Hat install, 07-11-2010

20. BP disaster and ORION
— ORION project aims to build an integrated ocean
observatory network, capable of monitoring the whole
ocean waters.
— Imagine if ORION project was full-operative when the
disaster of the BP began.
— Perhaps the BP disaster would have been avoid or, in the
worst case, efficiently monitored.
— Efficient monitoring of the oil spill, with online data sent
to centralized operative centers is possible with a full
connected network of robots.
— Efficient reaction to the disaster
— Remember that once the networks are built and in
communication with each others, we can extend them
with different kind of sensors devices.

21. CINAPS: The King Harbor Sampling
— 6 Hydrolab water-quality sondes, located at 3 locations
within the harbor (green circles)
— Each site has 2 instruments located at depths of 0.5 m
and 0.5 m above the sea floor (3-4 m depth)
— The sensors: depth, turbidity,
temperature, dissolved oxygen
chlorophyll-a fluorenscence …
— The collected data is used to study
the growth and mortality of
harmful algal species.
— These observations provide
information about algal dynamics
considering tidal mixing and
vertical movements of the algae.

22. The Software (1/2)
— Some major meaningful components are the software and
algorithm, responsible for actions like:
¡ Data routing in the network
¡ Vehicle control in the Ocean
¡ Data Acquisition Optimization
¡ Data Analysis
¡ Presentation of Results
— Optimal Path planning for the mobile sensors
¡ Regulated with the estimation or a priori-knowledge of the region and
parameters to be sampled.
¡ The model for the environmental monitoring is developed or learned
using the data collected by the sensors.
— Design of a control system to maintain the desired position for
the ASV during the sampling.
¡ Align the boat in the direction of the oncoming wind.

23. The Software (2/2)
— Long-Term desired Goal:
¡ WHAT? For tracking the ocean features that evolves dynamically.
¡ HOW? The design of real-time and optimal trajectories for the
robotic sensors, based on the predictions from a regional ocean
model
— OPTA-BLOOM-Pred algorithm:
1. Identify a feature of interest in the ocean (e.g. the SCB).
2. Gets a prediction of its evolution in time from the ocean model
3. Produce the trajectory based on the prediction using a waypoint-
generation algorithm.
4. The vehicle moves towards the desired points (of high scientific
interest for that particular feature).
5. Upgrade ocean model with collected data, and generate new
prediction.
6. Iterate the process.

24. Multirobot Collaboration Algorithm
— The Surface and Underwater vehicles were used in
cooperation in order to achieve the imposed sampling
missions.
Example:
— ASV case: 2 autonomous surface vehicles were used to
visit a set of sampling locations in a lake.
— Vehicles were required to respect some constraints:
1. Each sampled location must be visited exactly once.
2. Each vehicle has to maintain a inter-vehicle distance and
constantly guarantee their wireless communication
3. No collision between each other or with environmental obstacles
— Behavior-based approach to manage the mission’s
objectives.

25. Communication Infrastructure
— The HFR sites and sensor nodes computers run a
communication software to transmit data wirelessly
to the USC (the University of Southern California).
— Static Sensors use WiFi, while Mobile Sensors
Platforms transmit over the Freewave Network.
— Communication protocol on the freewave network:
¡ There are 2 mode of transmission: guaranteed or not.
¡ Data and System Status is sent to the base stations (HFR sites),
¡ Then the collected data is sent to the central data server
located at the USC.
¡ The USC’s data server can compute missions and elaborate
commands, and send directives to the network’s platforms.

26. Large-Scale Data Analysis
— The data collected by a glider survey, shows the distribution of temperature
(figure a) and chlorophyll-a fluorescence (figure b).
— The observations, useful to understand and predict a HAB (harmful algal
bloom), were done using preset track-lines.
— Maximum Chlorophyll concentration is located below the surface (aprox.
about 15 m of depth)
— Decline in chlorophyll concentration from northern to southern part
— A complete investigation of this data is still ongoing.

27. Small-Scale Data Analysis (1/2)
— Acquisition of chemical, physical, and biological information
at scales that are appropriate for the organism under study.
— Some observations need more restricted
periods of time, like response of microbial
populations to their environment.
— The measures shown in the figure are by
one marina buoy in King Harbor
— 12 days of data collected at 2 depth (0.5 and
3.8 m)
— For temperature and salinity the
variations are coincident with the local tidal
cycle forcing.
— Colder water and more salinity from days 7
to 11, probably due to upwelling.
— From days 15 to 18 we can observe an
increase of chlorophyll and dissolved oxygen
values, that may be caused by the presence
of colder and nutrient-rich water (observed
in the same days).

28. Small-Scale Data Analysis (2/2)
— The data analyzed in the previous slide, is compared to water samples and
other data collected from within and outside the marina
— Hence a complete picture of the overall
dynamics is created.
— This is essential to understand the relation
between planktonic microbes and the algal
bloom events.
— In figure 6a, a 2-dim plot of the vertical
distribution (wrt depth of water) of algal
biomass. High concentrations in red.
— Large variations in the subsurface
chlorophyll distribution between 2 and 3 m
over less than 24 hours
— In figure 6b the tidal cycle variation during
the same time period
— Comparing the 2 graphs we can observe a
peek of chlorophyll fluorescence when the
tidal height is showing a peek.

29. Summary and future Application (1/2)
— Observing ocean processes through the acquisition and analysis of
data collected through the aquatic network sensors, is important in
order to deal with coastal contamination and HABs.
— By the way this sensor network is not limited to phytoplankton
research nor to the analysis of the clearness of waters.
— In fact it is possible to replace these sensors based upon the
scientific interests, without any problem for the whole network.
— However these efforts can not be accomplished just by USC CINAPS
team.
— CINAPS is in collaboration with Southern California Coastal Ocean
Observing System (SCCOOS)
¡ For example CINAPS team makes use of the larger scale SCCOOS
surface current data to provide weather information for the SCB.

30. Summary and future Application (2/2)
— A regional study, in particular the Bight Study 2010
(organized every 5 years), focuses in an analysis of
the importance of some processes, such as water
treatment and river discharge.
— The CINAPS will contribute to this project by
supplying 4 autonomous gliders for the studies in the
SCB.
— Nevertheless CINAPS will deploy the algorithms for
tracking and monitor fresh water plume, waste
water outfall or HAB events.

31. Thanks For Your Attention J