(355 °C, 3 V)
(355 °C, 2 V)
0 10 20 30 0
0 20 40 60
Figure 2. The Current Response of a CO2 Sensor fabricated as described in the text was measured at an applied potential at a temperature of 355 °C. Fig-
ure 2(a) shows a CO2 sensor response without a nanocrystalline SnO2 coating, while Figure 2(b) shows a dramatic difference enabled by the addition of a
coating of nanocrystalline SnO2.
alumina substrate by use of standard electrolyte by sputtering through a future work is to decrease the power
techniques of sputter deposition, pho- shadow mask. consumption to enable, for example,
tolithography, and liftoff. 5. The workpiece is heated to 686 °C for 10 long-term battery operation of CO2 sen-
2. In a second process involving the use minutes, then to 710 °C for 20 minutes. sor systems.
of standard techniques of sputter dep- 6. The layer of nanocrystalline SnO2 is This work was done by Gary W. Hunter
osition, photolithography, and liftoff, deposited on the Na2CO3:BaCO3 layer and Jennifer C. Xu of Glenn Research Center.
the Na3Zr2Si2PO12 solid electrolyte is by a sol-gel process. Further information is contained in a TSP (see
deposited mainly between (and 7. The workpiece is heated to 500 °C for page 1).
touching) the platinum interdigitated 2 hours. Inquiries concerning rights for the com-
electrodes. The workpiece is then ready for use as mercial use of this invention should be ad-
3. The workpiece is heated to a temper- an amperometric CO2 sensor. dressed to NASA Glenn Research Center, In-
ature of 850 °C for 2 hours. Research will continue to optimize novative Partnerships Office, Attn: Steve
4. The Na2CO3:BaCO3 auxiliary solid CO2 sensor performance, while decreas- Fedor, Mail Stop 4–8, 21000 Brookpark
electrolyte is deposited on the elec- ing the operating temperature and Road, Cleveland, Ohio 44135. Refer to
trodes and the Na3Zr2Si2PO12 solid power consumption. The objective of LEW-18324-1
Tele-Supervised Adaptive Ocean Sensor Fleet
A software architecture and system deploys robotic boats to study ocean surface and subsurface
phenomena such as coastal pollutants, oil spills, and hurricanes.
NASA’s Jet Propulsion Laboratory, Pasadena, California
The Tele-supervised Adaptive Ocean
Sensor Fleet (TAOSF) is a multi-robot
science exploration architecture and sys-
tem that uses a group of robotic boats
(the Ocean-Atmosphere Sensor Integra-
tion System, or OASIS) to enable in-situ
study of ocean surface and subsurface
characteristics and the dynamics of such
ocean phenomena as coastal pollutants,
oil spills, hurricanes, or harmful algal
blooms (HABs). The OASIS boats are ex-
tended-deployment, autonomous ocean
surface vehicles. The TAOSF architec-
ture provides an integrated approach to
multi-vehicle coordination and sliding
One feature of TAOSF is the adap-
A concept of the TAOSF Field Deployment System shows an overhead aerostat (an unmanned blimp
tive re-planning of the activities of the tethered to a manned field operations vessel) that provides a global camera overview of three OASIS
OASIS vessels based on sensor input platforms and a patch of rhodamine dye. The overhead map is shown on the right.
NASA Tech Briefs, January 2009 7
(“smart” sensing) and sensorial coordi- GSFC is a surrogate for the OASIS ASV IG is used for analysis of science data
nation among multiple assets. The ar- system and allows for independent de- from both the OASIS platforms and ex-
chitecture also incorporates Web-based velopment and testing of higher-level ternal sources such as satellite imagery
communications that permit control of software components. The Platform and fixed sensors. These data are used
the assets over long distances and the Communicator acts as a proxy for both by the SSA in planning vessel naviga-
sharing of data with remote experts. actual and simulated platforms. It trans- tional trajectories for data gathering.
Autonomous hazard and assistance de- lates platform-independent messages The SSA also provides an operator inter-
tection allows the automatic identifica- from the higher control systems to the face for those occasions when a scientist
tion of hazards that require human in- device-dependent communication pro- desires to exert direct monitoring and
tervention to ensure the safety and tocols. This enables the higher-level control of individual platforms and their
integrity of the robotic vehicles, or of control systems to interact identically instruments.
science data that require human inter- with heterogeneous actual or simulated Using this architecture, multiple mo-
pretation and response. Also, the archi- platforms. bile sensing assets can function in a co-
tecture is designed for science analysis The Adaptive Sensor Fleet (ASF) pro- operative fashion with the operating
of acquired data in order to perform vides autonomous platform assignment mode able to range from totally au-
an initial onboard assessment of the and path planning for area coverage, as tonomous control to tele-operated con-
presence of specific science signatures well as monitoring of mission progress. trol. This increases the data-gathering
of immediate interest. The System Supervision Architecture effectiveness and science return while
TAOSF integrates and extends five (SSA) provides high-level planning, reducing the demands on scientists for
subsystems developed by the participat- monitoring, tele-supervision, and sci- tasking, control, and monitoring. This
ing institutions: Emergent Space Tech- ence data analysis. The latter is done system is applicable also to areas where
nologies, Wallops Flight Facility, NASA’s using the Inference Grid (IG) frame- multiple sensing assets are needed like
Goddard Space Flight Center (GSFC), work to represent multiple spatially- and ecological forecasting, water manage-
Carnegie Mellon University, and Jet temporally-varying properties. The In- ment, carbon management, disaster
Propulsion Laboratory (JPL). The ference Grid is a probabilistic multi- management, coastal management,
OASIS Autonomous Surface Vehicle property spatial lattice model, where homeland security, and planetary explo-
(ASV) system, which includes the vessels sensor information is stored in spatially ration.
as well as the land-based control and and temporally registered form, and This work was done by Gregg W. Podnar
communications infrastructure devel- which is used for both scientific infer- and John M. Dolan of Carnegie Mellon Uni-
oped for them, controls the hardware of ences and for vehicle mission planning. veristy, Alberto Elfes of Caltech, and Jeffrey C.
each platform (sensors, actuators, etc.), The information in each Inference Grid Hosler and Troy J. Ames of Goddard Space
and also provides a low-level waypoint cell is represented as a stochastic vector, Flight Center for NASA's Jet Propulsion Lab-
navigation capability. The Multi-Plat- and metrics such as entropy are used to oratory. Further information is contained in a
form Simulation Environment from measure the uncertainty in the IG. The TSP (see page 1). NPO-45478
8 NASA Tech Briefs, January 2009