| NSF Org: |
DMS Division Of Mathematical Sciences |
| Recipient: |
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| Initial Amendment Date: | September 6, 2009 |
| Latest Amendment Date: | June 6, 2011 |
| Award Number: | 0914840 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Leland Jameson
DMS Division Of Mathematical Sciences MPS Directorate for Mathematical and Physical Sciences |
| Start Date: | September 1, 2009 |
| End Date: | August 31, 2013 (Estimated) |
| Total Intended Award Amount: | $240,043.00 |
| Total Awarded Amount to Date: | $240,043.00 |
| Funds Obligated to Date: |
FY 2010 = $80,625.00 FY 2011 = $82,810.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
110 INNER CAMPUS DR AUSTIN TX US 78712-1139 (512)471-6424 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
110 INNER CAMPUS DR AUSTIN TX US 78712-1139 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | COMPUTATIONAL MATHEMATICS |
| Primary Program Source: |
01001011DB NSF RESEARCH & RELATED ACTIVIT 01001112DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.049 |
ABSTRACT
This proposal contains a research program that systematically covers a novel source discovery problems involving dynamic visibility, the Poisson equation, the heat equation, and the wave equations posed in complicated, non-simply connected domains. These problems are motivated by robotic path planning applications. Assuming sparse and sequential measurements of data, the PI and collaborators propose new robotic path algorithms along which new measurements can be added in order to efficiently determine plausible source locations and to reach particular vantage points such that the plausible source locations are under visual surveillance. The PI further studies situations in which obstacles in the domains are partially known through the visibility along the robotic path.
Consider the situation in which a robot, sent into an unknown environment, is supposed to discover the location of a signal source and place it under its line-of-sight in an efficient manner. The unknown environment contains non-penetrable solid obstacles and should be avoided along the robot's path. In this environment, the properties of the signal, such as the signal strength, are assumed to satisfy certain mathematical equations. The robot gathers measurements from two different sensors: a range sensor that gives distance from the robot to the surrounding obstacles, and a sensor that measures the signal strength that is being emitted from the yet-to-be-located source. While measurements can be taken anywhere, the PI and the collaborators are interested in having the robot take very few measurements with its sensors. This consideration is particularly relevant to efficient surveillance and anti-terrorism applications. The goal is to design an robust algorithm that determines how the robot should navigate through the environment and where along its path it should take measurements so that the ob jective of discovering signal sources and their surveillance can be achieved efficiently.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This project involves the idealization of a situation in which a robot, sent into an unknown environment, is supposed to discover the location of a signal source and place it under its line-of-sight in an efficient manner. The unknown environment contains non-penetrable solid obstacles and should be avoided along the robot's path. In this environment, the properties of the signal, such as the signal strength, are assumed to satisfy certain mathematical equations. The robot gathers measurements from two different sensors: a range sensor that gives distance from the robot to the surrounding obstacles, and a sensor that measures the signal strength that is being emitted from the yet-to-be-located source. While measurements can be taken anywhere, we are interested in having the robot take very few measurements with its sensors.
This project resulted in the creation of robust algorithms that determines how the robot should navigate through the environment and where along its path it should take measurements so that the objective of discovering signal sources and their surveillance can be achieved efficiently.
The research supported by this project showed that certain reciprocity, derived from the mathematical related models, may lead to very effective source identification. The concept of reciprocity may also be used to quantify the effectiveness of the measurements taken, and used to further improve the results.
The proposed research has immediate and direct impact on many different tasks involving anti-terrorism such as surveillance and discovery of harmful contamination sources in unknown battle fields as well as domestic regions. The proposed research will result in new designs of autonomous vehicles or robots to achieve these tasks.
This project has resulted in 10 peer-reviewed publications in leading international journals. Four post-doctoral researchers were involved in related research, and four graduate students wrote their PhD theses involving their research activities provided by this project.
Last Modified: 10/29/2013
Modified by: Yen-Hsi Tsai
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