
NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
Recipient: |
|
Initial Amendment Date: | July 31, 2018 |
Latest Amendment Date: | October 15, 2020 |
Award Number: | 1762577 |
Award Instrument: | Standard Grant |
Program Manager: |
Reha Uzsoy
ruzsoy@nsf.gov (703)292-2681 CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
Start Date: | August 1, 2018 |
End Date: | July 31, 2023 (Estimated) |
Total Intended Award Amount: | $668,465.00 |
Total Awarded Amount to Date: | $714,082.00 |
Funds Obligated to Date: |
FY 2020 = $45,617.00 |
History of Investigator: |
|
Recipient Sponsored Research Office: |
300 TURNER ST NW BLACKSBURG VA US 24060-3359 (540)231-5281 |
Sponsor Congressional District: |
|
Primary Place of Performance: |
403H Hutcheson Hall Blacksburg VA US 24061-0001 |
Primary Place of
Performance Congressional District: |
|
Unique Entity Identifier (UEI): |
|
Parent UEI: |
|
NSF Program(s): |
GOALI-Grnt Opp Acad Lia wIndus, CDS&E |
Primary Program Source: |
01002021DB NSF RESEARCH & RELATED ACTIVIT |
Program Reference Code(s): |
|
Program Element Code(s): |
|
Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.041 |
ABSTRACT
Autonomous sensing systems play an increasingly important role in health care, manufacturing, transportation, disaster response, national security and other activities. Sensing systems in platforms such as autonomous vehicles often require clear lines-of-sight and are inefficient. They generate large amounts of data that cannot be processed by small platforms with limited onboard computation. This project lays the foundation for a new transformative data-centered efficient sensing platform based on bats' bio-sonar. The researchers will develop a computational framework that facilitates estimation of environmental parameters for sensing and navigation--enabling enhanced performance in complex environments and with reduced hardware requirements. This enables the use of autonomous devices for a broad scope of applications ranging from cooperative robots to medical devices and micro-air vehicles. The project will also offer multidisciplinary training to university students and will engage K-12 students in outreach activities.
This project will develop a computational approach to advance the discovery and understanding of parsimonious sonar sensing in complex environments. The researchers will perform large-scale simulations to generate densely vegetated environments and vegetation echoes; develop novel statistical models to extract echo signatures and learn their relation to environmental parameters and design dynamic algorithms for sensing and navigation. This computational approach provides enabling tools--validated using simulated and experimental data--to advance the community's ability to develop adaptive models for various sensing scenarios and enable novel sensing paradigms. Simulation algorithms and algorithms for sensing and navigation will be integrated into open source packages and made available on GitHub. The conceptual framework will be published in appropriate journals.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
Note:
When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external
site maintained by the publisher. Some full text articles may not yet be available without a
charge during the embargo (administrative interval).
Some links on this page may take you to non-federal websites. Their policies may differ from
this site.
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.
Intellectual merit. Existing sensing systems are often limited as they usually produce large amounts of sensory data that are expensive to be processed. These systems are typically confined to operation in open spaces or structured urban environments. In contrast, echolocating bats showcase the potential for efficient sensing using small, affordable transducers within intricate natural settings. This grant allows the PIs to develop a computational framework in order to gain deeper understanding of bats' biosonar systems and replicate their capabilities. Leveraging numerical simulations, the PIs devised techniques to simulate geometries of plants and structures of landscape that resemble the natural habitats of bats. These techniques were then integrated with acoustic models and deep generative models to simulate foliage echoes received by bats' biosonar systems. In this pursuit, the PIs harnessed state-of-the-art statistical models and machine learning techniques to tackle specific tasks like generating leaf impulse responses, identifying systematic differences between biosonar signals, and estimating environmental parameters. To further validate of the fidelity of the simulated data, the PIs conducted experiments to collect actual foliage echoes. Subsequent data analyses were performed to compare statistical attributes of the simulated echoes with those obtained from experimentation.
Broader impacts. This project enables the generation of rich sensory data by emulating the biosonar systems of bats. The research outputs promote small, low-cost sensors to be used in compact systems such as micro-air vehicles, thus can potentially benefit various aspects of society. Furthermore, this project furnishes a group of students with cross-disciplinary training and research exposure, offering them valuable opportunities to develop data-oriented thinking and problem-solving skills. The PIs have also been actively involved in educating K-12 students and a wider audience through outreach activities, achieved by collaborating with institutions such as the Smithsonian National Museum of Natural History, Virginia Tech's Center for the Enhancement of Engineering Diversity (CEED), and the local library.
Last Modified: 10/02/2023
Modified by: Hongxiao Zhu
Please report errors in award information by writing to: awardsearch@nsf.gov.