Award Abstract # 1762577
CDS&E: A Computational Framework for Parsimonious Sonar Sensing

NSF Org: CMMI
Division of Civil, Mechanical, and Manufacturing Innovation
Recipient: VIRGINIA POLYTECHNIC INSTITUTE & STATE UNIVERSITY
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 2018 = $668,465.00
FY 2020 = $45,617.00
History of Investigator:
  • Hongxiao Zhu (Principal Investigator)
    hongxiao@vt.edu
  • Rolf Mueller (Co-Principal Investigator)
  • Xiaowei Wu (Co-Principal Investigator)
  • Pratap Tokekar (Co-Principal Investigator)
Recipient Sponsored Research Office: Virginia Polytechnic Institute and State University
300 TURNER ST NW
BLACKSBURG
VA  US  24060-3359
(540)231-5281
Sponsor Congressional District: 09
Primary Place of Performance: Virginia Polytechnic Institute and State University
403H Hutcheson Hall
Blacksburg
VA  US  24061-0001
Primary Place of Performance
Congressional District:
09
Unique Entity Identifier (UEI): QDE5UHE5XD16
Parent UEI: X6KEFGLHSJX7
NSF Program(s): GOALI-Grnt Opp Acad Lia wIndus,
CDS&E
Primary Program Source: 01001819DB NSF RESEARCH & RELATED ACTIVIT
01002021DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 016Z, 019Z, 026Z, 030E, 034E, 042E, 082E, 1504, 8084, 9263
Program Element Code(s): 150400, 808400
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

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Dhami, Harnaik and Yu, Kevin and Xu, Tianshu and Zhu, Qian and Dhakal, Kshitiz and Friel, James and Li, Song and Tokekar, Pratap "Crop Height and Plot Estimation for Phenotyping from Unmanned Aerial Vehicles using 3D LiDAR" International Conference on Intelligent Robots and Systems (IROS) , 2021 https://doi.org/10.1109/IROS45743.2020.9341343 Citation Details
Huo, Shuning and Morris, Jeffrey S. and Zhu, Hongxiao "Ultra-Fast Approximate Inference Using Variational Functional Mixed Models" Journal of Computational and Graphical Statistics , v.32 , 2023 https://doi.org/10.1080/10618600.2022.2107532 Citation Details
Tanveer, M. H. and Thomas, A. and Wu, X. and Mueller, R. and Tokekar, P. and Zhu, H. "Recreating Bat Behavior on Quad-Rotor UAVsA Simulation Approach" The Thirty-Third International FLAIRS Conference (FLAIRS-33) , 2020 Citation Details
Tanveer, M. H. and Thomas, A. and Wu, X. and Zhu, H. "Simulate Forest Trees by Integrating L-System and 3D CAD Files" 2020 3rd International Conference on Information and Computer Technologies (ICICT) , 2020 Citation Details
Tanveer, M. Hassan and Wu, Xiaowei and Thomas, Antony and Ming, Chen and Müller, Rolf and Tokekar, Pratap and Zhu, Hongxiao "A simulation framework for bio-inspired sonar sensing with Unmanned Aerial Vehicles" PLOS ONE , v.15 , 2020 https://doi.org/10.1371/journal.pone.0241443 Citation Details
Tanveer, M. Hassan and Zhu, Hongxiao and Ahmed, Waqar and Thomas, Antony and Imran, Basit Muhammad and Salman, Muhammad "Mel-spectrogram and Deep CNN Based Representation Learning from Bio-Sonar Implementation on UAVs" 2021 International Conference on Computer, Control and Robotics (ICCCR) , 2021 https://doi.org/10.1109/ICCCR49711.2021.9349416 Citation Details
Tanveer, Muhammad Hassan and Thomas, Antony and Ahmed, Waqar and Zhu, Hongxiao "Estimate the Unknown Environment with Biosonar EchoesA Simulation Study" Sensors , v.21 , 2021 https://doi.org/10.3390/s21124186 Citation Details
Wahed, M. and Islam, M. and Wu, X. and Zhu, H. "Fast Simulation of Trees and Forests for Bat-inspired Sonar Sensing" 2022 5th International Conference on Information and Computer Technologies (ICICT) , 2022 https://doi.org/10.1109/ICICT55905.2022.00041 Citation Details
Zhu, Hongxiao and Gupta, Anupam Kumar and Wu, Xiaowei and Goldsworthy, Michael and Wang, Ruihao and Mikkilineni, Mohitha and Müller, Rolf "A validation study for a bat-inspired sonar sensing simulator" PLOS ONE , v.18 , 2023 https://doi.org/10.1371/journal.pone.0280631 Citation Details

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

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