SBIR Phase I: Robust autonomy for robotics-based data collection in surface water environments

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 1843049
Agency Tracking Number: 1843049
Amount: $225,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: IT
Solicitation Number: N/A
Solicitation Year: 2018
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-02-01
Award End Date (Contract End Date): 2020-01-31
Small Business Information
195 Adams St Apt 14D, Brooklyn, NY, 11201
DUNS: 080692685
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Jeffrey Laut
 (718) 614-5807
Business Contact
 Jeffrey Laut
Phone: (718) 614-5807
Research Institution
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project lies in the increase in efficiency and reduction in cost of gathering information in surface water environments using unmanned surface vehicles (USVs). USVs have the potential to increase accessibility to bodies of water, enhance coverage, and make data collection safer by removing humans from the task. However, to fully realize the potential of USVs for gathering information, a level of autonomy with minimal need for human supervision and intervention is required. The development of this technology will open the door to the pervasive use of USVs for environmental data collection, and ultimately benefit society through: i) more information upon which to base decisions about resource management and environmental policy; ii) higher frequency of data collection activities through reduced cost; and iii) increased safety by keeping human personnel out of harsh environments. This Small Business Innovation Research (SBIR) Phase I project will lay the foundation for a transformative approach to environmental data collection in surface waters. The intellectual merit of this project lies in addressing technical challenges that will enable unmanned surface vehicles (USVs) to effectively navigate autonomously in complex surface waters that may contain a range of diverse obstacles, which would otherwise be inaccessible or at best require a high degree of human supervision. This will be achieved by advancing the state-of-the-art in computer vision and machine learning toward intelligent obstacle characterization. The proposed framework interprets images or video of obstacles to extract salient features, such as flexibility or rigidity, through the innovative use of machine learning, dimensionality reduction, and tools borrowed from the analysis of collective behavior. This extra layer of information provided through the proposed framework represents a significant advancement in computer vision, which offers direct benefits to environmental monitoring and data collection by empowering USVs with new unmanned capabilities. 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.

* Information listed above is at the time of submission. *

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