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The Award database is continually updated throughout the year. As a result, data for FY22 is not expected to be complete until September, 2023.

Download all SBIR.gov award data either with award abstracts (290MB) or without award abstracts (65MB). A data dictionary and additional information is located on the Data Resource Page. Files are refreshed monthly.

  1. In Situ Inspection of Additive Manufactured Metallic Parts Using Laser Ultrasonics

    SBC: INTELLIGENT OPTICAL SYSTEMS INC            Topic: N15AT008

    Additive manufacturing (AM) is a very promising technique for rapid, low-cost production of aircraft parts directly from a CAD file. AM is especially appealing for complex parts that would be costly or impossible to fabricate by machining or casting. At the current time there are no reliable, cost-effective techniques to qualify the finished parts. Several government studies have noted this gap an ...

    STTR Phase I 2015 Department of DefenseNavy
  2. In Situ Inspection of Additive Manufactured Metallic Parts Using Laser Ultrasonics

    SBC: INTELLIGENT OPTICAL SYSTEMS INC            Topic: N15AT008

    Additive manufacturing (AM) is a very promising technique for rapid, low-cost production of aircraft parts directly from a CAD file. AM is especially appealing for complex parts that would be costly or impossible to fabricate by machining or casting. At the current time there are no reliable, cost-effective techniques to qualify the finished parts. Several government studies have noted this gap an ...

    STTR Phase II 2016 Department of DefenseNavy
  3. LEARNING-BASED APPROACH FOR RELEVANT DATA EXTRACTION (LARDE)

    SBC: ROBOTIC RESEARCH OPCO LLC            Topic: N13AT016

    Robotic Research, LLC (RR) and Southwest Research Institute (SwRI) are creating a prototype Learning-based Approach for Relevant Data Extraction (LARDE). The LARDE framework is a data extraction and handling framework that can intelligently reduce the volume of raw data from on-board sensors, and organize and persistently store the reduced relevant dataset.

    STTR Phase II 2015 Department of DefenseNavy
  4. Computational Methods for Dynamic Scene Reconstruction

    SBC: ROBOTIC RESEARCH OPCO LLC            Topic: N16AT017

    Reconstruction of dynamic scenes is at the limits of the state of the art. It is still challenging to accurately reconstruct models in static scenes. Dynamic scenes add a list of challenges that further complicate the problem:separating the dynamic objects from the motion created by the camera motionMorphological changes to the dynamic object itself. Not only is the system moving, but it is actual ...

    STTR Phase I 2016 Department of DefenseNavy
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