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The Award database is continually updated throughout the year. As a result, data for FY24 is not expected to be complete until March, 2025.

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.

The SBIR.gov award data files now contain the required fields to calculate award timeliness for individual awards or for an agency or branch. Additional information on calculating award timeliness is available on the Data Resource Page.

  1. Deep Reinforcement Learning for Collaborative Multi-Robot Systems with Low-Latency Wireless Networking

    SBC: TIAMI LLC            Topic: N23BT031

    In this Phase I effort, Tiami, LLC, aims to develop and demonstrate a hardware proof of concept for a collaborative multi-robot system (MRS) that leverages imitative augmented deep reinforcement learning (IADRL) amongst heterogeneous uncrewed systems (robots) to achieve a common task. Collaboration is based on low-latency machine-to-machine wireless links between robots that use both RF and optica ...

    STTR Phase I 2023 Department of DefenseNavy
  2. Ad Hoc Swarm Modulation and Adaptation

    SBC: IOTAI INC            Topic: N23BT031

    Ad Hoc Swarm Modulation and Adaptation focuses on the ability to enable secure cyber communications, data, and distributed AI processing for any robotic swarm in any condition.  The system incorporates a range of multi-robotic system functionality to allow for coordination, cooperation, and reconfigurable methods of robotic teams, flocks, and swarms.  The system further includes methods for swar ...

    STTR Phase I 2023 Department of DefenseNavy
  3. AI/ML Aided Aviation Sensors for Cognitive and Decision Optimization

    SBC: KRTKL INC.            Topic: SOCOM23B001

    krtkl (“critical”) will conduct a Phase I Feasibility Study to identify the best approach for reducing aviator cognitive load by optimizing information delivery and decision-making based on a thorough analysis of existing platforms, sensors, data sources, and onboard compute resources. This information will be used to identify Artificial Intelligence and Machine Learning based algorithms for p ...

    STTR Phase I 2023 Department of DefenseSpecial Operations Command
  4. AI-Based Learning Environment (ABLE) for Undersea Warfare (USW) Training

    SBC: PACIFIC SCIENCE & ENGINEERING GROUP, INC.            Topic: N23AT014

    To compete on the world stage of undersea warfare (USW), the US Navy’s USW systems are frequently updated with advanced capabilities. As a result, modernization trainers need to perform the challenging tasks of updating training material to reflect the new (and obsolete) capabilities. This process requires comparing legacy to updated documentation, identifying changes to system capabilities, and ...

    STTR Phase I 2023 Department of DefenseNavy
  5. UUV Sensor Transformation

    SBC: Arete Associates            Topic: N23AT013

    Areté and its teaming partner the University of Arizona (UofA) will develop a software tool that transforms sensor and metadata from a given sensor system into realistic synthetic data as if it were collected by a different sensor system. The exponential rise in available data from a multitude of sensor systems has driven commercial and academic entities to achieve significant innovations in arti ...

    STTR Phase I 2023 Department of DefenseNavy
  6. Time Resolved Multiparameter Flow Diagnostic for Engine Exhaust Plumes

    SBC: METROLASER, INCORPORATED            Topic: N23AT005

    High temperature jet plumes emanating from aircraft engines and missiles produce effects that are of interest for threat detection, environmental noise, and engine development purposes. Optical and infrared emissions from plumes are sources of light and heat signatures, respectively, that can potentially be used for tracking or targeting vehicles in flight.  Acoustic noise from jet plumes can pot ...

    STTR Phase I 2023 Department of DefenseNavy
  7. A Massively Parallel Scalable Processor for Order of Magnitude Increase in Acceleration of Photonic Simulations

    SBC: VIRTUAL EM INC.            Topic: N23AT008

    Virtual EM proposes develop a massively parallel ASIC for orders of mangnitude speed up of electromagnetic simulations of thin optical lenses made of metamaterials. The ASIC will implement a prorietary algorithm and will deliver scalable run-times that cut the simulation time by more than 1000x compared to today's state-of-the-art simulators.

    STTR Phase I 2023 Department of DefenseNavy
  8. Atmospheric Aerosol Model and Data Collection Over the Marine Boundary Layer...

    SBC: N.P. PHOTONICS, INC.            Topic: N23AT012

    This overall research program is oriented towards a deeper experimental and theoretical understanding of meteorological properties of the marine wave boundary layer (MWBL) and laser light propagation in the marine environment. The main objective is to develop a periscope imaging, electronic warfare, and High Energy Laser (HEL) beam propagation model over the marine aerosol boundary layer for the i ...

    STTR Phase I 2023 Department of DefenseNavy
  9. Compact Condensers Enabled by Print-to-Cast Additive Manufacturing

    SBC: ERG Aerospace Corporation            Topic: N23AT024

    High power electronics are being increasingly limited by conventional single-phase thermal management technologies. Refrigerant two-phase cooling presents a significant opportunity for thermal management of high-power electronics. While recent advances in cold plates and evaporators have been demonstrated in high heat flux applications, the condenser-side of the two-phase coolant loop has not kept ...

    STTR Phase I 2023 Department of DefenseNavy
  10. Compact Condensers Enabled by Additive Manufacturing

    SBC: WECOSO, INC.            Topic: N23AT024

    Thermal management using two-phase cooling systems offers significant advantages in terms of size, weight, and power reduction. These size reductions are mainly attributable to the evaporation part of the process, where heat transfer coefficients can be as high as 100 kW m-2 K-1. This is not the case for condensation, which can typically transfer lower heat fluxes at the same temperature differenc ...

    STTR Phase I 2023 Department of DefenseNavy
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