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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/ML Aided Aviation Sensors for Cognitive and Decision Optimization

    SBC: Mente Systems Inc.            Topic: SOCOM23B001

    Sensor systems aboard aircrafts address unique problems and are siloed in their objectives. A data silo is a term used to describe a data system that is insulated from other data systems. While keeping information categorized may lead to easier organization, the costs often outweigh the benefits. In aviation systems, data silos often lead to miscommunication, cognitive overload, and waste. These d ...

    STTR Phase I 2023 Department of DefenseSpecial Operations Command
  5. AI/ML Aided Aviation Sensors for Cognitive and Decision Optimization

    SBC: XR 2 LEAD LLC            Topic: SOCOM23B001

    In manned aviation environments – both military and commercial – AI support is being developed and researched for ground-based planning and operational decision support. Using AI in real-time with crews poses additional questions and issues. This research will provide the means to understand the measures and requirements to architect potential AI-Agent solutions before implementation. This fea ...

    STTR Phase I 2023 Department of DefenseSpecial Operations Command
  6. Human Automation Teaming for Efficient Knowledge Extraction and Test Generation

    SBC: BARRON ASSOCIATES, INC.            Topic: N23AT014

    Effective tools for evaluating the proficiency of warfighters at employing complex systems in an operational setting are essential to ensure operational capability within the Navy and other branches of the DoD. Generating exams with answer keys is a time-consuming process, made more difficult by the complexity of both the systems and the operational employment of those systems.  The burden is fur ...

    STTR Phase I 2023 Department of DefenseNavy
  7. Knowledge Extraction for the Evaluation of Learning

    SBC: IN-DEPTH ENGINEERING CORPORATION            Topic: N23AT014

    To maintain undersea dominance in an environment of near peer adversaries the Navy must deploy new capabilities rapidly. In response, development contractors are adopting a DevSecOps culture with agile processes to meet the demand for new technology deployment. To keep pace, the training community needs innovative technology to rapidly update training knowledge bases and automatically generate exa ...

    STTR Phase I 2023 Department of DefenseNavy
  8. 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
  9. Multi-cell X-ray target with energy recuperation

    SBC: RADIABEAM TECHNOLOGIES, LLC            Topic: HR0011ST2023D01

    Food irradiation technology has attracted more attention in the past decade given that it can provide a sustainable solution on how to address pathogen contamination in food. X-ray irradiation has several advantages compared to other irradiation technologies including superior penetrating quality compared to electron beams, and considerably larger dose rate compared to gamma rays. However, the low ...

    STTR Phase I 2023 Department of DefenseDefense Advanced Research Projects Agency
  10. 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
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