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On February 4, the Air Force Research Lab (AFRL) released information about its Soaring Otter broad agency announcement (BAA). Responses are due by 3:00 p.m. Eastern on March 10, according to SAM.
Soaring Otter will be a one step, Closed BAA to advance, evaluate and mature Air Force autonomy capabilities, leveraging the latest advancements in both the fundamental science of autonomy and Machine Learning (ML) and the most modern computing technologies designed to support them.
The Air Force (AF) is increasingly employing the science of autonomy to solve complex problems related to global persistent awareness, resilient information sharing, and rapid decision making. Current autonomy approaches include a growing spectrum of techniques ranging from the well-understood to the truly novel: ML, Artificial Intelligence (AI), many varieties of Neural Networks, Neuromorphic Computing, Data Exploitation and others. Together with the rapid progress of autonomy algorithms and methodologies is the equally rapid progress of hardware and software designed to support the efficient execution of autonomy. These computing solutions bring new capabilities, but also new challenges, including how best to develop applications with them, and how to integrate them into larger systems.
The application space for autonomy is rapidly growing, with critical technologies like target identification and recognition, Positioning, Navigation and Timing (PNT) and Unmanned Aircraft Systems (UAS) route planning. Finally, how best to integrate and test these new solutions within reasonable constraints of cost and risk is still not well understood, and there is need for a well-defined progression from lab prototype, through realistic System Integration Lab (SIL) testing, finally through field and flight testing for Technology Readiness Level (TRL) increase.
The scope includes the following seven main topic areas:
Autonomy Development and Testing: Develop novel approaches to solving autonomy problems using the latest techniques in ML, neural networks, AI and other fields. Constantly seek to leverage the newest developments from both government and industry; mature existing approaches toward greater levels of robustness and determine early what is required for the eventual successful transition of these autonomy technologies to the warfighter.
Evaluation of Autonomy Capabilities: Provide neutral 3rd party evaluation of algorithms from Government, Academia and Industry. Provide unbiased analysis of alternatives for algorithms being produced by the Government, Industry and Academia to provide actionable information to AFRL about which algorithms are performing best against objective criteria, as well as determine which solutions are most ready for maturation and integration into systems. Design and perform trade studies to identify best-of-breed solutions and make recommendations to the Government for their application and further maturation.
Novel Computing Approaches: This area will focus on compact computing solutions that push processing to the edge for real or near real time solutions to support the warfighter. Assess the latest emerging computing architectures from government and industry, together with the latest approaches to efficiently developing applications using these technologies.
New Application Spaces: Evaluate emerging Air Force priorities and user requirements, to determine where autonomy can bring the greatest benefit, focusing on Intelligence, Surveillance, and Reconnaissance (ISR).
Open System Architectures for Autonomy: Assess existing and emerging Open System Architectures (OSAs) as fundamental elements of future autonomous systems.
Autonomy Technology Integration and Testing: Plan and execute paths by which new autonomy technologies can be rapidly integrated into larger systems for lab, SIL and field/flight testing.
Maturing System Support: Plan and execute technology transition and system transition activities for operational partners. System deployment support and participation, system integration, testing, and assessment support activities.
Full information is available here.
Source: SAM
