Open Modular System (OMS) Supported by Deployable Low SWaP (Size, Weight, and Power) AI/ML Solutions for Space and Aeronautics Edge Devices

As the numbers of autonomous systems collecting data increases the benefits and importance of performing more of the processing at the edge increases exponentially. Moving the analysis to the edge decreases bandwidth requirements and the latency between data collection and access to results, as well as enabling intelligent sensor operations.  ???????Little Place Labs is dedicated to developing and implementing a Size, Weight, and Power (SWaP) optimized Artificial Intelligence/Machine Learning (AI/ML) solution for space and aeronautics edge devices, with an absolute emphasis on deployments that are safe, secure and reliable. This process incorporates modern software development methodologies, including continuous integration and continuous delivery (CICD), ensuring rapid, reliable, and efficient deployment of software updates. The technical aspects of the research involve detailed design and development of a software framework tailored for operational use across multiple platforms, with an emphasis on agile development practices to facilitate quick iteration and responsiveness to changing requirements. A subsequent Phase 2 will allow us to perform a comprehensive design document and SWaP-optimized software for satellites and aeronautic assets like UAVs, HAPS, drones, supported by rigorous performance benchmarking. In line with ML Ops principles, this phase will ensure that the machine learning lifecycle - from development to deployment and monitoring - is streamlined and efficient. We will also use a network of edge devices to demonstrate where the effectiveness of the AI application in extracting intelligence from remote sensing imagery will be tested and analyzed.