Hyperdimensional AI for Autonomous Anomaly Response in Smart Habitats

This proposal introduces a novel brain-inspired anomaly detection and response framework based on Hyperdimensional Computing (HDC) for deployment in autonomous deep-space smart habitats. Unlike conventional AI models that require high-power GPUs and retraining for new anomalies, the proposed HDC-based system enables ultra-low-power, real-time, and explainable fault detection, isolation, and recovery (FDIR) capabilities. HDC’s distributed, symbolic vector representations provide exceptional robustness to noise, adaptability to previously unseen failures, and human-interpretable reasoning—critical for supporting astronaut-autonomy collaboration in long-duration space missions. Phase I funding will be used to design, implement, and validate an HDC prototype that performs onboard anomaly detection across multi-modal spacecraft telemetry, supports one-shot learning, and operates on reconfigurable hardware like FPGAs and embedded processors. The system will undergo rigorous fault injection testing and hardware-in-the-loop validation using NASA-relevant datasets to ensure operational resilience and real-time responsiveness in spaceflight conditions. Target markets include NASA, DoD, and commercial space companies requiring intelligent onboard health monitoring systems, as well as broader terrestrial markets in industrial IoT, aerospace, and critical infrastructure. This innovation aims to enhance mission autonomy, reduce operational costs, and extend AI-driven fault management to edge devices in both space and Earth-based applications.

The proposed HDC-based anomaly detection system directly supports NASA’s mission directives in enabling increased autonomy, fault resilience, and intelligent decision-making for long-duration space exploration. As NASA prepares for complex missions such as Artemis, Gateway, and Mars surface operations, the ability to perform onboard, low-power, and adaptive fault detection is critical for ensuring crew safety, reducing dependence on ground control, and maintaining system reliability in deep-space environments. The proposed system will serve as a core technology for autonomous Fault Detection, Isolation, and Recovery (FDIR) within smart habitats and distributed spacecraft subsystems. Its capability to process real-time telemetry data across life support systems, propulsion units, thermal control systems, and power management modules enables comprehensive situational awareness and preemptive fault response without requiring extensive pre-labeled training data. The integration of HDC on reconfigurable hardware platforms like FPGAs further ensures compatibility with flight-ready systems under strict Size, Weight, and Power (SWaP) constraints. The approach is highly relevant for supporting human-autonomy teaming, real-time mission health management, and adaptive system reconfiguration during communication latency or failure. Additionally, the system can enhance onboard autonomy for robotic exploration missions, space logistics vehicles, and interplanetary probes by providing robust, low-power cognitive processing for anomaly detection and control adaptation, fulfilling key objectives outlined in NASA’s Technology Roadmaps (TA11, TA04, TA06). The proposed HDC-based anomaly detection system offers significant commercialization opportunities across multiple high-impact sectors beyond NASA, particularly in domains requiring real-time, low-power, and robust fault detection in complex autonomous systems. Key application areas include aerospace and defense, industrial IoT, critical infrastructure monitoring, autonomous vehicles, and edge AI systems. In the aerospace sector, the technology can be deployed for onboard health monitoring of commercial aircraft, UAVs, and next-generation spaceplanes, where its energy efficiency and ability to detect novel faults without retraining offer considerable operational advantages. In the industrial domain, the system can be integrated into predictive maintenance platforms for power grids, manufacturing equipment, and energy systems to detect faults and prevent costly downtime. In the rapidly growing autonomous vehicle market, the technology can enhance real-time sensor anomaly detection and fail-safe control decisions, improving reliability and safety. The system’s implementation on low-cost, reconfigurable hardware platforms such as FPGAs and RISC-V processors makes it ideal for edge computing applications in smart cities, logistics, and remote infrastructure. Its explainable, learning-efficient architecture provides a competitive edge over conventional deep learning solutions that require heavy computing resources and large datasets. The system can be commercialized as a standalone embedded solution or integrated into existing digital twin, cyber-physical system, and AI safety frameworks. With rising global demand for AI systems that are robust, energy-efficient, and adaptive, the proposed technology is well-positioned for broad adoption in both defense and commercial markets.

This is a mature technology (TRL 7+) — the realistic path in is usually NASA's Technology Transfer Program: licensing an existing NASA patent, or a Space Act Agreement to use NASA facilities/expertise directly. NASA also runs a startup licensing program with no upfront fee for companies formed to commercialize a specific NASA technology.

None of these are guaranteed paths for this specific project — TechPort itself doesn't have an "apply" button. Reaching out to the contact(s) above with a specific question is usually the fastest way to find out what's actually open.