GRAMS is a modular cognitive architecture for autonomous spacecraft failure detection, mitigation, and response, developed by VISIMO through a successful NASA SBIR Phase I effort.
The relevant public case-study content is organized here for readability and technical review.
NASA's Graceful Architecture for Mitigation of System Failures project addresses long-duration mission risk by combining machine learning, digital twin simulation, and autonomous decision support. Phase I validated the feasibility of detecting and responding to spacecraft failures in real time.
Deep-space missions cannot rely on immediate Earth-based intervention. Communications delays, hardware failures, radiation exposure, micrometeoroid impacts, and cascading faults can make real-time autonomous response essential.
Autonomous spacecraft systems need to detect anomalies, model likely failure paths, recommend corrective action, and adapt across mission profiles without depending on constant ground control.
GRAMS uses a modular cognitive architecture that combines anomaly detection, alert routing, failure simulation, digital twin modeling, and recommended corrective action.
The system was tested in simulated ISS-relevant environments with scenarios such as pressure leaks, sensor malfunctions, and pump failures. VISIMO deployed GRAMS on Hewlett Packard Enterprise's Spaceborne Computer-2 Test and Development System to validate compatibility with space-grade computing constraints.
The Phase I effort exceeded anomaly-detection benchmarks, validated digital twin modeling against complex failure scenarios, and positioned GRAMS for further validation in an ISS testing pathway.
Where the work can be applied or adapted.
Support autonomous anomaly detection and mitigation for deep-space crewed missions, lunar missions, Mars missions, and robotic probes.
Improve resilience and operational continuity for low Earth orbit platforms, space stations, and satellite operations.
Adapt the architecture to unmanned aerial systems, nuclear facilities, chemical plants, and other complex systems where failure management matters.
GRAMS demonstrates a path toward resilient autonomous operations by pairing failure detection with simulation, recommendation, and modular integration.
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VISIMO works with federal stakeholders, primes, research institutions, and technical collaborators on focused AI R&D efforts where software, data, and model evaluation can create practical mission value.