Real-time Hazard Detection via Deep Learning

HazNet is a robust hazard detection solution that leverages deep learning and hardware acceleration to achieve mission-speed performance on path-to-flight hardware. The HazNet solution seeks to maximize data use while maintaining flexibility by leveraging the independent strengths of LiDAR and camera data to produce a single hazard map. Flexibility is maintained by using two independent convolutional neural networks for computation, one for LiDAR data and one for image data, which are combined into an existing hazard map to improve knowledge, resolve unknown regions, and increase hazard map resolution. This method de-risks the transition from traditional hazard detection to deep learning-based algorithms by leveraging well-proven, rather than experimental models to identify hazards. It improves upon these traditional methods by acting on the strengths of complimentary sensors, enabled by hardware acceleration. Astrobotic proposes the development of a prototype sensor package for HazNet, while further advancing developed hazard detection models and techniques. This Phase II effort will entail five major efforts: working in collaboration with NASA and Astrobotic stakeholders to develop a reference mission and associated requirements; advancement of models developed in the Phase I effort to incorporate uncertainty and combine hazard map outputs; development and testing of custom sensor package; demonstration in a series of relevant simulations; and a final technology demonstration across a descent-like scenario in a lunar-relevant environment.

Generally speaking, as NASA targets increasingly complex and challenging landing scenarios on the Moon, asteroids, Mars, icy moons, and beyond, the Agency and its commercial contractors will be looking for flexible systems like HazNet which utilize as much data as possible and as little hardware as possible to produce accurate landing solutions. HazNet will be a valuable tool not only for future CLPS missions, but also for NASA’s forthcoming Artemis landings, which are also targeting rugged polar sites.

HazNet’s generation of real-time maps would benefit the Department of Defense (DOD) in identifying and tracking hazards in real time. It would benefit the Department of the Interior (DOI) and Department of Agriculture (USDA), as terrain models aid in the understanding of agricultural needs, geological impacts of climate change, and improved understanding of forests, oceans, and remote regions.

This is early/mid-stage (TRL 5) — the most realistic path in is NASA SBIR/STTR, which funds small businesses and research institutions to develop technology aligned with NASA's needs (equity-free, phased funding). Check whether a current SBIR/STTR solicitation topic overlaps with this project's technology area, or contact the project directly (above) to ask.

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