Advancement of Deep Learning and Geometric Methods for Active Terrain Relative Navigation

To enhance NASA’s precision landing capabilities, in conjunction with the maturation of a novel active terrain relative navigation and hazard mapping system, this work will perform a comparative analysis between both deep-learning (DL) based and geometric approaches to hazard detection (HD) and safe-site-identification (SSI) through hardware-in-the loop testing on the Six degree-of-freedom Tendon Actuated Robot (STAR).

This study would address the research gap in active terrain relative navigation related work by performing a comprehensive analysis of the real-time performance of state-of-the-art approaches to hazard detection and safe-site-identification to inform future requirements and development across the space exploration industry and future NASA missions. This work would also serve as a benchmark analysis for deep-learning based approaches to navigation to evaluate their prospective efficacy in space flight operations.

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