Advanced Space will investigate multiple activities supporting the exploration, development, and settlement of space, including techniques that support low lunar orbit space situational awareness (SSA), deep-space traffic management, and relative navigation and mapping for Rendezvous, Proximity Operations, and Docking (RPOD) scenarios.
WESTMINSTER, CO, 10 September 2025 – NASA has recently awarded Advanced Space two Small Business Innovation Research (SBIR) Phase I contracts and one Small Business Technology Transfer (STTR) Phase I contract. These projects will enable the company and its partners to prove the feasibility of close-approach risk assessment of spacecraft at the Moon using space-based sensors, a deep-space object catalog to ensure safety of flight, and vision-based algorithms to provide relative navigation and mapping for spacecraft or asteroids in poor lighting conditions. These projects include:
“Lunar Sentinel” – In partnership with AstronetX PBC (AstronetX), we will study deploying sensors in space, which includes satellites in low lunar orbit (LLO) or an array of telescopes at future lunar landing sites, to provide close-approach risk assessment of spacecraft orbiting the Moon. This study leverages AstronetX’s Lunar-Camera (L-CAM) space-based sensor, for which AstronetX has a Phase III contract with the Air Force Research Laboratory (AFRL) to mature the technology to provide unique size, weight, power, and cost (SWaP-C) efficient optical tracking data services aboard commercial cislunar spacecrafts. Lunar Sentinel builds upon the Cislunar Autonomous Positioning System Technology Operations and Navigation Experiment (CAPSTONE™) mission, which has provided strategic lessons learned in cislunar operations, navigation, and communications for NASA. The primary goal of this effort is to support the growing cislunar market with a framework for an accurate SSA architecture capable of detecting, tracking, and maintaining custody of spacecraft in low lunar orbits with precision PNT capabilities.
“Deep-space Risk Assessment for non-Gaussian Operations and Navigation” – Advanced Space and our partner Utah State University (USU) will generate a deep-space object catalog with a comprehensive conjunction risk assessment process to ensure continued use of non-Earth-orbit regimes. The specific improvements will use advanced mathematical tools, including Gaussian Mixture Models (GMMs) and Directional State Transition Tensor (DSTTs), which represent uncertainties more realistically by reflecting the actual complexity of deep space behavior. The goal of this effort is to enhance the catalog of human-made objects in deep space by improving the agency’s ability to identify the risks of spacecraft conjunctions and collisions in non-Earth-orbit regimes. Our study addresses the demand for enhanced, comprehensive collision risk assessment for objects at the Moon, the lack of tracking data, and the reliance on operator-provided ephemerides for catalog maintenance.
“Relative Navigation and Mapping using Visual Points Clouds and Neural View Synthesis” – Under the H9.03 subtopic and working with The University of Colorado, Boulder (CU Boulder), we will study an RPOD-focused, monocular optical navigation and mapping pipeline for unknown, natural, or unprepared space objects that is robust to the harsh lighting conditions of space. The result of this approach will be a system that allows spacecraft to perform RPOD operations with an unfamiliar object such as an unexplored asteroid or inactive spacecraft. These algorithms will generate dense, relightable, and photorealistic object reconstructions that could aid operators in interpreting target object characteristics.
Advanced Space has a robust history using funding from NASA under the H9.03 subtopic to commercialize new flight dynamics and navigation technologies with Johnson Space Center (JSC), Goddard Space Flight Center (GSFC), and Marshall Space Flight Center (MSFC). The first Phase I SBIR for Advanced Space from 2015 led to a Phase III commercial services contract with multiple years of support to JSC for mission design and navigation studies for the Lunar Gateway and Artemis campaign. The second program, called SLALOM, focused on lunar terrain relative navigation in a very low lunar orbit (~10 km). The third project, Neural Networks for Enhanced Planning (NNEP), used machine learning (ML) techniques in the form of neural networks to do onboard station keeping and maneuver planning. NNEP was tested onboard the CAPSTONE spacecraft at the Moon to demonstrate its safety check mechanism and decision-making process for maneuver planning. The current project is Contingency Analysis for Low-thrust Missions (CALM), which aims to generate a mission design tool to mitigate the impact of anomalies and missed-thrust events on low-thrust and dynamically sensitive missions.
Together, these efforts will enable Advanced Space and its partners to support NASA to improve the exploration, development, and settlement of space.