An autonomous lunar landing system applicable to a wide variety of crewed and robotic lunar descent vehicles is under development as part of the ALHAT (Autonomous precision Landing and Hazard detection and Avoidance Technology) project. This system, referred to as the ALHAT System Module (ASM) is a highly advanced integrated sensor suite that enables landing a lunar descent vehicle within tens of meters of a certified and designated landing location anywhere on the Moon, under any lighting condition. This paper describes the basic ASM architecture and its novel concept of operations, and matures this architecture through description of top level lunar landing requirements.Working closely with NASA primary stakeholders, a fully developed ASM design will enable global lunar access for exploration of unique and challenging areas on the lunar surface never before visited. 12
Terrain relative navigation (TRN) offers a means to constrain absolute vehicle position and attitude without using a GPS receiver or star camera, such as for unmanned aerial vehicles or planetary landing spacecraft, using a pre-computed terrain database of distinctive landmarks in the operational environment. However, depending on the length of the planned trajectory, these terrain landmark databases may grow prohibitively large for the onboard data storage, processing, and communication capabilities of these types of vehicles.In this paper, we introduce a definition of landmark utilitywhich encapsulates the observation probability of a landmark and its spatial distribution relative to nearby landmarks in the database -as a measure of the value of potential line-ofsight measurements to that landmark. We additionally present an efficient algorithm to sort all potential landmarks in an environment based on their relative utility without access to the actual sensor measurements or trajectory. This enables pre-determination of a limited-size terrain landmark database containing the N -best landmarks in the environment, which is applicable to any landmark-based TRN system.To evaluate our landmark selection algorithm, we additionally introduce an incremental smoothing-based approach to TRN using a Bayesian factor graph representation. This system is capable of overcoming several challenges associated with TRN systems, including relinearization of past measurements, simultaneously utilizing pre-mapped and opportunistic features in a common estimator, and efficient sensor fusion in a common, probabilistic framework. We provide results showing the benefits of our utility-based landmark selection compared to alternative selection approaches, using evaluation metrics independent of any particular TRN estimation framework and the navigation performance of our graph-based TRN system.
An industry wide survey of GNC sensors, namely star trackers, gyros, and sun sensors was undertaken. Size, mass, power, and various performance metrics were recorded for each category. A multidimensional analysis was performed, looking at the spectrum of available sensors, with the intent of identifying gaps in the available capability range. Mission types that are not currently well served by the available components are discussed, as well as some missions that would be enabled by filling gaps in the component space.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.