Systems Thinking for Organizational Design: Vision, Alignment, and Execution
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The goal of this project was the integration of NASA's Copernicus and Genesis trajectory design tools to create a new capability for end-to-end mission design and optimization of all flight phases, including Earth ascent, lunar ascent, rendezvous, and lunar descent. This capability is a critical component needed for the next generation of complex human spaceflight design and operations (Orion, HLS, Gateway, Mars) and can serve as a pathfinder for developing a future autonomous, onboard trajectory optimizer. The result was the creation of a new Copernicus/Genesis plugin, which allows a Genesis trajectory (e.g., ascent or descent) to be incorporated into a Copernicus mission, enabling an end-to-end optimization. Inputs from Copernicus are sent to Genesis, which runs and produces output sent back to Copernicus. Thus, the full power of the Genesis tool is available in Copernicus and can be used for a wide variety of applications.
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Hermes LunarG is a flight-proven research platform that can measure the effects of lunar gravity on different types of astromaterials using Blue Origin's reusable sub-orbital flight system. Hermes LunarG is part of the Strata-1 family of experiments that leverages International Space Station (ISS) flight hardware to investigate particle dynamics. The platform is a single payload locker consisting of four clear, polycarbonate tubes containing different regolith simulant materials. Onboard the New Shephard, once lunar gravity conditions are achieved, the payload releases the simulants to move freely in the tubes. The payload will capture images and sensor data to illustrate the dust settling effects and behavior of lunar regolith simulant in this environment. Funded by NASA STMD (Space Technology Mission Directorate) GCD (Game Changing Development) Program, the project has been developed by NASA, T STAR (Texas Space Technology Applications and Research) and Texas A&M's SOAR (Sub-Orbital Astromaterial Research) team to deliver a platform that will further address gaps in dust mitigation and improve the understanding of regolith in lunar gravity.
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NASA’s vision for Advanced Air Mobility (AAM) is to provide safe, sustainable, accessible, and affordable aviation for transformational local and intraregional missions and includes the transportation of passengers and cargo as well as aerial work missions, such as infrastructure inspection or search and rescue operations. NASA’s technical expertise, intergovernmental relationships, and high level of public trust will help this technology come to market concurrent with infrastructure readiness, public acceptance, and constructive regulation. By advising and integrating disparate AAM efforts across the country and working collaboratively with the FAA, the National Campaign (NC) objective is to motivate industry progress and support the development of policy, regulatory, and technical standards in a manner that best ensures public safety and benefit to the American people. NC began with the Dry Run and Developmental Test (NC-DT), which served as a pathfinder for collaboration and direct involvement with industry partners in integrated simulation exercises and flight tests. NC-DT culminated with an acoustics-gathering flight test in September 2021 with industry partner Joby’s prototype S4 2.0 air vehicle, which delivered the first foundational baseline of noise levels present in Electric Vertical Takeoff and Landing (eVTOL) vehicles. Through the DT phase, the NC team built and tested the airspace and range infrastructure while assessing the readiness level of industry partners leading up to future NC events. The purpose of this paper is to provide an overview of NC-DT.
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We present a novel use of Monte Carlo Tree Search (MCTS),adapted to explore a search space produced by the choice points embedded in Python code. The choice points are non-deterministic assignment statements and subroutine calls. We present MCTS extensions required for doing tree search in this context which includes control constructs like hierarchical decomposition (subroutine calls), iterative while loops and conditional statements. We demonstrate how the system works in a simulated rideshare scenario in an urban setting, and present preliminary experiments as a proof of concept.
In 2019, NASA’s Space Technology Mission Directorate (STMD) established the Lunar Surface Innovation Initiative (LSII) which has evolved into a key agency asset aimed at spurring technology development and providing risk reduction for lunar surface system and infrastructure development. In the five years since its inception, LSII’s work has serviced the needs of technology stakeholders from U.S. industry, academia, government agencies, non-profit institutions, and has expanded to include participation from international organizations. Continued domestic and foreign engagement has gained rapid momentum through LSII’s Lunar Surface Innovation Consortium (LSIC), an innovative approach that encourages public-private partnerships and collaboration across sectors to support NASA’s existing technology investments and prepare for increasingly complex lunar surface technology demonstrations.
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