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Studying Passive Dust Mitigation on Anisotropic Structured Surface 


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Preparations for Next Moonwalk Simulations Underway (and Underwater)

ESI24 Zhai Quadchart

Lei Zhai
University of Central Florida

Lunar dust, with its chemical reactivity, electrostatic charge, and potential magnetism, poses a serious threat to astronauts and equipment on the Moon’s surface. To address this, the project proposes developing structured coatings with anisotropic surface features and electrostatic dissipative properties to passively mitigate lunar dust. By analyzing lunar dust-surface interactions at multiple scales, the team aims to optimize the coatings’ surface structures and physical properties, such as Young’s modulus, electrical conductivity, and polarity. The project will examine tribocharging, external electric fields, and the effects of particle shapes and sizes. Numerical sensitivity analyses will complement simulations to better understand lunar dust dynamics. Once fabricated, the coatings will be tested under simulated lunar conditions. The team will employ a state-of-the-art nanoscale force spectroscopy system, using atomic force microsope (AFM) microcantilevers functionalized with regolith to measure dust-surface interactions. Additional experiments will assess particle adhesion and removal, with scanning electron microscopy used to analyze remaining dust. This project aims to provide insights into surface structure effects on dust adhesion, guiding the creation of lightweight, durable coatings for effective dust mitigation. The findings will foster collaborations with NASA and the aerospace industry, while offering training opportunities for students entering the field.

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      To learn more visit: https://www.nasa.gov/stmd-game-changing-development/

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      Figure 7. Screenshot of OceanWATERS lander on a terrain modeled from the Atacama Desert. A scoop operation has just been completed. NASA/JPL – Caltech Because of Earth’s distance from the ocean worlds and the resulting communication lag, a planetary lander should be programmed with at least enough information to begin its mission. But there will be situation-specific challenges that will require onboard intelligence, such as deciding exactly where and how to collect samples, dealing with unexpected issues and hardware faults, and prioritizing operations based on remaining power. 
      Results
      All six of the research teams funded by the ARROW and COLDTech programs used OceanWATERS to develop ocean world lander autonomy technology and three of those teams also used OWLAT. The products of these efforts were published in technical papers, and resulted in development of software that may be used or adapted for actual ocean world lander missions in the future. The following table summarizes the ARROW and COLDTech efforts.
        Principal Investigator (PI) PI Institution Project Testbed Used Purpose of Project ARROW Projects Jonathan Bohren Honeybee Robotics Stochastic PLEXIL (SPLEXIL) OceanWATERS Extended PLEXIL with stochastic decision-making capabilities by employing reinforcement learning techniques. Pooyan Jamshidi University of South Carolina Resource Adaptive Software Purpose-Built for Extraordinary Robotic Research Yields (RASPBERRY SI) OceanWATERS & OWLAT Developed software algorithms and tools for fault root cause identification, causal debugging, causal optimization, and causal-induced verification. COLDTech Projects Eric Dixon Lockheed Martin Causal And Reinforcement Learning (CARL) for COLDTech OceanWATERS Integrated a model of JPL’s mission-ready Cold Operable Lunar Deployable Arm (COLDarm) into OceanWATERS and applied image analysis, causal reasoning, and machine learning models to identify and mitigate the root causes of faults, such as ice buildup on the arm’s end effector. Jay McMahon University of Colorado Robust Exploration with Autonomous Science On-board, Ranked Evaluation of Contingent Opportunities for Uninterrupted Remote Science Exploration (REASON-RECOURSE) OceanWATERS Applied automated planning with formal methods to maximize science return of the lander while minimizing communication with ground team on Earth. Melkior Ornik U Illinois, Urbana-Champaign aDaptive, ResIlient Learning-enabLed oceAn World AutonomY (DRILLAWAY) OceanWATERS & OWLAT Developed autonomous adaptation to novel terrains and selecting scooping actions based on the available image data and limited experience by transferring the scooping procedure learned from a low-fidelity testbed to the high-fidelity OWLAT testbed. Joel Burdick Caltech Robust, Explainable Autonomy for Scientific Icy Moon Operations (REASIMO) OceanWATERS & OWLAT Developed autonomous 1) detection and identification of off-nominal conditions and procedures for recovery from those conditions, and 2) sample site selection Acknowledgements: The portion of the research carried out at the Jet Propulsion Laboratory, California Institute of Technology was performed under a contract with the National Aeronautics and Space Administration (80NM0018D0004).  The portion of the research carried out by employees of KBR Wyle Services LLC at NASA Ames Research Center was performed under a contract with the National Aeronautics and Space Administration (80ARC020D0010). Both were funded by the Planetary Science Division ARROW and COLDTech programs.
      Project Leads: Hari Nayar (NASA Jet Propulsion Laboratory, California Institute of Technology), K. Michael Dalal (KBR, Inc. at NASA Ames Research Center)
      Sponsoring Organizations: NASA SMD PESTO
      View the full article
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