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All sourcesNASA NTRSDOE OSTI
NASA NTRS1971

Lunar surface exploration

Experiences of living on moon, discussing effects on task performance and physical movements and observations of lunar surface, lighting and color

Armstrong, N.↗
NASA NTRS2012

NASA Human Spaceflight Architecture Team: Lunar Surface Exploration Strategies

NASA s agency wide Human Spaceflight Architecture Team (HAT) has been developing Design Reference Missions (DRMs) to support the ongoing effort to characterize NASA s future human exploration strategy. The DRM design effort includes specific articulations of transportation and surface elements, technologies and operations required to enable future human exploration of various destinations including the moon, Near Earth Asteroids (NEAs) and Mars as well as interim cis-lunar targets. In prior architecture studies, transportation concerns have dominated the analysis. As a result, an effort was made to study the human utilization strategy at each specific destination and the resultant impacts on the overall architecture design. In particular, this paper considers various lunar surface strategies as representative scenarios that could occur in a human lunar return, and demonstrates their alignment with the internationally developed Global Exploration Roadmap (GER).

Mueller, Rob P.↗
NASA NTRSDate not supplied

JETT3: A Holistic, Integrated Analog for Artemis Lunar Surface Exploration

Since 1972, NASA astronauts have performed hundreds of Extravehicular Activities (EVAs) in support of Skylab, Space Shuttle and International Space Station missions. Not since Apollo, however, have EVAs been driven by discovery-based principles of scientific exploration. Upcoming Artemis missions are challenged to build on lessons learned from Apollo, merging 50 years of EVA experience with the planetary science community’s expertise in the remote surface exploration of Mars. The highest-fidelity preparation for Artemis includes both operational and scientific underpinning to represent the complete, complex picture of lunar surface operations. The Joint EVA & Human Surface Mobility Test Team (JETT) is an interdisciplinary team providing such an environment for collaborative analog testing. JETT builds upon prior analog campaigns (e.g., [1, 2]) to provide high-fidelity environments for hardware and concept of operations development. Sponsored by the NASA EVA & Human Surface Mobility Program (EHP), JETT includes representatives from EHP, NASA Engineering, the Science Mission Directorate (SMD), Human Health & Performance, and the Flight Operations Directorate (FOD). JETT tests evaluate NASA reference designs for EVA (e.g., suits and tools), address gaps and risks for Artemis lunar surface operations, develop capabilities for EVA and science tasks, enable technology maturation, and provide training for Artemis EVA operations. JETT3, the final JETT field test of FY22, occurred Oct 3-11, 2022 in the San Francisco Volcanic Field north of Flagstaff, AZ. The test focused on developing the Artemis concept of operations and systems, including integrating an Artemis-like Science Team into a NASA Flight Control Team (FCT) to plan and execute a series of simulated lunar EVAs in an environment analogous to Artemis 3.

T. E. Caswell↗
NASA NTRSDate not supplied

JETT3: A Holistic, Integrated Analog for Artemis Lunar Surface Exploration

Since 1972, NASA astronauts have performed hundreds of Extravehicular Activities (EVAs) in support of Skylab, Space Shuttle and International Space Station missions. Not since Apollo, however, have EVAs been driven by discovery-based principles of scientific exploration. Upcoming Artemis missions are challenged to build on lessons learned from Apollo, merging 50 years of EVA experience with the planetary science community’s expertise in the remote surface exploration of Mars. The highest-fidelity preparation for Artemis includes both operational and scientific underpinning to represent the complete, complex picture of lunar surface operations. The Joint EVA & Human Surface Mobility Test Team (JETT) is an interdisciplinary team providing such an environment for collaborative analog testing. JETT builds upon prior analog campaigns (e.g., [1, 2]) to provide high-fidelity environments for hardware and concept of operations development. Sponsored by the NASA EVA & Human Surface Mobility Program (EHP), JETT includes representatives from EHP, NASA Engineering, the Science Mission Directorate (SMD), Human Health & Performance, and the Flight Operations Directorate (FOD). JETT tests evaluate NASA reference designs for EVA (e.g., suits and tools), address gaps and risks for Artemis lunar surface operations, develop capabilities for EVA and science tasks, enable technology maturation, and provide training for Artemis EVA operations. JETT3, the final JETT field test of FY22, occurred Oct 3-11, 2022 in the San Francisco Volcanic Field north of Flagstaff, AZ. The test focused on developing the Artemis concept of operations and systems, including integrating an Artemis-like Science Team into a NASA Flight Control Team (FCT) to plan and execute a series of simulated lunar EVAs in an environment analogous to Artemis 3.

T. E. Caswell↗
NASA NTRSDate not supplied

An Optimization Approach to Support Science Decision Making for Lunar Surface Exploration

Introduction: Scientific exploration is one of the three pillars of NASA’s Moon2Mars architecture, with crew surface extra vehicular activities (EVA) serving a critical enabling function. Development of surface EVA operational planning and execution, specifically integrating science and flight control teams (FCT), is currently being explored through analog scenarios. This integration, exercised, for example, through the Joint EVA and Hu-man Surface Mobility Test Team (JETT), allows for science input on EVA activities in near real-time through a Science Evaluation Room (SER), or Arte-mis science backroom, which integrates with the broader FCT through the Science Officer. The SER works within the FCT to support dynamic EVA planning in response to changes in operational constraints as well as science opportunities and re-prioritization, increasing the mission science return and accelerating the accomplishment of the Moon2Mars science objectives. The SER works within the FCT to provide recommendations to traverse execution in near real-time. One challenge is the requirement to deliver SER inputs to the FCT on operationally relevant timelines. Failure to do so may result in suboptimal execution of science exploration EVAs or even loss of key science objectives. To close this gap, we present a network optimization tool to allow the SER to provide rapid input to the FCT in response to changes in operational constraints or science opportunities. Inputs are predicated on approved science objectives, and clear rationale must be provided to the FCT for any requested change. Accordingly, this tool incorporates the Science Traceability Matrix (STM), SER prioritization scheme, and station characterization and action planning with operational constraints such as duration, traverse speed, and distance to maximize science objectives based on SER priorities, consistent with FCT operational requirements. Method: As a proof of concept, we used an existing linear programing software package used to simulate optimal routes through cellular metabolism. We built a Demonstrative Model with three STM objectives and four stations on a region of the Moon. The objectives were given an arbitrary prioritization and mapped to the stations through four possible crew actions. (Figs. 1 and 2). This station to STM mapping is consistent with the method used by the JETT5 Science Team to develop analog surface EVA science planning. We used a grid system with the landing site at the origin and the four stations placed across the positive x,y quadrant. Actions were assigned to each station and the accomplishment of those actions resulted in a numerical “reward” based on the ability of that action to achieve science objectives. The aggregate reward from each individual STM objective contributes to a global score (Science Yield), weighted by its priority. Operational constraints included a requirement to start and end at the landing site, 5 minutes each for initial station characterization and “clean up,” and variable total EVA time, traverse rate (fixed to 0.5 meters per second in our example), and time to perform each action (10, 5, 7, and 15 min for actions 1, 2, 3, and 4, respectively). Additional constraints and variables will be added in the future (e.g., sample mass, number of stations, traverse route constraints, illumination). Optimization. We converted the connections (arcs) between these stations (nodes) into a mixed integer linear programming optimization problem (arcs = constraints, nodes = variables) with the objective to maximize Science Yield. For any action, the Science Yield is equal to the relevance of that action to an STM objective [3, 2, and 1 point(s) for High, Med., and Low relevance, respectively], multiplied by the STM Objective Priority [3, 2, and 1 point(s) for High, Med., and Low priority, respectively]. This resulted in a model that computes the optimal station and action combination to maximize the Science Yield. These weightings can be adjusted by the SER as desired. Results: We explored three test cases for the Demonstrative Model. First, we set the maximum EVA duration to 120 minutes and computed the optimal route (Fig. 3A). The model suggested per-forming Actions 1 and 2 at Station P01, followed by Actions 1 and 2 at Station P02, and finally Actions 1 and 3 at Station P04 before returning to the Landing Site. Second, we adjusted the STM Objective Priori-ty order and computed the new optimal route (Fig. 3B). Under this situation, the model suggested per-forming all Actions at Station P02 followed by all Actions at Station P03. The previous test cases were relevant to SER planning activities. Next, we explored providing mid-EVA replanning input to the FCT. Scenario: While executing the Route in Fig. 3A the crew finishes at Station P01 and FCT decides that the EVA needs to finish in 45 minutes back at the Landing Site. FCT asks SER to recommend changes to the plan to accommodate this operation-al change. Using the model and incorporating these new constraints (start at Station P01, max. time of 45 min), the model suggested performing Actions 2 and 4 at Station P03 (Fig. 4), requiring 41 minutes to complete and return to the Landing Site. Interestingly, Station 3 was not part of the original route. Using the model, we determined the EVA would need 66 minutes, instead of 45, in order for the original Station P04 to yield a larger Science Yield than Station P03. The parametrization and simulation was per-formed in less than a minute, demonstrating the operational relevance of the approach. Future Efforts: The results from the Demonstrative Model suggest this tool can accelerate SER decision making on operationally relevant timelines. Use in analog activities, such as JETT5 or follow-ons, which have over a dozen stations for a crew to explore and over a dozen actions per station, will provide needed validation of the utility of this tool for planning EVAs, replanning mid-EVA, or planning follow-on EVAs based on previous results. Further integration with FCT execution monitoring tools may provide additional efficiency gains, al-lowing rapid and iterative exploration of operation-al and science decision space by the FCT and SER.

Science Operations↗
NASA NTRS1972

Remote control and navigation tests for application to long-range lunar surface exploration.

A vehicle system has been built and tested to investigate some of the unknown factors with regard to the operation of a remotely controlled vehicle on the surface of the moon. A general vehicle system description is presented, giving attention to the wheel drive subsystem, the steering subsystem, the power system, the control console, the command subsystem, the television subsystem, the telemetry subsystem, and the navigation subsystem. The remote control driving station consists of the antenna system, the video display system, the instrument and control panel, the data panel, and the steering and throttle controls. Initial tests with the vehicle and the remote driving station were conducted in March and April 1971.

Mastin, W. C.↗
NASA NTRS2016

Building on the Cornerstone: Destinations for Nearside Sample Return

Discoveries from LRO (Lunar Reconnaissance Orbiter) have transformed our knowledge of the Moon, but LRO's instruments were originally designed to collect the measurements required to enable future lunar surface exploration. Compelling science questions and critical resources make the Moon a key destination for future human and robotic exploration. Lunar surface exploration, including rovers and other landed missions, must be part of a balanced planetary science and exploration portfolio. Among the highest planetary exploration priorities is the collection of new samples and their return to Earth for more comprehensive analysis than can be done in-situ. The Moon is the closest and most accessible location to address key science questions through targeted sample return. The Moon is the only other planet from which we have contextualized samples, yet critical issues need to be addressed: we lack important details of the Moon's early and recent geologic history, the full compositional and age ranges of its crust, and its bulk composition.

Lawrence, S. J.↗
NASA NTRS1992

Planetary surface exploration MESUR/autonomous lunar rover

Planetary surface exploration micro-rovers for collecting data about the Moon and Mars have been designed by the Department of Mechanical Engineering at the University of Idaho. The goal of both projects was to design a rover concept that best satisfied the project objectives for NASA/Ames. A second goal was to facilitate student learning about the process of design. The first micro-rover is a deployment mechanism for the Mars Environmental Survey (MESUR) Alpha Particle/Proton/X-ray (APX) Instrument. The system is to be launched with the 16 MESUR landers around the turn of the century. A Tubular Deployment System and a spiked-legged walker have been developed to deploy the APX from the lander to the Martian Surface. While on Mars, the walker is designed to take the APX to rocks to obtain elemental composition data of the surface. The second micro-rover is an autonomous, roving vehicle to transport a sensor package over the surface of the moon. The vehicle must negotiate the lunar terrain for a minimum of one year by surviving impacts and withstanding the environmental extremes. The rover is a reliable track-driven unit that operates regardless of orientation that NASA can use for future lunar exploratory missions. This report includes a detailed description of the designs and the methods and procedures which the University of Idaho design teams followed to arrive at the final designs.

Stauffer, Larry↗