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Thomas Muller

Publications and source records attributed to Thomas Muller.

4 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

Moon Tycoon Version 2: A 3D Lunar Surface Emulator for Desktops and Virtual Reality

Moon Tycoon Version 2 (V2) enables and encourages in-situ resource utilization (ISRU) technology developers, principal investigators (PI), and managers across the agency to design and visualize various Lunar operations and missions with a high degree of photorealism. Users are completely immersed in the emulated Moon environment, enabling them to gain new knowledge as they explore the Lunar surface. Moon Tycoon V2 will soon be released to the NASA Software Catalog for public distribution, which will allow external partners, academia, and other interested parties from the public domain to take part.

Kurt Leucht↗
NASA NTRSDate not supplied

Towards Autonomous Lunar Resource Excavation via Reinforcement Learning

To continue on a sustainable and flexible path, NASA needs to address the challenge of collecting and moving large amounts of regolith at the destination. NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for In-Situ Resource Utilization (ISRU) processing. RASSOR’s design enables it to efficiently collect and deposit regolith, return collected material for processing, and myriad related ISRU activities. To reliably perform these operations on the lunar surface, RASSOR software and sensory systems need to be robust and maximize the information extracted from a reduced sensor payload. Herein, we present preliminary findings from the Intelligent Capabilities Enhanced RASSOR project. We created reduced-order simulation environments to develop autonomous trenching controllers via reinforcement learning and prototype state estimation architectures. The goal of reinforcement learning is for an agent to learn a policy (task strategy) through interactions with an environment. When the agent performs an action, a change occurs in environment state and a numerical reward is received which informs the agent whether the action performed was good or not. Since reinforcement learning algorithms learn through trial-and-error, a simulation is a desirable first environment for development and learning. We developed two simulations, the first is a 2D excavation simulation developed to facilitate parameter selection, and a 3D simulation developed using a game physics engine, to simulate simplified soil interactions and increase the fidelity of the dynamic models of the robotic agents. The development of this 3D simulation has enabled the training of additional sensing capabilities and research both at the granular mechanics and operations levels. We experimented with various virtual sensor payloads to identify a combination that enabled efficient excavation operation and learning. Our reward function is based on how much material is excavated per step. A penalty is also received for leaving the dig site and to smooth the acceleration of the drum arms. We implemented pseudo time-of-flight sensors to report distance from each drum to ground and the height above ground which was found to be more efficient than existing solutions. Our findings suggest that reinforcement learning for autonomous operations has learned viable trenching strategies within 3000 training episodes in our simplified 2D environment and helped identify desirable sensing capabilities, arrangements, and considerations such as the positioning of time-of-flight sensors. Future work includes expanding our simulation to more complex environments and scenarios, and transfer learning from simulation to RASSOR 2.0 hardware for deployment in the Regolith Test Bin at NASA's Kennedy Space Center.

rassor↗
NASA NTRSDate not supplied

ISRU Pilot Excavator Wheel Testing in Lunar Regolith Simulant

The ISRU Pilot Excavator, or IPEx, is a robotic excavator funded by NASA’s Space Technology Mission Directorate (STMD). The Concept of Operations for IPEx involves the robot driving on the lunar surface up to 70 km at a speed of up to 30 cm/s. As such, it is critical to the mission’s success to optimize the design of the wheels for performance in lunar conditions, specifically in lunar regolith. To achieve this, an array of tests was completed to observe the effects of various wheel design choices on the driving performance of the wheels in lunar regolith simulant. In order to facilitate testing, we designed a 12” dia. configurable wheel to allow for interchangeability between various wheel formations. Two types of wheel parts were designed to be swapped: cleats, which form the tread of the wheel; and grousers, which protrude from the treads. The test variables that we considered were as follows: square vs. round wheel shape, solid vs. perforated cleats, cleat spacing, grouser height, and grouser spacing. By combining different settings of each of these test variables, ten discrete wheel designs were created and tested. The configurable test wheels were mounted on the Regolith Advanced Surface Systems Operations Robot (RASSOR) developed at NASA’s Kennedy Space Center. In our experiments, the robot was driven at a controlled speed across a prepared surface of BP-1 lunar regolith simulant. Four types of tests were conducted: circle driving, straight driving, slope driving, and drawbar pull. The driving tests were chosen to mimic a variety of conditions in which IPEx may be expected to operate, and the drawbar pull test was chosen to provide a standard of comparison with existing wheel design literature. The circle and straight driving tests were each performed at different levels: for the circle driving test, the robot was driven at a constant linear speed and three different angular speeds, while for the straight driving test, the robot was driven at three different linear speeds. The data collected from these tests included the power usage from each of the wheels, measurements of the tread patterns left in the regolith surface, and the amount of slip the wheels experienced, which was calculated using data from an OptiTrack motion capture system. From the results of these experiments, we found that certain test variables were more significant than others in determining performance for each type of test, and no single wheel design clearly outperformed the others in all areas. The details of our findings will be discussed further in this paper. This data will be utilized to inform the design of the wheels for IPEx and can provide a basis for the design of wheels for future lunar terrain vehicles.

RASSOR↗