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Bradley C. Buckles

Publications and source records attributed to Bradley C. Buckles.

6 records
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

ICE-RASSOR: Intelligent Capabilities Enhanced Regolith Advanced Surface Systems Operations Robot

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 on-board sensory. Herein, we present preliminary findings from the Intelligent Capabilities Enhanced RASSOR project. We apply supervised learning using real data to estimate the soil mass collected without the need for mass flow rate monitors or other explicate sensing techniques. We also create a reduced-order simulation environment to develop autonomous trenching controllers via reinforcement learning and proto-type state estimation architectures. Our initial results suggest that excavated regolith mass can be inferred within 2.9% RMS error of full scale, and reinforcement learning for autonomous operations has learned viable trenching strategies and helped identify desirable sensing capabilities, arrangements, and considerations. Future work includes regolith mass estimation during dynamic operation, expanding our simulation to more complex environments, and transfer learning from simulation to hardware.

machine learning↗
NASA NTRSDate not supplied

Off Earth Landing and Launch Pad Construction – A Critical Technology for Establishing a Long-Term Presence on Extraterrestrial Surfaces

Space Policy Directive 1 has refocused the National Aeronautics and Space Administration (NASA) to lead the return of humans to the Moon for long-term exploration and utilization. A long-term presence on the moon will require numerous lunar landings and launches to build up surface assets, rotate crew, and deliver resources to/from the moon. Interactions between landing/launch plumes and the unprepared surface cause regolith to be eroded and ejected at high velocities from beneath the vehicle. The associated ejecta elevates mission risks by obscuring sensors and human vision during landing; cratering/modifying the surface that the vehicle will land upon; and subjecting the vehicle, surrounding assets, and potentially orbital assets to impacts from high velocity dust particles. A method to mitigate risks stemming from landing/launch plume ejecta is to construct reusable landing/launch pads. A landing/launch pad can provide a known landing/launch surface, minimize ejecta, protect assets from liberated particles, and provide protection in case of a landing/launch anomaly. This presentation will summarize previous work towards construction of off-Earth landing/launch pads and identify key technology gaps. Additionally, this presentation establishes metrics for comparison of pad construction technologies and trades current approaches. The overall objective of this paper is to baseline the state of the art of off-Earth landing/launch pad construction technologies and serve as the starting point for further technical development.

Landing & Launch Pad↗
NASA NTRSDate not supplied

Towards Autonomous Lunar Resource Excavation via Deep Reinforcement Learning

To support sustainable infrastructure on the Moon, NASA needs to leverage lunar resources for in-situ processing and construction. NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for these tasks. To reliably perform these operations on the lunar surface, RASSOR's sensors and control systems need to be robust and maximize information extracted from a reduced sensor payload. Herein, we present our findings from the Intelligent Capabilities Enhanced RASSOR project. We created reduced-order simulation environments in which we applied reinforcement learning algorithms to learn autonomous trenching controllers and produced state estimation architectures. We developed two simulations: a 2D excavation simulation used to facilitate parameter selection, and a 3D simulation developed using a game physics engine to simulate simplified soil interactions and incorporate robotic agents parameterized by dynamic models. Within these simulations, we learned autonomous excavation routines that exceed excavation efficiency measures as compared against RASSOR's existing control and teleoperation-based methods.

RASSOR↗
NASA NTRSDate not supplied

Towards Autonomous Lunar Resource Excavation via Deep Reinforcement Learning

To support sustainable infrastructure on the Moon, NASA needs to leverage lunar resources for in-situ processing and construction. NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for these tasks. To reliably perform these operations on the lunar surface, RASSOR's sensors and control systems need to be robust and maximize information extracted from a reduced sensor payload. Herein, we present our findings from the Intelligent Capabilities Enhanced RASSOR project. We created reduced-order simulation environments in which we applied reinforcement learning algorithms to learn autonomous trenching controllers and produced state estimation architectures. We developed two simulations: a 2D excavation simulation used to facilitate parameter selection, and a 3D simulation developed using a game physics engine to simulate simplified soil interactions and incorporate robotic agents parameterized by dynamic models. Within these simulations, we learned autonomous excavation routines that exceed excavation efficiency measures as compared against RASSOR's existing control and teleoperation-based methods.

RASSOR↗
NASA NTRSDate not supplied

ISRU Pilot Excavator (IPEx) Technology Readiness Level 5 Design Overview

This paper details the mechanical and mechatronic design of the Technology Readiness Level (TRL)-5 In-Situ Resource Utilization (ISRU) Pilot Excavator (IPEx). IPEx is a robotic excavator designed for a technology demonstration of regolith mining in the lunar south -pole region. The novel design uses pairs of counter-acting excavation tools called bucket drums, that dig at the same time in opposing directions to reduce the reaction force needed, thereby enabling mining with a small, low-mass, robotic system. IPEx builds on the prior work of the Regolith Advanced Surface Systems Operations Robot (RASSOR), which is the TRL-4 implementation of this concept. The TRL-5 IPEx subsystems that are discussed in this paper include: Regolith Delivery Subsystem (RDS), Mobility Subsystem (MS), Cameras and Dust Mitigation Subsystem (CDMS), and Thermal Control Subsystem (TCS). Each subsystem is described in detail with rationale for design selections. Dust tolerance is a key feature for IPEx and this paper details a thermal control system with an actuated radiator cover and phase change material as well as camera modules with removable electrodynamic dust shields (EDS). Additional components such as actuators, wheels, and bucket drums are discussed in detail. Due to their complexity, the avionics and software subsystems will be discussed in a separate publication.

RASSOR↗