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Goel, Ashish

Publications and source records attributed to Goel, Ashish.

11 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Geophysical Observations of the 2023 September 24 OSIRIS-REx Sample Return Capsule Reentry

Sample return capsules (SRCs) entering Earth's atmosphere at hypervelocity from interplanetary space are a valuable resource for studying meteor phenomena. The 2023 September 24 arrival of the Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer SRC provided an unprecedented chance for geophysical observations of a well-characterized source with known parameters, including timing and trajectory. A collaborative effort involving researchers from 16 institutions executed a carefully planned geophysical observational campaign at strategically chosen locations, deploying over 400 ground-based sensors encompassing infrasound, seismic, distributed acoustic sensing, and Global Positioning System technologies. Additionally, balloons equipped with infrasound sensors were launched to capture signals at higher altitudes. This campaign (the largest of its kind so far) yielded a wealth of invaluable data anticipated to fuel scientific inquiry for years to come. The success of the observational campaign is evidenced by the near-universal detection of signals across instruments, both proximal and distal. This paper presents a comprehensive overview of the collective scientific effort, field deployment, and preliminary findings. The early findings have the potential to inform future space missions and terrestrial campaigns, contributing to our understanding of meteoroid interactions with planetary atmospheres. Furthermore, the data set collected during this campaign will improve entry and propagation models and augment the study of atmospheric dynamics and shock phenomena generated by meteoroids and similar sources.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2022

Multi-Robot Assembly Scheduling for the Lunar Crater Radio Telescope on the Far-Side of the Moon

The Lunar Crater Radio Telescope (LCRT) is a pro- posed ultra-long-wavelength radio telescope to be constructed on the far side of the moon. The proposed telescope will be constructed by deploying a 1km wire mesh in a 3-5km crater using a team of wall-climbing DuAxel robots. In this work, we consider the problem of generating minimum-time assembly sequences for LCRT, using realistic models of travel speed and lighting. Specifically, we pose the assembly sequencing problem as a mixed-integer linear program (MILP), which we solve to global optimality using commercial solvers. We present methods for modeling time-varying travel and assembly times, based on variable lighting conditions (including crater shadowing), and show how such time-varying parameters can be incorporated into the MILP. Finally, we present numerical studies of our method, showing how makespan varies with the number of assembly robots.

Schwager, Mac↗
NASA NTRS2022

Space Applications of a Trusted AI Framework: Experiences and Lessons Learned

Artificial intelligence (AI), which encompasses machine learning (ML), has become a critical technology due to its well-established success in a wide array of applications. However, the proper application of AI remains a central topic of discussion in many safety-critical fields. This has limited its success in autonomous systems due to the difficulty of ensuring AI algorithms will perform as desired and that users will understand and trust how they operate. In response, there is growing demand for trustability in AI to address both the expectations and concerns regarding its use. The Aerospace Corporation (Aerospace) developed a Framework for Trusted AI (henceforth referred to as the framework) to encourage best practices for the implementation, assessment, and control of AI-based applications. It is generally applicable, being based on terms and definitions that cut across AI domains, and thus is a starting point for practitioners to tailor to their particular application. To help demonstrate how the framework can be tailored into mission assurance guidance for the space domain, Aerospace sought the involvement of the Jet Propulsion Laboratory (JPL) to engage with actual examples of AI-based space autonomy.

Kaufman, James↗
NASA NTRS2022

Adapting a Trusted AI Framework to Space Mission Autonomy

As artificial intelligence (AI) is increasingly pro- posed for new and future capabilities in space missions, the question of how to trust AI-enabled space autonomy has been explored. Recently, a collaboration between The Aerospace Corporation (Aerospace) and NASA’s Jet Propulsion Labora- tory (JPL) investigated how Aerospace’s Trusted AI Frame- work could be applied to two JPL projects that planned on lev- eraging AI for critical autonomous tasks. This combined effort led to many insights in the practical implementation of trusted AI along with considerable updates to the Trusted AI Frame- work that tailored its topic threads to space exploration. This document cohesively summarizes the enhanced framework as tailored to space missions as well as estimation of the level of trust required as a function of mission criticality and key stakeholders. The goal of this work is to provide a set of best practices to inform autonomy researchers, flight engineers, mission and proposal reviewers, and instrument and mission principal investigators (PI’s) to drive AI-based autonomy that maximizes trust and lowers the barriers to mission adoption for both science and engineering applications.

Amini, Rashied↗
NASA NTRS2020

A Concept for the Deployment of a Large Lunar Crater Radio Telescope Using Teams of Tethered Robots

Kilometer-scale craters on the far side of the Moon have unique potential as future locations for large radio telescopes, which can observe the universe at wavelengths and frequencies (> 10 m, < 30 MHz) not possible with conventional Earth or orbital-based approaches. Distinct advantages of building a Lunar Crater Radio Telescope (LCRT) on the far side include i) isolation from radio noise due to the Earth’s ionosphere, orbiting satellites, and the Sun, ii) days of uninterrupted dark/cold sky viewing during lunar night, and iii) terrain geometry naturally suited for constructing the largest mesh antenna structure in the Solar System. A key challenge to constructing LCRT on the Moon is related to the complexity of deploying a 1-km diameter antenna and hanging receiver within a lunar crater whose diameter, depth, and slope are 3-5 km, 1 km, and ~30 degrees respectively. In this paper, we first evaluate the trade space for deploying a large, complex structure within a crater, and then provide a more detailed concept evaluation of our favored approach, which employs coordinated teams of tethered rovers to extract and suspend a folded antenna from a lander at the base of a crater. NASA’s Jet Propulsion Laboratory in collaboration with California Institute of Technology (Caltech) have developed a novel robotic system for accessing extremely steep terrains; the Axel rover is a two-wheeled rugged terrain vehicle that is supported by an electro-mechanical tether that provides power, data, and tensile support from a top-side anchor location. Recently, a pair of Axel robots have been used in a DuAxel configuration that allows for four-wheel driving and repeated passive anchoring at different locations. The DuAxel system has unique advantages for deploying an LCRT antenna, including the ability to deploy from a lander near a crater, drive a distance to the crater rim to deploy an Axel, and later, retract the deployed Axel in order to sequentially lift up sections of the antenna. Our proposed concept involves delivering a packaged antenna and receiver to the bottom-center of a crater floor on a lander, then later sending a team of multiple DuAxel rovers to retrieve guide wires from the lander, which are pulled to the top of the crater. We explore this concept in detail and provide some initial quantitative analysis to demonstrate the feasibility of our system with respect to the spatial and mass properties of the antenna as juxtaposed to DuAxel capabilities. Finally, we outline next steps towards validating our concept on the way to a future lunar deployment opportunity.

Hallinan, Greg↗