Search NASA⌕ Search

NASA NTRS · 19920018520

Automation and Robotics for Space-Based Systems, 1991

Abstract

A NASA Langley workshop on Automation and Robotics for Space-Based Systems was held on December 10, 1991. This conference proceedings document presents the overhead slides from each speaker at this event. The purpose of this in-house workshop was to assess the state-of-the-art of automation and robotics for space operations from a LaRC perspective and to identify areas of opportunity for future research. The workshop was sponsored by the Guidance, Navigation, and Control Technical Committee, chaired by Mr. Raymond C. Montgomery. Nineteen talks were given, reflecting a high level of interest in the field of automation at NASA Langley. Over half of the presentations came from the Automation Technology Branch, covering telerobotic control, EVA and IVA robotics, hand controllers for teleoperation, sensors, neural networks, and automated structural assembly, all applied to space missions. Other talks covered RMS active damping augmentation, space crane work, modeling, simulation, and control of large, flexible space manipulators, and virtual passive controller designs for space robots.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Robert L Williams, II. 1992-03-01. Automation and Robotics for Space-Based Systems, 1991. https://ntrs.nasa.gov/citations/19920018520

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Graph theory inspired anomaly detection at the LHC

Designing model-independent anomaly detection algorithms for analyzing LHC data remains a central challenge in the search for new physics, due to the high dimensionality of collider events. In this work, we develop a graph autoencoder as an unsupervised, model-agnostic tool for anomaly detection, using the LHC Olympics dataset as a benchmark. By representing jet constituents as a graph, we introduce a method to systematically control the information available to the model through sparse graph constructions that serve as physically motivated inductive biases. Specifically, (1) we construct graph autoencoders based on locally rigid Laman graphs and globally rigid unique graphs, and (2) we explore the clustering of jet constituents into subjets to interpolate between high- and low-level input representations. We obtain the best performance, measured in terms of the Significance Improvement Characteristic curve for an intermediate level of subjet clustering and certain sparse unique graph constructions. We further investigate the role of graph connectivity in jet classification tasks. Our results demonstrate the potential of leveraging graph-theoretic insights to refine and increase the interpretability of machine learning tools for collider experiments.

Automation↗

Accelerating template generation in resonant anomaly detection searches with optimal transport

We introduce Resonant Anomaly Detection with Optimal Transport (RAD-OT), a method for generating signal templates in resonant anomaly detection searches. RAD-OT leverages the fact that the samples from the conditional probability density of the target features vary approximately linearly along the optimal transport path connecting the resonant feature. This does not assume that the conditional density itself is linear with the resonant feature, allowing RAD-OT to efficiently capture multimodal relationships, changes in resolution, etc. By solving the optimal transport problem, RAD-OT can quickly build a template by interpolating between the background distributions in two sideband regions. We demonstrate the performance of RAD-OT using the LHC Olympics R&D dataset, where we find comparable sensitivity and improved stability with respect to deep learning-based approaches.

Automation↗

Automated Collision Avoidance (ACA) and Automated Return to Course (ARTC) Requirement and Guidance Review: Final

This report's objectives were to review regulatory and guidance documents to identify requirements and considerations for the design and operation of automated systems that perform the functions of automated collision avoidance (ACA) and/or automated return to course (ARTC). The importance of this work is twofold: 1) to help focus efforts addressing new automated collision avoidance and return to course systems requirements and considerations, and 2) in pulling together available requirements from multiple sources, generate a master resource for these automated system requirements and considerations. Reviewed documents include regulations, industry standards and research papers that specifically address ACA and ARTC systems, other automatedl control and guidance systems, ICAO (International Civil Aviation Organization) and Eurocontrol documents.

Automation↗