Using Aerodynamic Torque to Desaturate CubeSat Reaction Wheels Step-B

Metadata Updated: May 2, 2019

Motion planning entails autonomously planning and executing trajectories in dynamic and cluttered environments while obeying differential and other constraints such collision avoidance. Implementing motion planning algorithms to the realm of spacecraft guidance and control includes additional challenges such as operating in uncertain environments and necessitating fault-tolerant operation without human intervention. As such, fast re-planning and anytime computation poses its own set of challenges before accounting for the need to implement such algorithms on spacecraft embedded systems. This project will focus on the development of real-time, efficient, and dependable algorithms for autonomous maneuvering, with a focus on dynamic and cluttered environments. Leveraging advances from the fields of robotic motion planning and control, this work seeks to devise a technology for real-time, safe planning of trajectories in a range of missions such as proximity operations, attitude motion planning under complex constraints, and satellite reservicing missions. The foundation of this work will be steeped in sampling-based motion planning, an approach that scales well to high-dimensional systems and has a rich history of work at the Autonomous Systems Lab (ASL). The open research avenues on this topic include: - Leveraging embedded graphics processing units (GPUs) and embarrassingly parallel algorithms for GPUs to enable new modes of real-time planning for spacecraft systems. - Robust control of high-dimensional systems (i.e. spacecraft equipped with a robotic arm) in order to guarantee performance and provide safety certificate in the presence of uncertainty. - Theoretical analysis of bottlenecks in the planning process i.e. the calculation of nearest-neighbors for sampling-based planners. - Incorporating work from the field of machine learning and AI to increase autonomous capabilities of spacecraft while guaranteeing safe operation in new and unforeseen environments. Although these listed topics cover a broad swath of work, they will be developed with a specific eye on the aforementioned mission-enabling spacecraft applications.

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Public: This dataset is intended for public access and use. License: U.S. Government Work

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Metadata Created Date August 1, 2018
Metadata Updated Date May 2, 2019

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date August 1, 2018
Metadata Updated Date May 2, 2019
Publisher Space Technology Mission Directorate
Unique Identifier TECHPORT_93973
Maintainer
TECHPORT SUPPORT
Maintainer Email
Public Access Level public
Bureau Code 026:00
Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
Metadata Catalog ID https://data.nasa.gov/data.json
Schema Version https://project-open-data.cio.gov/v1.1/schema
Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
Datagov Dedupe Retained 20190501230127
Harvest Object Id 811fd1b2-a8a3-4971-8935-d31672cee20b
Harvest Source Id 39e4ad2a-47ca-4507-8258-852babd0fd99
Harvest Source Title NASA Data.json
Data First Published 2020-09-01
Homepage URL https://techport.nasa.gov/view/93973
License http://www.usa.gov/publicdomain/label/1.0/
Data Last Modified 2018-07-19
Program Code 026:027
Source Datajson Identifier True
Source Hash cde1097d2d867171a2acf9b3592ccbe5880d1ee4
Source Schema Version 1.1

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