東華大學圖書館 |

Task Planning for Earth Observing Satellite Systems.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Task Planning for Earth Observing Satellite Systems./
作者:	Eddy, Duncan.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	101 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
Contained By:	Dissertations Abstracts International83-02B.
標題:	Planning. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28483279
ISBN:	9798505571606

Task Planning for Earth Observing Satellite Systems.
Eddy, Duncan.

Task Planning for Earth Observing Satellite Systems. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 101 p.

Source: Dissertations Abstracts International, Volume: 83-02, Section: B.

Thesis (Ph.D.)--Stanford University, 2021.

This item must not be sold to any third party vendors.

Earth observing spacecraft provide unique data collection capabilities in terms of access, coverage, and persistence not achievable with air or land-based sensor platforms. The information remote sensing constellations provide is relied upon across defense, intelligence, financial, humanitarian, agricultural, and scientific domains. Increasing access to space in the form of low-cost commercial rocket launches, combined with commercial-off-the-shelf electronics, has led to the development of large constellations of small satellites with the aim of increasing data collection throughput. However, current and planned constellation sizes are beyond what can be practically managed using human operators alone. Automated planning of on-orbit operations tasks, in particular imaging activities, is needed to effectively manage Earth observing satellite systems. This thesis provides contributions to the modeling, analysis, and solution of the task planning problem for Earth observing satellite systems.Satellite task planning entails selecting actions that best satisfy mission objectives. This planning has historically been performed by human operators using heuristic or rule-based methods. Human-in-the-loop operations does not efficiently scale with the number of assets, as heuristics frequently fail to properly coordinate actions of multiple vehicles over long horizons. Additionally, the problem becomes more difficult to solve for large constellations as the complexity of the problem scales exponentially with the number of requested observations and linearly with the number of spacecraft.Time-ordered task scheduling is an NP-complete problem. Therefore application-specific approaches are required to solve it. This thesis presents techniques to schedule tasks for both single spacecraft, as well as multi-satellite constellations of Earth-observing spacecraft. The proposed techniques are evaluated on their ability to maximize the number of collected images over a fixed planning horizon as well as the time required to arrive at a solution.First, this thesis introduces a highly parallelizable framework for modeling the satellite task planning problem. The action space is modeled as a set of discrete opportunities that the planning agent can choose to select or ignore. Desired imaging locations can be points or areas, from which the action space computation algorithm finds the set of all possible collection opportunities. Similarly, the periods for communicating with ground stations are modeled as contact opportunities. The algorithm for computing collect and contact opportunities scales linearly in the number of requests and spacecraft, allowing for efficient action space computation for large planning problems. This thesis shows how other scheduling approaches, in particular heuristic, graph traversal, and mixed-integer linear programming (MILP) algorithms can be formulated in this opportunity-based modeling framework to take advantage of the efficient action space computation.By leveraging the discrete action-space modeling framework and applying spacecraft scheduling constraints, the underlying graph structure of the satellite task planning problem can be analyzed. The task planning problem can be viewed from a feasibility perspective as a directed acyclic graph (DAG), or viewed from an infeasibility perspective as an undirected graph. Analysis reveals that the infeasibility perspective is the more efficient representation of the problem for both single satellite and multi-satellite problems.This thesis introduces the equivalence of satellite task planning with the problem of finding a maximal independent set of vertices on an undirected graph.

ISBN: 9798505571606Subjects--Topical Terms:

552734
Planning.

Task Planning for Earth Observing Satellite Systems.
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520 $a Earth observing spacecraft provide unique data collection capabilities in terms of access, coverage, and persistence not achievable with air or land-based sensor platforms. The information remote sensing constellations provide is relied upon across defense, intelligence, financial, humanitarian, agricultural, and scientific domains. Increasing access to space in the form of low-cost commercial rocket launches, combined with commercial-off-the-shelf electronics, has led to the development of large constellations of small satellites with the aim of increasing data collection throughput. However, current and planned constellation sizes are beyond what can be practically managed using human operators alone. Automated planning of on-orbit operations tasks, in particular imaging activities, is needed to effectively manage Earth observing satellite systems. This thesis provides contributions to the modeling, analysis, and solution of the task planning problem for Earth observing satellite systems.Satellite task planning entails selecting actions that best satisfy mission objectives. This planning has historically been performed by human operators using heuristic or rule-based methods. Human-in-the-loop operations does not efficiently scale with the number of assets, as heuristics frequently fail to properly coordinate actions of multiple vehicles over long horizons. Additionally, the problem becomes more difficult to solve for large constellations as the complexity of the problem scales exponentially with the number of requested observations and linearly with the number of spacecraft.Time-ordered task scheduling is an NP-complete problem. Therefore application-specific approaches are required to solve it. This thesis presents techniques to schedule tasks for both single spacecraft, as well as multi-satellite constellations of Earth-observing spacecraft. The proposed techniques are evaluated on their ability to maximize the number of collected images over a fixed planning horizon as well as the time required to arrive at a solution.First, this thesis introduces a highly parallelizable framework for modeling the satellite task planning problem. The action space is modeled as a set of discrete opportunities that the planning agent can choose to select or ignore. Desired imaging locations can be points or areas, from which the action space computation algorithm finds the set of all possible collection opportunities. Similarly, the periods for communicating with ground stations are modeled as contact opportunities. The algorithm for computing collect and contact opportunities scales linearly in the number of requests and spacecraft, allowing for efficient action space computation for large planning problems. This thesis shows how other scheduling approaches, in particular heuristic, graph traversal, and mixed-integer linear programming (MILP) algorithms can be formulated in this opportunity-based modeling framework to take advantage of the efficient action space computation.By leveraging the discrete action-space modeling framework and applying spacecraft scheduling constraints, the underlying graph structure of the satellite task planning problem can be analyzed. The task planning problem can be viewed from a feasibility perspective as a directed acyclic graph (DAG), or viewed from an infeasibility perspective as an undirected graph. Analysis reveals that the infeasibility perspective is the more efficient representation of the problem for both single satellite and multi-satellite problems.This thesis introduces the equivalence of satellite task planning with the problem of finding a maximal independent set of vertices on an undirected graph.
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