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Automation of Ground Station Scheduling for the Cluster Mission using an Artificial Intelligence Framework
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In the course of simplifying and automating mission operation for the Cluster-II satellites of the European Space Agency (ESA), a tool for automated ground station pass scheduling has been implemented. The large number of constraints involved in pass scheduling requires a member of the Cluster-II flight control team to dedicate about 1.5 working days to planning and scheduling activities during one week. Due to the complexity of the problem, the resulting plans are often not optimal which results in higher costs for tracking hours than necessary. ESA's Advanced Planning and Scheduling Initiative (APSI), developed at the European Space Operations Centre (ESOC), is an approach to introduce automation and advanced technologies to tackle such scheduling and planning problems. The software framework is capable of solving and optimising scheduling problems by utilising artificial intelligence for resource driven planning. The Cluster-II scheduling constraints were analysed and modelled in accordance with the domain and problem languages provided by APSI. A graphical user interface allows the operator to compare the produced solutions in terms of predefined criteria. Additionally, to integrate the tool flawless in the daily mission operation and its internal mission platform ClusterWeb, interfaces pre- and post-processing the information for the scheduling tool have been developed and integrated to ClusterWeb.Preliminary results of automatically generated plans provide an insight in the current development process of the framework and point out its behaviour. Based on these results and the experiences made so far, it can be expected that the tool will have a big impact on mission operation for Cluster-II once the APSI framework is fully optimised and the scheduling tool can be deployed in routine operations.

Place, publisher, year, edition, pages
2016. , 86 p.
Keyword [en]
Technology, Planning, Scheduling, Cluster-II, Spacecraft Operations, APSI, Automation, Artificial Intelligence
Keyword [sv]
URN: urn:nbn:se:ltu:diva-46695Local ID: 44ff23b5-737d-4cd6-aa72-f11bb1cd1c66OAI: diva2:1020010
External cooperation
Subject / course
Student thesis, at least 30 credits
Educational program
Space Engineering, master's level
Validerat; 20160119 (global_studentproject_submitter)Available from: 2016-10-04 Created: 2016-10-04Bibliographically approved

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