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Automated Allocation of ESA Ground Station Network Services

Automated Allocation of ESA Ground Station Network Services. S. Damiani , H. Dreihahn, Jörg Noll, Marc Niézette ( VEGA IT GmbH) G.P. Calzolari (ESA/ ESOC) 24 th of October 2006. 5 th International Workshop on Planning and Scheduling for Space. Overview.

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Automated Allocation of ESA Ground Station Network Services

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  1. Automated Allocation of ESA Ground Station Network Services S. Damiani, H. Dreihahn, Jörg Noll, Marc Niézette (VEGA IT GmbH) G.P. Calzolari (ESA/ESOC) 24th of October 2006 5th International Workshop on Planning and Scheduling for Space

  2. Overview • Context and System Overview: ESTRACK Planning System • Modeling • Planning problem generation • Planning problem solving • Preliminary results • Future work

  3. ESTRACK – ESA Tracking Network Overview Kiruna, Sweden Maspalomas, Canary Island, Spain Cebreros & Villafranca, Spain Redu, Belgium Kourou, French Guyana New Norcia & Perth, Australia Malindi, Kenya

  4. ESTRACK Evolution • Yesterday • Ground stations were almost exclusively dedicated to a given ESA mission • Ground stations were operated locally • Today • ESTRACK is constantly growing in size, capability and complexity • Ground stations are remotely operated on a routine basis • Ground stations are supporting multiple missions, within and outside ESA • Users requests are evolving from direct request of specific facilities to more generic tracking service requirements • Tomorrow • The level of cross-support will increase • The load of the network will continue to grow • Need for centralised automated allocation

  5. Planning Process Online ESTRACK Mgnt Managed Facilities Ground Stations NMS ESTRACK Management & Scheduling System EMS Non ESA Agency EMS Users Interaction with Users Interaction with External Providers

  6. Planner MMI Online MMI SMF Mission Operation MAS MCS NIS SICF ESTRACK Coordination System M&C ESTRACK Ground Station ESTRACK Scheduling System Schedules Communications Network Management SICF EMS in more details Flight Dynamics EMS User Mission Planning EMS ESTRACK Management Plan Format Converter ESTRACK Planning System File Server Proxy External Provider (scheduling office)

  7. Dynamic use of the system • Three levels • EMS system • Planning session • Planning run • Plans are frozen one week before execution

  8. ESTRACK Service Allocation Requirements • One pass from AOS to LOS of a minimum duration per orbit (e.g. ENVISAT) • Maximum continuous contact per orbit, with mandatory contact in a time period between two events specified for each orbit, and minimum hand-over duration (e.g. XMM, INTEGRAL) • Maximum/Minimum total contact duration and number of passes within a period, with minimum pass duration (e.g. SMART-1) • Maximum coverage for a constellation; in case of conflict between several S/C for the visibility of the same GS, the duration of the contact with the first S/C visible from the GS is maximized (e.g. CLUSTER)

  9. Modeling requirements Mission Agreement Mission: ENVISAT User Service Selector: every second orbit Standing OrderOne orbit Constraint Not less than 10 minutes Service Telemetry Constraint Station Visible Service Telecommand

  10. Modeling resources • Services available during visibility windows Ground Station Name: Kourou Ground Station Name: KIRUNA Service Telemetry Service Telemetry Service Ranging Service Telecommand Service Ranging

  11. Modeling preferences • ESTRACK allocates priorities to the Mission Agreements for using Ground Stations • Mission Agreements have internal preferences for the choice of the Ground Stations

  12. Planning Objectives • Every User Service must be completely implemented • No global optimisation is required • No alternative solutions are proposed to the user • If no solution can be found the operator can enable degraded User Services

  13. Planning problem generationResources • Ground Station: exclusive single capacity reusable resource • Rules to create all opportunities for all User Services taking into account all Ground Stations • Other constraints can be specified (temporal filtering) • Language for Mission Planning • Service Opportunity Windows (SOWs) ( fact(spacecraft_visibility, ?tvS, ?tvE, ?station, SPACECRAFT X) ^ fact(operator_shift, ?toS, ?toE, SPACECRAFT X) ^ ?toS < ?tvS – 10 ^ ?t0E > ?tvE + 10 -> sow(?station, ,operational_service_group_name, ?tvS, ?tvE))

  14. Impl Impl Impl User Service x t Start of orbit events Planning problem generationGoals • Extending goals: • For each Mission Agreement • For each User Service • Generate all periods during which the User Service shall be implemented • BSOP (Basic Standing Order Period) • E.g. every second orbit • BSOP length: one orbit • BSOP Selector: 2

  15. Impl BSOP t6 t5 COSS_1 COSS_3 Ground Station A t1 t2 handover COSS_2 Ground Station B t4 t3 Planning problem generationActivities • Inside each BSOP to be planned, a pattern of activities must be implemented • Each activity represents the use one of one Ground Station by one Spacecraft during one time interval • Start and end times • are variables during the planning run • are fixed in the ESTRACK Management Plan • Candidate Operational Service Sessions (COSSes)

  16. Planning problem generationDomain • Constraints attached to User Services: Rules defining the characteristics of the planning problem • E.g. minimum and maximum contact distance minimum and maximum contact duration • Implicit constraints of the domain • Availability constraints (each COSS inside one unique SOW) • Resource constraints (each GS used by one SC at a time) • Priorities (4) COSS_4 t6 t5 (4) t8 t7 COSS_3 Ground Station A (2) t1 t2 COSS_1 Astart SOW A Aend COSS_2 Ground Station B (3) t4 t3

  17. Planning AlgorithmsOverview • Parameters of a planning run • Set of Mission Agreements • Planning horizon • Principle: incrementally fill a valid plan with activities until all extended goals are satisfied

  18. Impl Impl Impl Impl Impl Impl Impl Impl Impl Impl Planning AlgorithmsBSOP selection • Try to minimise repairs • Earliest deadline first ordering • Plan is incrementally filled from the start to the end of the planning session Planning horizon t User Service 1 User Service 2

  19. Planning AlgorithmsCOSSes generation Impl BSOP • Given one BSOP to implement and the set of available SOWs • Select the SOWs to useand determine the handovers • Optimisation algorithm: maximise the overall contact time weighed by the preferences of the GS • Parameterised to obtain desired patterns • Generate the COSSesand the temporal constraints available SOWs C(5) GS1 D(3) A(3) GS2 E(2) GS3 B(6) GS4 D(3) A(3) GS2 handover handover B(6) GS4 COSSes C1 C3 D A GS2 B GS4 C2

  20. Planning AlgorithmsConsistency check • Check the consistency of the temporal constraint network • Constraints • Simple binary constraints • Linear constraints • Disjunctions of binary constraints (partial orderings to avoid unary resource contentions) • Generally speaking: Disjunctive Linear Problem [Li and Williams 2005] Mixed Integer Programming • But relatively few linear constraints, not in disjunctions • Solve of a Disjunctive Temporal Problem (DTP) [Stergiou and Boubarakis 1998] • Check the linear constraints at each solution of the DTP

  21. Planning AlgorithmsDTP solving • Based on Epilitis [Tsamardinos and Pollack 2003] • Meta Variable: disjunction of binary constraints • Meta Domain: set of disjuncts • Meta Constraint: implicit (partial assignment valid iff underlying STP is consistent) • Conflict directed backtracking tree search with forward checking • Recursive algorithm which records conflict information (no-good: pair invalid partial assignment / involved variables) • Dynamic meta-CSP • Adding activities: tighteningRemoving activities: relaxation • No-goods reuse • Oracles

  22. Planning AlgorithmsRepair • Conflict detection and selection: use the no-goods returned by Epilitis (associated to the empty assignment of the DTP) • Activity selection: heuristics, e.g. delete the one with the lowest priority • Modification of the associated BSOP (generate new pattern of activities) • New consistency check • If no solution found after a given number of repairs, report to the operator with incriminated BSOPs

  23. Implementation resultsComputation time Total duration94 seconds 219 activities 1533 binary constraints 25 disjunctive constraints 2 repairs Input 5 satellites => 5 User Services BSOP to implement: each orbit (two days) Planning horizon: 5 days Computation time (seconds) 5.4 6.4 (d) (b) (c) (a) (a) (a) Rank of the consistency check

  24. Implementation resultsVisualisation • Input • 7 satellites (2 LEOs / 5 HEOs) => 7 User Services • BSOP to implement: each orbit (100 minutes / 2 days) • Planning horizon: 10 days • Complexity • 325 activities • 18 disjunctive constraints • 2830 Simple Temporal Constraints • Results • Planning duration: 804 seconds • Number of repairs: 23

  25. Future work • Integration and test • Linear programming • Conflict information returned to the operator • Technical improvements • Guide the generation of the patterns of activities for one BSOP • Heuristics for conflict detection and selection • Repair algorithm • Dynamic meta-CSP resolution techniques (relaxation) • Adaptation of the plan following changes in events (flexibility) • Implement further requirements • Granularity of the resources: from the ground stations to the services => discrete resources • Improve interaction of the operator with the plan view

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