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Planning Concept Meeting

Planning Concept Meeting. Agenda - morning. Presentations: conceptual level, implementation not discussed 9:30 Rosetta 10:00 BepiColombo 10:30 break 10:45 MIG: Generic planning concept + lessons learned from routine operations 11:15 GSP activity 11:30 TEC-SWM: Conceptual SGS proposal

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Planning Concept Meeting

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  1. Planning Concept Meeting

  2. Agenda - morning Presentations: conceptual level, implementation not discussed • 9:30 Rosetta • 10:00 BepiColombo 10:30 break • 10:45 MIG: Generic planning concept + lessons learned from routine operations • 11:15 GSP activity • 11:30 TEC-SWM: Conceptual SGS proposal 12:15 Lunch

  3. Agenda – early afternoon Discussion sessions • 13:00 'science driven' observation selection • assessing the science objectives - are they achievable – attach value? • capturing the planning information - what is needed to identify and prioritise observations? • defining use cases - appropriate levels of abstraction for effective planning • observation life cycle - mission analysis, opportunity analysis through to ingestion in the archive • 14:00 'knowledge driven' planning process • defining planning horizons based on planning information availability/predictability • preservation of planning information over planning boundaries

  4. Agenda – late afternoon • 15:00 'prediction limited' plan validation • plan validation reference • modelling/simulation needed for resource assessment (pointing, power, thermal, data) • SPICE for planning? • 16:00 Identifying requirements - first steps • prerequisites for ROS, BC based on mission characteristics • feasibility assessment (P v/d Plas) • consolidating requirements with other groups • 17:00 next steps • outline meeting plan • involvement of other missions/groups • prototyping and studies • responsibilities

  5. Generic Planning Concept

  6. Present Status • A science planning concept does not exist for the planetary missions in routine phase • Planning requirements are poorly documented. • All SGS for planetary missions in routine phase have tools for: • Simulation and request validation (MAPPS, EPS, MIRA) • Visualisation of underlying request files (MAPPS, MIRA) There are no science planning tools in use, done by the teams • PI teams prepare requests without access to all available planning information and/or planning tools. • Selections made without assessment of impact on other participants • Any choices made do not propagate to all parties • Result: conflicting requests, unnecessary iteration, sub-optimal planning • Planning process is not transparent • Information lost whenever requests cross an interface • The goals of the science planning are obscured • Prioritisation of observations extremely hard (impossible ?) to assess • No feedback from previous plans, no knowledge of future opportunities • Observations selection not driven by the LTP science goals

  7. Need for a Planning Concept All missions face the same science planning problem: How can the science return be optimised while remaining within the operational constraints of the mission? The planning process should be transparent and place all planning selections in the context of the entire mission (as a minimum). Generic Concept: • Formulate the science goals in operational terms • Identify the conditions to satisfy an experiment teams’ request • Find the best time in the mission to schedule the requests • Prioritise requests to optimise the science return of the mission • Ensure that the operational constraints are not violated Generic Concept: • Identify the data flow between all involved planning parties • Propagate planning choices • Track the progress of plans

  8. Goal of Science Planning • Science goals of ESA missions have remained top—level, vague, ambitions Maximise the science return without violating constraints or exceeding resources resources need to be known, calculated or predicted. well specified constraints permit a plan to be validated science return must be quantifiable Generic Concept: • Identify achievable science objectives for the nominal mission

  9. Science Driven Analysis Opportunity analysis: sufficient planning information being available to reliably identify when observations are possible • Planning information can be categorised as: • static – unchanging characteristics of a mission • predictable – information that can be modelled or simulated • unpredictable – uncertainty needs to be accommodated within the plan Generic Concept: • Only the SGS has access to the information needed to identify opportunities • To accomplish this it is necessary to know: • when a predetermined set of information is complete with acceptable accuracy • how to proceed to obtain the opportunities

  10. Static planning information The concept will need accommodate some parameters of the planning problem that have already been fixed: • platform has been designed • payloads have been selected • Able to define how experiments observe and what the target allows us to observe Generic Concept: Fixed aspects of the mission can be treated as planning information • Detector characteristics, sensitivity criteria • Platform limitations • The target object (known to varying degrees, from Smart-1 to Rosetta)

  11. Observation requirements Generic Concept: • the characteristics of a mission represent the known/static planning information • they can form the foundation of a valid science observation E.g. For a detector to perform an certain observation requires:

  12. Unknown planning information The orbit may be undefined it will be constrained by the experiment design: • maximum footprint velocity (minimum dwell time) Coverage requirements are well known: • Spatial/spectral/temporal coverage • Target environment The orbit is further constrained further by the spacecraft design: • spacecraft fuel budget • slew rates (AOCS) • spacecraft resources (data budget, power budget) Generic Concept: • SGS should have themeans to select a baseline orbit that will optimise the science return within the resources.

  13. Predictable Information • In terms of a planning concept it is only important to: • identify the missing information needed to validate a plan • demonstrate that it will be predictable and at what time (e.g. once a baseline orbit is delivered). • MEX approach to opportunity analysis enhances the information associated to a valid opportunity: • Primary geometric parameters used to identify a valid opportunity • contextual geometry used to provide a qualitative assessment Generic Concept: • Planning horizons are based on planning information availability/predictability

  14. Knowledge driven planning

  15. Operations Science Science Driven Planning revisited Top-level science goals Science Themes& Sub-Themes Trajectory Environment Operationalconstraints ScienceObjectives Quantifiable “Scenarios” Orbit types Measurements Link between operations and pointing lost Physical parameters Observations Iteration based on all available planning information Timeline Pointing Link to observation lost Sequences PTR blocks PTR Iteration on operational missions ITL/POR

  16. Planning iteration • For science driven planning the iteration must be on observation level • Avoid the loss of planning information • Permits higher level abstraction • Commanding requests can be generated Generic Concept: • Planning information should be preserved over planning horizons and interfaces. • Iterations should take place on object that pools all of the information related to an observation

  17. Resource Assessment For each of the resource types: • There will be a reference model • The nature of the mission will demand a level of accuracy for each resource type Generic Concept: • SGS validation must be synchronised with the reference model • Resource estimation will be used to assess validity of observation • Accuracy of prediction may impact the science return depending on the mission characteristics • Critical resources should be identified • Acceptable level of accuracy for each resource type

  18. Existing Focus of study: Information repository Reuse existing / prototype modules Information Repository Payload Definition Observation Request Science Objectives Constraints Priorities Feedback Execution Success Data Quality Prototype Missing Simulation Modules Environment Payload Science Opportunity Analyser Visualisation Module Spacecraft Thermal Requests Slew Module Geometry Planner & Scheduler Request Generation GSP Planning Information Repository

  19. Requirements

  20. Drivers

  21. Capabilities

  22. Repository Architecture

  23. Development Schedule • Prerequisite: • Generic planning concept • Reference SGS implementation

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