Opsforum advanced planning_19032010

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Opsforum advanced planning_19032010

  1. 1. Infusion of Advanced Planning and Scheduling Technology in Space ESA Achievements and Perspectives Alessandro Donati, Nicola Policella (OPS-HSC) Colin Haddow (OPS-GI) Erhard Rabenau (OPS-OPM) OPS-Forum, ESA/ESOC 19/03/2010
  2. 2. OUTLINE – Introduction (A. Donati) – Motivation – A.I. Planning and Scheduling (N. Policella) – Technology – Experience & Evaluation – Transfer to Infrastructure (C. Haddow) – MPS Framework – Conclusions (A. Donati) – On-going project – Future work and Lessons Learnt
  3. 3. Introduction Dwight D. Eisenhower Plans are nothing; planning is everything. Observe always that everything is the result of change, and get used to thinking that there is nothing Nature loves so well as to change existing forms and make new ones of them. - Marcus Aurelius, emperor of Rome (121-180 AD)
  4. 4. What are we talking about today Generate a plan Execute a plan Repair a plan “better” stands for: -More Robust How do it “better” ? - Optimal - Automated - Conflict free Generate Generate Input + Input or repair aa or repair PLAN PLAN Execute
  5. 5. Elements of a Planning System Algorithms Solver Problem Problem To Solve Problem Description Description Language Language Domain Domain Domain Description Description Language Language Environment
  6. 6. Motivation for Technology Infusion – Challenging Operations Scenarios Operations Pull – Planning and Scheduling, a process to consolidate – Adequately matured techniques ready to be Technology Push exploited
  7. 7. AI P&S technology supporting Mission Control, Ground Stations, and On- board P&S processes
  8. 8. Planning & Scheduling : a process to consolidate – Independent Tools for Mission Specific Long Term/Medium T/Short T P&S – Science Planning – Platform Operations Planning – Labor Intensive Ground Planning Tasks – Automatic Conflict Detection but Manual Conflict Resolution – Limited On-board Conditional Execution Plan
  9. 9. Planning & Scheduling : a possible future scenario Multimission Planning & Scheduling Infrastructure Intelligent Intelligent Intelligent Solver A Solver B Solver C
  10. 10. Bridging the gap Pre- Enhanced Catalyst : Operational Enabling P&S Concepts IWPSS 04 Prototyping Flying Mission Technology Use Case Modeling Mars Express Extended Solving on-board Operational memory dump Validation problem – Sponsorship, Chicken & Eggs – Current Missions: Test Beds for the Future MEXAR 2 Assessment RAXEM
  11. 11. A.I. Planning and Scheduling
  12. 12. Definitions – Planning : to devise or project the realization or achievement of a purpose – Automated planning and scheduling is a branch of artificial intelligence (AI) that concerns the automated realisation of strategies or action sequences – Usually there are 3 kinds of input : A domain (e.g., a set of possible actions that the planner can take); an initial state of the world, and the desired goals – OPS vs A.I. : different meanings Planning Scheduling A.I. Time FACD0013|0|5|2|1|A|0| FACD0014|0|5|2|1|A|0| OPS FACD0015|0|5|2|1|A|0| FACD0016|0|5|2|1|A|0| FACD0017|0|5|2|1|B|0| FACD0018|0|5|2|1|B|0|
  13. 13. A.I. P&S Technology – AI Model-Based Approach – Reusability – Flexibility and Adaptability – Timeline-Based Modeling – based on modeling core focuses on both temporal evolution of key components and the ability to capture relevant domain constraints – Capability of Scheduling Problem Representation and Solution – Relevant to real-world applications – Space mission operations (HSTS, EUROPA, ASPEN) – Robotics (IxTeT, IDEA, T-REX) – Manufacturing – Integration of human strategic capabilities and automatic problem solving algorithms – User Interaction - which supports different levels of interaction with a user
  14. 14. AI Model-Based Approach – Problem Solving – Flexibility Domain 2 – Scalability Domain 1 Domain – Adaptability (Unforeseen Events, Damages,…) AI Model-Based Solver Output 1 Output Output 2 – System Design – Rapid Prototyping Problem 1 – Portability Problem 2 – Reusability
  15. 15. Timeline-Based Modeling – Modelling – Focus on key features – Describe their possible consistent temporal behaviours Modeling – Represent the relevant constraints (domain theory) – Represent the management policy (control laws) Solving – Solving – Synthesize timelines according to current goals satisfying modelled constraints and management policy Problem Solving = Timeline synthesis
  16. 16. Timeline-Based Modeling Methodology (1) 1) Choose Components of the Domain Slewing(… Locked(st1) Unlocked(st1) Slewing(st1,…) ,st12) 4 t 3 0 t SwitchOn SwitchOff t
  17. 17. Timeline-Based Modeling Methodology (2) 1) Choose Components of the Domain 2) Model how the Components behave max O On Resource Max Availability [lb,ub] Burned Off On Off On t Durat ion
  18. 18. Timeline-Based Modeling Methodology (3) 1) Choose Components of the Domain 2) Model how the Components behave 3) Put them together and model the interactions Panoramic Camera TakePicture(rk1) Tracking Heat TakePict(rk2) CoolDown Microscopic Imager Stowed Unstow Place(rk2) TakeImage(rk2) Stow Mobility System At(rk1) Drive(rk1,rk2) At(rk2) Communication System Transmit Off Transmit
  19. 19. Why Timeline Planning ? Classical, Activity Planning Timeline-based planning – State-transition system – Temporal reasoning – Pre-conditions – Handling concurrency – Post-conditions – Action synchronization – Produces a sequence of – Explicit resource management actions that lead from an initial state to a state – The approach based on timeline which meets the desired synthesis has root in solid work in goals the space domain – RAX-PS/EUROPA [Jonsson et al., 2000], ASPEN [Chien et al., 2000] Causal Resource and Reasoning Time Allocation
  20. 20. Current experience and results Framework based Xmas AIMS From scratch MrSpock APSI Mexar2 Raxem SKeyP 2001 2009
  21. 21. Mexar2 – The problem: generation of Payloads Science Science Science Housekeepin spacecraft operations for efficient C B A g on-board mass memory dumping for MEX Spacecraft TM (Science + Housekeeping) – The downlink activities were TM synthesized manually by a team of Router Limited people continuously dedicated to Non visibility capacity windows this task Limited bandwidth – Several constraints & requirements: Earth Communication Channel limited on-board memory, limited communication capability, avoid data overwriting
  22. 22. Mexar2 Technical features and performance – Software design – Efficient solving algorithms (e.g., a – Object-oriented dump plan over a period of 30 days – Two modules: is computed within 1 minute of computation) – Problem Solver (PS) – MEXAR2 has reduced by 50% the time needed to generate dump – Man-Machine plans Interface (MMI) – Produces plans of higher quality – Implemented in Java without data loss (robustness) – Multiplatform: works – Allows to spot in advance resource under UNIX, Windows, bottlenecks (increased science Mac OSX return) – Interactive problem style allowing what-if analysis – MEXAR2 is a configurable tool (e.g., adding a new packet store)
  23. 23. SKeyP SOHO Keyhole Planner – The problem: to generate plans for Requirements & Goals SOHO Keyhole periods operations – satisfy the different constraints – Keyhole period: The HGA pointing (e.g., finite recorder capacity, DSN capability, recorder dumping antenna limitations, robustness) capabilities (possible only with DSN 34/70 m antennas) and recorder – flexibility in recorder usage, capacities are not sufficient to switching commands timings, etc. downlink all data, – allow exploration of options – selection and prioritization – reduce planner’s mechanical and repetitive tasks (and time) needed – Plan : to produce a baseline solution – What to store in the on- – reduce dependence on planner board memory experience – Data Downloading – Integration with the current Activities workflow
  24. 24. SKeyP Achievements – SKeyP solves the problem and – Different guidelines contributed to reduces the working time the current result – It produces a plan in – Users (mission planners) under 10 seconds integrated in the – Rapid what-if analysis, parameter development team set comparisons – Spiral iterative – Manual fine-tuning of prototyping & validation solutions cycles – Better understanding of – Solved problems in algorithm’s behaviour compatible time constants – SKeyP permits a fast handover between operational users – It has been easily integrated with the current workflow
  25. 25. APSI Advanced Planning & Scheduling Initiative – experimental software framework – operational validation of new AI P&S concepts & algorithms Modeling – open, plug in architecture Solving – reusable, scalable – coherent with the EGOS Mission Planning Framework approach
  26. 26. APSI Current Implementation Problem Solver User Interaction Services Software Interfaces Software Interfaces Domain Description Domain Domain Decision Network Language Layer Manager (current plan) (DDL.3) Component Layer Component1 Component2 Time & Parameters Time & Parameters Network Layer TRF
  27. 27. APSI Project Outcome User Interaction Specific Application Problem Knowledge Engineering for Application Support MrSPOCK AIMS XMAS Specific Application Mars Express APSI Integral XMM-Newton Specialized End Users for Long Term Mission Adv Mission Problem Solving Planning Scheduler Scheduler APSI FRAMEWORK APSI-TRF Timeline Representation Framework
  28. 28. MrSpock MEX Science Planning Opportunities Coordination Kit – Problem: to generate a pre- Aims: optimized skeleton plan for Mars – Minimize the iterations between Express Long Term Planning Science Team and Mission Planning Team, taking into account a very LTP PI Payload request Long Term detailed scenario and several co- Plan existing constraints Mission Planning Science Team Mission Planning Science Team Team Team MTP – Provide the ability to explore the MTP Medium Term Payload request Medium Term Plan solution space according to different PI Plan STP STP optimization functions Plan Short Term Short Term refinements Plan Plan – maximize planned science – maximize total – Integration of: UpLink/DownLink (UL/DL) – Ground station availability time – Uplink activities – Spacecraft maintenance – Downlink activities – Science at pericentres
  29. 29. MrSpock Conclusions & Recommendations – Multi-dimensional constraint / solution – Benefits achieved with the use of space using AI Genetic algorithm the APSI framework: – Successful iterative prototyping – The application design development time is shortened – reduced distance between – Plan to use operationally for 2010 the domain and the – Expected benefits: application model – Improved use of uplink & – Reduced coding time downlink channels (+ 5% increase of traffic) – New exploited opportunities: VMC/webcam@Mars – Faster planning cycle (cost reduction)
  30. 30. AIMS APSI INTEGRAL Mission Scheduler – The problem: to build and optimize – Generally, not possible to make all a long-term observation plan (1 obs. (over-constrained problem) year) for INTEGRAL – Quality of a consistent plan depends – Standard constraints: on: – obs. activities included in – the completion of visibility windows observations – no overlap for obs. – the way each observation Activities is realized – Special constraints: – the priority degree of – existence of special observations observations (periodic, spread, no splitting) – existence of a maximum filling factor for each revolution – maximum number of obs. activities per revolution
  31. 31. AIMS Achievements – Scheduling is now automatic: much Benefits achieved by using APSI less physical labour intensive...! – Tasks delegated to the APSI core – Provides various solutions: pick the framework: best, save, compare, etc. – check all standard constraints – Takes a coffee break to get a decent – extract precise start times Long Term Plan for observation activities – Easy updates on past schedule info – Tasks handled directly in AIMS: from operational database – deal with special – Operational scientists are happy constraints – optimization task – Under validation for operational use – Input, LTP-scheduling, output + monitoring status in 1 tool – development of eAIMS
  32. 32. Advanced Planning & Scheduling : An added value for operations Current space operation systems Why adding advanced P&S on top? – Identify, retrieve, and merge necessary – Problem solving functionalities information – Managing Resource Conflict – Timeline model – Propagation through rules definition – Optimization – Science return – Identify possible conflicts – Platform utilization – Robustness & Flexibility of the solutions – Integration of human strategic capabilities and automatic problem solving algorithms – Decision support system More science return, Reduced operations cost, Reduced resources utilisation
  33. 33. Transfer to Infrastructure
  34. 34. Mission Planning System Framework – Objectives – Provide support for the various types of missions supported by ESOC, e.g. – Deep Space/Planetary – Earth Observation – Observatory – Provide framework that can be used by mission as a basis for their planning system – Provide standard format for inputs and outputs (Planning File ICD) – Provide straightforward mechanisms for allowing for extension (e.g. A.I. algorithms integration) MPSF will not be a generic planning system
  35. 35. Mission Planning Typical Workflow (Deep Space)
  36. 36. MPSF Architecture – Conceptual View
  37. 37. MPSF – Offline Planning – Offline Planning – defining the “building blocks” of a plan, e.g. – rules and constraints that apply to the mission (e.g. instrument A cannot be active when instrument B is on, resource limits etc.), – definition of “Plan fragments”, i.e. templates of pre- planned operations that can be used in building a plan. – large part before launch, but continued evolution throughout mission due to – instruments degradation – revision of operational constraints – mission objectives evolution
  38. 38. MPSF – Online Planning – Online Planning – Plan initialization – External data ingestion – Rules and constraints propagation – Plan validation and adjustment – Plan consolidation – Scheduling
  39. 39. OCC (OPSLan) Groundstation G/S Schedules (GRSS) STC EMS EMS OPS EMS MATIS Monitoring FIDES and Control G/S Schedules (GRSS)+ Data Service Instance Configuration Files (SICF) Files SFC SFS EMS EMS EMS EMS SMF FARC MPS Radiometric Misc. data Files EMS G/S Sub- EMS systems EMS + EMS EMS EMS IFMS LTA EMS TM, TC Data EMS EMS EMS and files MCS NIS SimSat Service DDS TM and TC TM and TC Instance Data Data Configuration FDS Files (SICF) TMTCS TM and TC Planning and Data Data TM and TC Requests from External Data Entities + Data to External Entities Predicts and Radiometric data OCC (RelayLan) External Files Transferred by GFTS Services Provided by SMF EMS DDS Planning and Data Requests EMS External Files from/to File Archive EMS from External Entities + EMS Other data transfer Front end Data to External Entities Entities mechanisms (e.g. TCP)
  40. 40. MPSF – Status – Currently – MPSF Architectural design completed. – Rules engine not specified in detail – MPSF development of Online functionality started – Agile approach adopted – rapid feedback from end users – Future – Refinement of offline requirements and architectural design should be carried out – Now possible to more closely integrate with MOIS than was originally foreseen – Define requirements for rules engine (probably in the context of a mission development) – Use 3rd party product, e.g. DROOLS ? – Build on LMP used in VEX and EMS ?
  41. 41. Conclusions
  42. 42. Ongoing projects – Autonomous Controller (TRP) (TEC-MMA) (reusing APSI) – IRONCAP (TRP) – Consolidation of Domain Description Language – Formulation of Problem Description Language
  43. 43. Future Work – Completion of APSI framework and associated DDL and PDL documented and available for ESA member states’ R&D and Industry – Potential P&S Upcoming Applications (Algorithms) for – ESTRACK and ESA’ Deep Space Network scheduling – Science Operations – GMES – COL-CC Payload P&S – ATV rendez-vous – Contribution as building block for GOAL Based Operation scenario demonstration
  44. 44. Lessons Learnt – Relationship with Stakeholders – Mission Operations and Mission Managers – Key Role of the Use Case Owner: – Empowerment during the Development – Ambassadorship during the Operational Assessment – Ground Segment Infrastructure Managers – Coordination – Compatibility with Standards and Interfaces
  45. 45. Lessons Learnt – Cost Benefit Analysis – Results have to Justify the Effort Spent – Use of a Framework Ease Reuse and Improves the ROI – A consolidated efficient and effective modelling approach will further burst the ROI in introducing AI P&S in the space domain
  46. 46. Lessons Learnt – Leverage on Scientific Community – Open Reusable Framework allows easy sharing of use cases, benchmarks, algorithms and innovative technologies – Timeline based planning and scheduling is becoming the current ESA reference approach for AI P&S – Other solving approaches might well be considered and validated (e.g. mathematical programming)
  47. 47. Recognition & Acknowledgement – ESA and ESOC has gained outstanding recognition for the Infusion of A.I. Planning & Scheduling technology in Mission Operations – Best Application Award @ ICAP 2007 – NASA Recognition of MEXAR @ iSAIRAS 2008 – ESA Keynote Speech at IWPSS 09 – Technology Transfer to D/TEC – All work so far thanks to full commitment and expertise of – European Research Institutes (CNR-ISTC, ONERA, PoliMI) – Industrial Partners – OPS-O, OPS-G, OPS-HAS, OPS-HSC (initiator & in-house know-how) – TEC-MMA, SRE-OA, SRE-PAT
  48. 48. Infusion of Advanced Planning and Scheduling Technology in Space Time for Questions Alessandro Donati Alessandro.Donati@esa.int Publications available at: http://opstools.esoc.esa.int/wiki/bin/view/Groups/OPS_HSC/PublicationsHSC

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