AI Planning & Scheduling and Data Analytics for a new generation of Mission Planning tools

1. AI Planning & Scheduling and Data Analytics for a new generation of Mission Planning tools S. Fratini & N. Policella

2. Solenix: Our Services Consulting Services • Spacecraft Operations • Ground Segment Engineering • Management Support • Research of Advanced Technologies and Concepts • Data Processing Software Products • Support to management and operations of complex systems • Focus on usability and ergonomics • High performance • Integration capabilities Software Engineering • Client solutions: mobile and web clients • Distributed systems • Data-driven systems: data management & analysis • Complex graphical data visualisation 1st International Round Table on Intelligent Control for Space Missions

3. Intelligent Control 1st International Round Table on Intelligent Control for Space Missions

4. Intelligent Control 1st International Round Table on Intelligent Control for Space Missions

5. Automated Planning and Scheduling? Wikipedia says… • Automated planning and scheduling, sometimes denoted as simplyAI Planning, is a branch ofartificial intelligence that concerns the realization of strategies or action sequences, typically for execution by intelligent agents, autonomous robots and unmanned vehicles. • In AI Planning, planners typically input a domain model (a description of a set of possible actions which model the domain) as well as the specific problem to be solved. Such planners are called "Domain Independent" to emphasis the fact that they can solve planning problems from a wide range of domains. • A schedule… consists of a list of timesat which possible tasks, events, or actionsare intended to take place, or of a sequence of events in the chronological order in which such things are intended to take place. The process of creating a schedule - deciding how to order these tasks and how to commit resourcesbetween the variety of possible tasks - is called scheduling. “I keep six honest serving men (they taught me all I knew); their names are What and Why and When and How and Where and Who.” --- Rudyard Kipling 1st International Round Table on Intelligent Control for Space Missions

6. Planning and Control 1st International Round Table on Intelligent Control for Space Missions

7. Planning Scheduling A.I. Time FACD0013|0|5|2|1|A|0| FACD0014|0|5|2|1|A|0| 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| Space Operations Scheduling vs. Planning in Operations Space operations vs. A.I. : different meanings 1st International Round Table on Intelligent Control for Space Missions

8. Realistic Planning Problems • Time • Numerical Reasoning • Concurrent actions • Context–Dependent Effects • Interaction with Users, “man in the loop” • Oversubscribed Problems • Focus on Optimization rather than on Achievement • Robustness, Flexibility • Execution Monitoring • Re-Planning & Re-Scheduling • Scalability • Integration of external solving processes • Need for Formal Verification and Validation of Plans and Schedules 1st International Round Table on Intelligent Control for Space Missions

9. Knowledge in Automated Planning and Scheduling • Knowledge about the domain • Timing, causal relationships, resources • Knowledge about “good plans” • Oversubscription, Optimization, Robustness, Flexibility • Explicit search-control knowledge • Domain specific know-how, Optimization • Knowledge about the user and about user’s preferences • “man in the loop” • Knowledge about plan repair during execution • Execution monitoring, re-planning and re-scheduling Explanation 1st International Round Table on Intelligent Control for Space Missions

10. Integrating Planning and Scheduling It does not matter how many resources you have… …if you don’t know how to use them! 1st International Round Table on Intelligent Control for Space Missions

11. Integrating Planning and Scheduling “Planning & scheduling are rarely separable” 1st International Round Table on Intelligent Control for Space Missions

12. AI Planning in Space FCT (Flight Control Team) Lander Payload Experts PI (Principal Investigators) S/C Attitude, Modes Planning Requests SPT Science Plan Science Planning Team (ESA-ESAC, Spain) MPT TM (TeleMetry) TC (TeleCommands) UL/DL Plan Mission Planning Team (ESA-ESOC, Germany) Ground Station Management GS Plan Science Planning Ground Segment Robotics U/L (UpLink) OnBoard Segment D/L (DownLink) S/C S/C Autonomy Mission Planning Ground Station Planning

13. Timelines Timeline (n): a graphical representation of a period of time, on which important events are marked. - Oxford English Dictionary A timeline is a way of displaying a list of events in chronological order, sometimes described as a project artifact. It is typically a graphic design showing a long bar labeled with dates alongside itself and (usually) events labeled on points where they would have happened. - Wikipedia 1st International Round Table on Intelligent Control for Space Missions

14. Solving Modeling Timeline Based Approach • Modelling • Focus on key systems • Describe their possible consistent temporal behaviours • Represent the relevant constraints(domain theory) • Solving • Synthesize timelines according to current goals satisfying modelled constraints Problem Solving = Timeline synthesis 1st International Round Table on Intelligent Control for Space Missions

15. On Burned Off Timeline-Based Planning & Scheduling max Off() On() On() t O Symbolic Value Constraints Transitions O [lb,ub] Numeric Off() On() On() 1 - Represent Features 2 – Describe Features t Temporal Constraints Pulses 1st International Round Table on Intelligent Control for Space Missions 3 – Synchronize Features

16. Architectures 1st International Round Table on Intelligent Control for Space Missions

17. Timelines in ESA 1st International Round Table on Intelligent Control for Space Missions

18. The ESA APSI Framework 1st International Round Table on Intelligent Control for Space Missions

19. Alphasat TECO system 1st International Round Table on Intelligent Control for Space Missions

20. Alphasat TECO system • The Alphasat spacecraft carries an Inmarsat communications payload and four Technology Demonstration Payloads (TDPs), provided under ESA responsibility • ESA is in charge of coordinating the use of the different TDPs through the TDP ESA Coordination Office (TECO) • TECO’s main objective is to manage the selection of TDP science programs, planning, and data archiving for Alphasat Inmarsat TDP Operations Centres ESA TECO TDP Operations Centres TDP Operations Centres Consolidated plan Individual requests TDP Operations Centres Plan and execution feedback Execution feedback 1st International Round Table on Intelligent Control for Space Missions

21. Alphasat TECO system • Weekly planning of the execution of the activity requests from the different TDP OCs on the shared Inmarsat platform • Temporal constraints (e.g. do not perform science during chemical manoeuvres) • Resource constraints (e.g. limited power and bandwidth) • The goal is to deliver an automated planning system requiring as little operator involvement as possible: • Nominal robust plan generation and validation is performed automatically by the software system • Operator involvement is required only in case of anomaly • Challenge: • Automatic explanation approach (as the decisions taken are taken by an automatic system) • No black-box • Build trust with the final users 1st International Round Table on Intelligent Control for Space Missions

22. In-flight experience • TECO systems has proved to be • reliable, • robust, • flexible • TECO System grants • to Inmarsat (mission operator and satellite owner) a safe and transparent operations, and • to the hosted payloads the maximum experimental return • Nominal operations phase since January 1st, 2014 • More than 35K activities for the TDPs have been scheduled. • All the abortions were fully explained and understood, with none of them due to the activities planning (i.e., TECO System). 1st International Round Table on Intelligent Control for Space Missions

23. TIAGO – Tool for Intelligent Allocation of Ground Operationsfor Cluster-II 1st International Round Table on Intelligent Control for Space Missions

24. Cluster-II Mission Overview The Cluster-II mission is part of ESA’s Horizon 2000 missions programme from 1985 investigating the Earth’s magnetic field with 10 European and 1 American scientific instrument. • 4 identical satellites with magnetic cleanliness • Launch in 2000 (first launch in 1996 failed) • Extended until 2018 • Apogee: ~110.000km • Perigee: ~20.000km • Orbital period: 54.3h 1st International Round Table on Intelligent Control for Space Missions

25. Problem Overview Allocate ground station passes to… • download all scientific data recorded from instruments • avoid overflow of on-board memory • keep on-board memory level < 80% (robustness) Constraints: • Ground station availability & booking procedures • Link Budget: changing downlink bitrates (due to highly elliptic orbit) • Science Modes: Intense commanding at the beginning/ending of the pass • Operational Constraints (duration, separation, frequency) 1st International Round Table on Intelligent Control for Space Missions

26. Original Approach Planning activities can take up to 1.5 man-days per week 1st International Round Table on Intelligent Control for Space Missions

27. New Workflow 1st International Round Table on Intelligent Control for Space Missions

28. An Integrated P&S Problem Planning/Scheduling 1st International Round Table on Intelligent Control for Space Missions

29. Tiago MMI 1st International Round Table on Intelligent Control for Space Missions

30. Autonomy & Robotics 1st International Round Table on Intelligent Control for Space Missions

31. Intelligent Control 1st International Round Table on Intelligent Control for Space Missions

32. Motivation for Autonomy • Upcoming missions will require a higher degree of remote operations to increase quality and quantity of science return • Remote operations are a challenging scenario, mainly because communication delays and errors • Autonomy can entail opportunistic science, provide contingency recovery procedures and allow fast reaction to tracked events • AI planning-based control layers have demonstrated to be able to entail autonomy, but modeling still constitute a bottleneck for the use of these technologies 1st International Round Table on Intelligent Control for Space Missions

33. OPS-OSA Activities – Autonomy & Robotics • APSI - Advanced Planning & Scheduling Initiative (ESOC GSP Study, 2007-2008) • GOAC - Goal Oriented Autonomous Controller (ESTEC TRP Study, 2009-2011) • IRONCAP (2011-2012) • Application on TeleRobotics (2013) • On-Board Autonomy for NetSat (2016-) 1st International Round Table on Intelligent Control for Space Missions

34. The GOAC Approach • A goal-oriented system • Interleaved sense-plan-act cycles (reactors) • Reactors embed a domain independent, model driven, timeline-based planning technology • Well defined interaction protocol: • Goals (up to down) • Observations (down to up) • Increased depth allows HTN and planning at different temporal scopes • Increased width allows planning with different functional scopes 1st International Round Table on Intelligent Control for Space Missions

35. GOAC Achievements • Support for “Long”, “Medium” and “Short” Term planning: • Planning for mission goals, complex planning problems, long horizon • Planning for platform management goals, medium term horizon • Task sequencing, fast planning, very short horizon • Interleaved Planning and Execution • Plan flexibility: plans are flexible enough to allow a smooth execution • Planning and Re-Planning 1st International Round Table on Intelligent Control for Space Missions

36. IRONCAP • Goal: Preparing ESA for future robotics missions operations through the Investigation and Prototyping of Innovative Planning Operations Concepts for Rovers equipped with Autonomy Capabilities • Developing an operational concept for autonomous Rovers and define the processes and tools required for Rover ground control. • Developing a prototype of a Rover planning and scheduling facility supporting the operational concept • Demonstratingand evaluating the prototype in the context of two case studies 1st International Round Table on Intelligent Control for Space Missions

37. Application on TeleroboticsThe MOCUP rover with LEGO NXT 2.0 Mindstorms kit. Deliberative Layer - APSI Executive Agent - GOAC FunctionalLayer – METERON Infrastructure TeleCommands Telemetry 1st International Round Table on Intelligent Control for Space Missions

38. Monitoring, Novelty Detection & Diagnostics, DrMUST Courtesy of Jose Martinez Heras – Black Hat S.L. 1st International Round Table on Intelligent Control for Space Missions

39. Intelligent Control 1st International Round Table on Intelligent Control for Space Missions

40. From Monitoring… • Is everything fine? • Classic: Out-Of-Limits (OOL) Hard Limit Soft Limit Soft Limit Hard Limit 1st International Round Table on Intelligent Control for Space Missions

41. OOL …to Anomaly Detection • Is everything fine? • Anticipate Anomalies: Anomaly Detection 20,000 – 40,000 TM parameters Unusual behaviour Potential anomaly 1st International Round Table on Intelligent Control for Space Missions

42. Pattern Matching DrMUST Correlator Diagnostics Anomaly: deviation from expected behaviour. • Recognized by particular behaviour of one or more parameters over a time period • Key questions: • Does this anomaly already occurred in the past and went unnoticed? When? • What are the effects on this anomaly? • What are its causes? • Can we minimize its effects? • Can we prevent this anomaly from happening again? 1st International Round Table on Intelligent Control for Space Missions

43. Traditiona Traditional DrMUST Anomaly investigation approach Very labour intensive (weeks) Automatic Runs unattended (minutes / hours) 1st International Round Table on Intelligent Control for Space Missions

44. Operational Assessment • Allows “Googling” through spacecraft data • Searching for similar occurrences • Or correlated occurrences • Impressive performance • Queries run very quickly compared to manual searches 1st International Round Table on Intelligent Control for Space Missions

45. Thermal Power Consumption Prediction + Produced Power - Platform Power - Thermal Power Science Power

46. Data Mining Competition http://kelvins.esa.int

47. Data Mining Competition Amperes Real Telemetry Prediction Time 1st International Round Table on Intelligent Control for Space Missions

48. Conclusions 1st International Round Table on Intelligent Control for Space Missions

49. Closed Loop for Intelligent Control APSI TIAGO Temporal Planning Resource Allocation and Optimization GS Mission Planning Support Anomaly Detection Dr. MUST Prediction TECO Timeline-Based Execution Flexibility and Controllability Support for Autonomy 1st International Round Table on Intelligent Control for Space Missions

50. Lessons Learned • Declarative Model-Based Engine: • Support cost reduction, foster reusability • Ease interoperability and standards design • Verification & Validation • Support for what-If Analysis • Timeline-Based AI Planning: • Model cognitively close to the users’ practice • Algorithm synthesis oriented to the explanability of the solution • Relevance of user interaction services: • Support for a mixed-initiative approach to the solving process • Man-in-the-loop 1st International Round Table on Intelligent Control for Space Missions