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Applications spatiales nécessitant de la p lanification d’actions concurrente sous incertitude

Explore the challenges and solutions for planning actions concurrently in spatial applications under uncertainty, focusing on mission planning for Mars rovers. The application involves generating plans with concurrent actions under resource and time uncertainty, optimizing objective functions, and using probabilistic heuristic methods. Literature review and existing approaches are discussed to tackle the uncertainties associated with planning spatial missions. Dive into the complexities of planning with uncertain outcomes and durations in spatial applications. Implement a project to tackle these challenges.

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Applications spatiales nécessitant de la p lanification d’actions concurrente sous incertitude

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  1. Applications spatiales nécessitant de la planification d’actions concurrente sous incertitude Éric Beaudry http://planiart.usherbrooke.ca/~eric/ 6 juin 2011

  2. Observation de la Terre Robots sur Mars

  3. Image Source : http://marsrovers.jpl.nasa.gov/gallery/artwork/hires/rover3.jpg Sample application Mission Planning for Mars Rovers

  4. Mars Rovers: Autonomy is required Robot Sejourner > 11 Minutes * Light

  5. Mars Rovers: Constraints • Navigation • Uncertain and rugged terrain. • No geopositioning tool like GPS on Earth. Structured-Light (Pathfinder) / Stereovision (MER). • Energy. • CPU and Storage. • Communication Windows. • Sensors Protocols (Preheat, Initialize, Calibration) • Cold !

  6. Mars Rovers: Uncertainty (Speed) • Navigation duration is unpredictable. 5 m 57 s 14 m 05 s

  7. robot robot Mars Rovers: Uncertainty (Speed)

  8. Mars Rovers: Uncertainty (Power) • Required Power by motors  Energy Level Power Power Power

  9. Mars Rovers: Uncertainty (Size&Time) • Lossless compression algorithms have highly variable compression rate. Image size : 1.4 MB Time to Transfer: 12m42s Image size : 0.7 MB Time to Transfer : 06m21s

  10. Mars Rovers: Uncertainty (Sun) Sun Sun Normal Vector Normal Vector

  11. Objectives

  12. Goals • Generating plans with concurrent actions under resources andtime uncertainty. • Time constraints (deadlines, feasibility windows). • Optimize an objective function (i.e. travel distance, expected makespan). • Elaborate a probabilistic admissible heuristic based on relaxed planning graph.

  13. Assumptions • Only amount of resources and action duration are uncertain. • All other outcomes are totally deterministic. • Fully observable domain. • Time and resources uncertainty is continue, not discrete.

  14. Dimensions • Effects: DeterministvsNon-Determinist. • Duration: Unit (instantaneous) vs Determinist vs Discrete Uncertainty vsProbabilistic (continue). • Observability : Fullvs Partial vs Sensing Actions. • Concurrency : Sequential vsConcurrent (Simple Temporal) []vs Required Concurrency.

  15. Literature review

  16. Existing Approaches • Planning concurrent actions • F. Bacchus and M. Ady. Planning with Resource and Concurrency : A Forward Chaining Approach. IJCAI. 2001. • MDP : CoMDP, CPTP • Mausam and Daniel S. Weld. Probabilistic Temporal Planning with Uncertain Durations. National Conference on Artificial Intelligence (AAAI). 2006. • Mausam and Daniel S. Weld. Concurrent Probabilistic Temporal Planning. International Conference on Automated Planning and Scheduling. 2005 • Mausam and Daniel S. Weld. Solving concurrent Markov Decision Processes. National Conference on Artificial intelligence (AAAI). AAAI Press / The MIT Press. 716-722. 2004. • Factored Policy Gradient : FPG • O. Buffet and D. Aberdeen. The Factored Policy Gradient Planner. Artificial Intelligence 173(5-6):722–747. 2009. • Incremental methods with plan simulation (sampling) : Tempastic • H. Younes, D. Musliner, and R. Simmons. « A framework for planning in continuous-timestochastic domains. International Conference on Automated Planning and Scheduling(ICAPS). 2003. • H. Younesand R. Simmons. Policy generation for continuous-time stochastic domains withconcurrency. International Conference on Automated Planning and Scheduling (ICAPS). 2004. • R. Dearden, N. Meuleau, S. Ramakrishnan, D. Smith, and R. Washington. Incremental contingency planning. ICAPS Workshop on Planning under Uncertainty. 2003.

  17. Families of Planning Problems with Actions Concurrency and Uncertainty Fully Non-Deterministic (Outcome + Duration) + Action Concurrency FPG[Buffet] + Deterministic Outcomes [Beaudry] [Younes] + Sequential (no action concurrency) [Dearden] + Discrete Action Duration Uncertainty CPTP[Mausam] + Deterministic Action Duration = Temporal Track at ICAPS/IPC Forward Chaining [Bacchus] + PDDL 3.0 + Longest Action CoMDP[Mausam] MDP Classical Planning A* + limited PDDL The + sign indicates constraints on domain problems.

  18. Application 2 : observation de la Terre • Conditions d’acquisition (ex: météo) incertaines(très problématique pour les données optiques). • Des requêtes urgentes peuvent survenir. • Les fenêtres de communications sont limitées. • Capacité de stockage limitée sur les satellites. • Les changements d’orbite sont coûteux. • Volume de données incertain. • Besoin de planifier les actions pour optimiser les acquisition de données. • Réf.: [Capderou 2002]. RadarSat II

  19. Planification classique

  20. Planification classique

  21. Planification temporelle

  22. Planification avec actions concurrentes

  23. MDP : Séquence d’actions avec incertitude

  24. Incertitude sur le temps

  25. Comment combiner incertitude, incertitude sur le temps, et actions concurrente ?

  26. Voir diapos 21 à 39de ma présentation @UQAM

  27. Ces défis vous intéressent ?

  28. Ces défis vous intéressent ? • Projet libre en IFT615 (3 à 5 semaines) • Projets IFT592/692 (3 ou 6 crédits) • Stage en recherche / Bourse CRSNG 1ercycle • Minimum 5625 $ (bourse non imposable) • Durée de 16 semaines • Peut être ou ne pas être un stage coop • Moyenne de B- • Excellente expérience avant la maîtrise • CRSNG (Conseil de la recherche en sciences naturelles et génie) • Infos: http://www.crsng.ca ou un prof du département

  29. Maitrise type recherche • Maitrise = initiation à la recherche • Projet de recherche (travail individuelle / équipe) • 5 cours gradués • Possibilité de publier dans des journaux et conférences scientifiques (voyages !) • Financement • Bourses subvention d’un prof-chercheur : ~ 12 k$ / an. • Bourses CRSNG (17 k$ / 12 mois) • Bourses FQRNT (15 k$ / 4 sessions) • Bourses CRSNG à incidence industrielle (15 à 25 k$ / an). • CRSNG : http://www.crsng.ca/ . • FQRNT : http://www.fqrnt.gouv.qc.ca/ .

  30. Chercheurs • Eric Beaudry @ • Froduald Kabanza @

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