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Applications spatiales nécessitant de la planification d’actions concurrente sous incertitude Éric Beaudry http://planiart.usherbrooke.ca/~eric/ 6 juin 2011 Robots sur Mars Observation de la Terre 2 Image Source : http://marsrovers.jpl.nasa.gov/gallery/artwork/hires/rover3.jpg Sample application MISSION PLANNING FOR MARS ROVERS 3 Mars Rovers: Autonomy is required Robot Sejourner > 11 Minutes * Light 4 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 ! 5 Mars Rovers: Uncertainty (Speed) • Navigation duration is unpredictable. 5 m 57 s 14 m 05 s 6 Mars Rovers: Uncertainty (Speed) robot 7 Mars Rovers: Uncertainty (Power) • Required Power by motors Energy Level Power Power Power 8 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 9 Mars Rovers: Uncertainty (Sun) Sun Sun Normal Vector Normal Vector 10 OBJECTIVES 11 Goals • Generating plans with concurrent actions under resources and time 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. 12 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. 13 Dimensions • Effects: Determinist vs Non-Determinist. • Duration: Unit (instantaneous) vs Determinist vs Discrete Uncertainty vs Probabilistic (continue). • Observability : Full vs Partial vs Sensing Actions. • Concurrency : Sequential vs Concurrent (Simple Temporal) [] vs Required Concurrency. 14 LITERATURE REVIEW 15 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-time stochastic domains. International Conference on Automated Planning and Scheduling (ICAPS). 2003. – H. Younes and R. Simmons. Policy generation for continuous-time stochastic domains with concurrency. 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. 16 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] + Longest Action CoMDP [Mausam] + Deterministic Action Duration = Temporal Track at ICAPS/IPC Forward Chaining MDP Classical Planning A* + limited PDDL [Bacchus] + PDDL 3.0 17 The + sign indicates constraints on domain problems. 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 18 PLANIFICATION CLASSIQUE Planification classique Planification temporelle Planification avec actions concurrentes MDP : Séquence d’actions avec incertitude Incertitude sur le temps COMMENT COMBINER INCERTITUDE, INCERTITUDE SUR LE TEMPS, ET ACTIONS CONCURRENTE ? Voir diapos 21 à 39 de ma présentation @UQAM CES DÉFIS VOUS INTÉRESSENT ? Ces défis vous intéressent ? • Projet libre en IFT615 (3 à 5 semaines) • Projets IFT592/692 (3 ou 6 crédits) • Stage en recherche / Bourse CRSNG 1er cycle – – – – – – – Minimum 5625 $ (bourse non imposable) Durée de 16 semaines Peut être ou ne pas être un stage coop Moyenne de BExcellente 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 @