110 likes | 207 Views
How is Planning & Scheduling Changing the World? . Activity Info. Plan Updates. Plan Manager. Client Modeler. Sensor Data. Inferred Activity. Client Model. Client Plan. Activity Info. Client Model Info. Intelligent Reminder Generator. Reminders. Preferences.
E N D
Activity Info Plan Updates Plan Manager Client Modeler Sensor Data Inferred Activity Client Model Client Plan Activity Info Client Model Info Intelligent Reminder Generator Reminders Preferences Autominder: An Intelligent Cognitive Orthotic System A brief word on the State of the Practice PI: Martha Pollack
Daily automatic selection and scheduling of complex observations: mono or stereo, ground targets or areas. Fair sharing of the use of the satellite between several owner entities Pléiades:Earth Observation Satellite Management Jean Michel Lachiver, CNES Michel Lemaître, Gérard Verfaillie, ONERA Toulouse, France
Problem: Day-to-day allocation of aircraft & crews to airlift/tanker missions Characteristics Large scale: 1,000s of missions; 100s of assets Continuous, dynamic stream of mission requirements Core Technology: Incremental, constraint-based search Rapid gen. of airlift/tanker schedules Localized revision in response to changing circumstances Flexible, what-if option generation Status: Embedded in AMC’s operational planning system & transitioning into use DARPA The AMC Allocator:Advanced Scheduling for US Airforce Carnegie Mellon PI: Steve Smith
ICAPS 2005 http://siadex.ugr.es Distributed execution over the internet Andalusian Regional Ministry Of The Enviornment (SPAIN) University of Granada SEPIA Planning Group PI: Luis Castillo
The EO-1 Autonomous Sciencecraft Onboard planning part of autonomy software flying onboard Earth Observing One Spacecraft Fall 2003 – present Planning software onboard enables spacecraft to autonomously monitor and retarget volcanoes, flooding, cryosphere PI: Steve Chien
Robust Task Execution for Rovers: LITA • ASTEP LITA Atacama Field Campaign (Sep-Oct 2004) • Zöe rover with life detecting instruments • On-board planning and autonomous navigation over long distances • Rover executive results (preliminary, telemetry still being analyzed) • Total hours of operations (cumulative over several runs): 17 hours • Total distance covered: 16 km • Longest autonomous traverse: 3.3Km 2h 29m • “Roughest traverse”: 1h 2m with 19 faults recovered • Faults addressed: • Navigator “confused” • Internal processes failed • Early and late arrival at waypoint IDEA PI: Nicola Muscettola Courtesy: Nicola Muscettola
The problem of spacecraft memory dumping Domain ESA Mars Express mission Problem components Finite memory banks Limited downlink windows Limited data rate Input-solver Interface Solver-output Interface Mexar Solver PI: Amedeo Cesta Planning and Scheduling Team, CNR - Italy – http://pst.istc.cnr.it
Commanding Spirit & Opportunity with MAPGEN • Mixed-Initiative ground-based Activity Planning Decision Support system • Generative planning • Plan editing • Constraint formulation and moves • Deals with time and resources • First AI based system to command a vehicle on the surface of another planet • ROI for NASA > 20% for science return in comparision to a manual planning process PI: Kanna Rajan