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Dynamic Mission Planning for Multiple Mobile Robots Barry Brumitt and Anthony Stentz 26 Oct, 1999 AMRS-99 Class Presentation Brian Chemel
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Dynamic Mission Planning for Multiple Mobile Robots Overview Problem description Mission grammar System architecture: GRAMMPS Brief digression: D* Results Analysis and limitations
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Dynamic Mission Planning for Multiple Mobile Robots Problem Description: Environment Dynamic, complex environment Typical situation World state initially unknown Runtime observations incorporated into shared world model
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Dynamic Mission Planning for Multiple Mobile Robots Problem Description: Robots Multiple, heterogeneous mobile robots Assumptions Robots have relatively open workspaces, so solution set is not too sparse Effective positioning, communication and perception are a given
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Dynamic Mission Planning for Multiple Mobile Robots Problem Description: Goals Robot task: move to specified locations, in specified order Reconnaissance example Waypoints to be scouted by team of robots Warehouse example Multiple pickup points, to be followed by multiple delivery points
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Dynamic Mission Planning for Multiple Mobile Robots Problem Description: GRAMMPS Planner Military reconnaissance task Outdoor environment Multiple robot vehicles (Navlab HMMWV’s, in practice) Multiple distributed goals, with sequencing Requires mission grammar to pass parameters to distributed planning system
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Dynamic Mission Planning for Multiple Mobile Robots Mission Grammar ExpressionMeaning “Do A, then do B” A OR B A AND B Robot i Goal j “Move robot r to goal g”
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Dynamic Mission Planning for Multiple Mobile Robots Mission Grammar: Example “Move either robot 1 or robot 2 to goals 1, 2, 3, and 4. Then move both robots 1 and 2 to goal 5.”
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Dynamic Mission Planning for Multiple Mobile Robots System Architecture: Overview Global (shared) dynamic planners Global (shared) mission planner Local (individual) plan execution Mission Planner D* … Dynamic Planners (one per goal) Robot 1Robot 2Robot n …
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Dynamic Mission Planning for Multiple Mobile Robots System Architecture: Local Navigators Input Path to assigned goal Perception information Output Steering commands Mission Planner D* … Dynamic Planners (one per goal) Robot 1Robot 2Robot n …
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Dynamic Mission Planning for Multiple Mobile Robots System Architecture: Mission Planner Input Estimated path costs for each (robot,goal) pair Output Mapping from robots to goals Algorithm TSP heuristic Mission Planner D* … Dynamic Planners (one per goal) Robot 1Robot 2Robot n …
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Dynamic Mission Planning for Multiple Mobile Robots System Architecture: Dynamic Planners Input Current world state knowledge Output Path to each goal for each robot Planning algorithm D* Mission Planner D* … Dynamic Planners (one per goal) Robot 1Robot 2Robot n …
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Dynamic Mission Planning for Multiple Mobile Robots Brief Digression: The D* Algorithm Modification of the A* planning algorithm Provides efficient, optimal and complete path planning in unknown, partially known, and changing environments As new information about the environment is learned, cost information is propagated through state space Each time new information makes previous path calculations obsolete, a new path is calculated Original paper: Stentz, ICRA ‘94
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Dynamic Mission Planning for Multiple Mobile Robots Example: Simulation Initial Plan Mission statement:
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Dynamic Mission Planning for Multiple Mobile Robots Example: Simulation Final Plan Goals re-assigned on the fly Mission successfully completed
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Dynamic Mission Planning for Multiple Mobile Robots Highlights Demonstration implementation on Navlab HMMWVs allows real-time, team-based mission planning in dynamic environments System scales gracefully up to large numbers of robots and goals
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Dynamic Mission Planning for Multiple Mobile Robots Limitations Waypoint task structure is very limiting No discussion of how to modify TSP approach to allow heterogeneity One robot must act as “leader” of the entire team When D* fails, system can lock up
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Dynamic Mission Planning for Multiple Mobile Robots Related Work Stentz, T. “Optimal and Efficient Path Planning for Partially-Known Environments.” ICRA ’94 Brumitt, B., Stentz, T. “GRAMMPS: A Generalized Mission Planner for Multiple Mobile Robots in Unstructured Environments.” ICRA ‘98 Brumitt, B., Hebert, M. “Experiments in Autonomous Driving With Concurrent Goals and Multiple Vehicles.” ICRA ‘98
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