l2_pro_path_plan

l2_pro_path_plan - 16.412/6.834J Cognitive Robotics...

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Unformatted text preview: 16.412/6.834J Cognitive Robotics February 7 th , 2005 Probabilistic Methods for Kinodynamic Path Planning Based on Past Student Lectures by: Lecturer: Paul Elliott, Aisha Walcott, Nathan Ickes and Stanislav Funiak Prof. Brian C. Williams How do we maneuver or manipulate? courtesy NASA JSC courtesy NASA Ames Outline Roadmap path planning Probabilistic roadmaps Planning in the real world Planning amidst moving obstacles RRT-based planners Conclusions Outline Roadmap path planning Probabilistic roadmaps Planning in the real world Planning amidst moving obstacles RRT-based planners Conclusions Brian Williams, Fall 03 Path Planning through Obstacles Start position Goal Brian Williams, Fall 03 position 1. Create Configuration Space Assume : Vehicle Start position translates, but no rotation Idea: Transform to equivalent Goal position problem of navigating a point. Brian Williams, Fall 03 2. Map From Continuous Problem to a Roadmap: Create Visibility Graph Start position Goal Brian Williams, Fall 03 position 2. Map From Continuous Problem to a Roadmap: Create Visibility Graph Start position Goal Brian Williams, Fall 03 position 3. Plan Shortest Path Start position Goal Brian Williams, Fall 03 position Resulting Solution Start position Goal Brian Williams, Fall 03 position A Visibility Graph is One Kind of Roadmap Start position What are some other types of roadmaps? Goal Brian Williams, Fall 03 position Roadmaps: Voronoi Diagrams Brian Williams, Fall 03 Lines equidistant from CSpace obstacles Roadmaps: Approximate Fixed Cell Brian Williams, Fall 03 Roadmaps: Approximate Fixed Cell Brian Williams, Fall 03 Roadmaps: Exact Cell Decomposition Brian Williams, Fall 03 Khatib 1986 Potential Functions Latombe 1991 Koditschek 1998 Attractive Potential Repulsive Potential Combined Potential for goals for obstacles Field Move along force: F(x) = U att (x)- U rep (x) Brian Williams, Fall 03 Exploring Roadmaps Shortest path Dijkstras algorithm Bellman-Ford algorithm Floyd-Warshall algorithm Johnsons algorithm Informed search Uniform cost search Greedy search A* search Beam search Hill climbing Brian Williams, Fall 03 Robonaut Teamwork: Tele-robotic High dimensional state space Controllability and dynamics Safety and compliance Brian Williams, Fall 03 Outline Roadmap path planning Probabilistic roadmaps Planning in the real world Planning amidst moving obstacles RRT-based planners Conclusions Applicability of Lazy Probabilistic Road Maps to Portable Satellite Assistant By Paul Elliott courtesy NASA Ames Zvezda Service Module Idea: Probabilistic Roadmaps Search randomly generated roadmap Probabilistically complete Trim infeasible edges and nodes lazily Place Start and Goal Goal Start Place Nodes Randomly Select a Set of Neighbors A* Search 16 18 18 2 1 2...
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This note was uploaded on 11/07/2011 for the course AERO 16.410 taught by Professor Brianwilliams during the Fall '05 term at MIT.

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l2_pro_path_plan - 16.412/6.834J Cognitive Robotics...

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