3 voronoi graph is fragile voronoi diagram for a

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3. Voronoi graph is fragile. Voronoi Diagram: For a given set of objects called sites, each Voronoi cell defines the set of points in the plane that are closer to each site than to any other site. The outlines of all Voronoi cells form the Voronoi Diagram. The Voronoi path will have the equal distance to obstacles. Potential Field Path Planning: 1. Provide a simple control function to move robot: gradient descent 2. Can be trapped in local minimum 3. Can be used as online, local method. Robot will be attracted to the goal but be repelled by obstacles. Use the gradient descent algorithm to find out the minimum value. However, it will not guarantee a path the goal, because the robot might be trapped in local minimum value. Geometry based path planning: Voronoi, Vgraph
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Sampling based path palnning: Probabilistic Roadmap Planner – PRM It is a multi-query planner. There is no guarantee that this algorithm will provide fully connected graph. Sol: use denser sampling Obstacle-Based PRM: free space do not need to many samples, collision area need denser samples. Use K-D tree to find out the K nearest neighbor. Local Planner: try to connect nearest neighbor Distance function: how to compare two points in C space is close or not. Rapidly-Exploring Random Trees
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Single Query planner to get from config A to config B The nearest tree node q is the node that closest to to q_rand. The path will not be optimal, every time you run the method will give you different path. RRT optimization: Try to connect non-adjacent configurations. A* Search and Dijkstra Search A* search: Heuristic has to be less than the true cost of path from current node to goal.
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  • Fall '14
  • Midterm, Nearest neighbor search, Voronoi diagram

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