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Interactive Navigation in Complex Environments Using Path Planning Salomon et al.(2003) University of North Carolina Prepared By Xiaoshan Pan
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Content n 1 st section (3/4) – Pre-compute a global roadmap – Graph search (ini goal) in real-time – Display motion n 2 nd section (1/4) – User-steered exploration
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Basic Idea Preprocessing phase Runtime algorithm
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Precomputation: Sampling Gravity Shooting rays Random Rays
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Precomputation: Sampling Gravity Shooting rays өө Walkable surface Construct roadmap -Max # of samples -Min dist between samples
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Connectors - Rc > Rg Rc Guards & Connectors (C-space) Reachability (vs. visibility) RgRg Guards - guards can’t see each other
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- yes reject c, goto while c - no! c becomes a Guard, connect to connectors (if any), goto while Algorithm (build_roadmap) While (map_coverage < P_cover), do// map_coverage = guards_reachable/entire_space Return roadmap Connector Guard Connector Guard Connector Guard 2. Can c be a Connector? See any Guards in Rc? - Yes then connect, goto while (else goto 3) 1. Pick a random config. c c 3. Can c be a Guard? See any Guards in Rg? c Be a ConnectorBe a GuardBe rejected
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Search for a path: init goal n Initial position (Rc radius) ini goal n Goal position n Graph search…
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Display Motion: Smooth Path n Walk along the path ini goal n Smoothing path (cutting redundant corners while walking)
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User-steered exploration (local walk) n User has control – A directional vector n Robot always stays on a walkable surface – In free space – Surface within a tolerance angle – Steps ok, cliffs NO!! n Robot do not penetrate objects
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Local Walk Algorithm n Follow the directional vector, if - Goal is reached, stop - Collision, project along obstacle edge - New surface, step up/down (not a cliff!) - Edge, step up/down or project along the edge
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Discussion n Can deal with complex environment – Because it pre-computes a global roadmap. n Still… – Pre-computation could be time consuming. – Walking along line segments does not look natural. n Overall assessment: Pretty good
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