l13b_incr_pat_pln

l13b_incr_pat_pln - Outline Incremental Path Planning...

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Incremental Path Planning Continuous Planning and Dynamic A* Prof. Brian Williams (help from Ihsiang Shu) 16.412/6.834 Cognitive Robotics March 16 th , 2004 Outline Optimal Path Planning in Partially Known Environments. Continuous Optimal Path Planning ± Dynamic A* ± Incremental A* (LRTA*) [Appendix] [ 1. Generate global path plan from initial map. 2. Repeat until goal reached or failure: ± Execute next step in current global path plan ± Update map based on sensors. ± If map changed generate new global path from map. Compute Optimal Path F C A S K H D B G L I E O N M J Zellinsky, 92] Begin Executing Optimal Path F C A S K H D B G L I E O N M J h = 5 h = 4 h = 3 h = 4 h = 4 h = 3 h = 2 h = 3 h = 3 h = 2 h = 1 h = 2 h = 2 h = 1 h = 0 h = 1 Uses sensors to detect discrepancies along way. Obstacle Encountered! F C A S K H D B G L I E O N M J h = 5 h = 4 h = 3 h = 4 h = 4 h = 3 h = 2 h = 3 h = 3 h = 2 h = 1 h = 1 h = 2 h = 1 h = 0 h = 1 Robot moves along backpointers towards goal. At state A, robot discovers edge from D to H is blocked (cost 5,000 units). Update map and reinvoke planner. 1
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F C A S K H D B G L I E O N M J h = 5 h = 4 h = 3 h = 4 h = 4 h = 3 h = 2 h = 3 h = 3 h = 2 h = 1 h = 1 h = 2 h = 1 h = 0 h = 1 A’s previous path is still optimal. Continue moving robot along back pointers. F C A S K H D B G L I E O N M J h = 5 h = 4 h = 3 h = 4 h = 4 h = 3 h = 2 h = 3 h = 3 h = 2 h = 1 h = 1 h = 2 h = 1 h = 0 h = 1 Second Obstacle, Replan! At C robot discovers blocked edges from C to F and H (cost 5,000 units). Update map and reinvoke planner. h = 5 Path Execution Achieves Goal F C A S K H D B G L I E O N M J h = 5 h = 4 h = 3 h = 4 h = 4 h = 3 h = 2 h = 3 h = 2 h = 1 h = 1 h = 2 h = 1 h = 0 h = 1 Follow back pointers to goal. No further discrepancies detected; goal achieved! Outline Optimal Path Planning in Partially Known Environments. Continuous Optimal Path Planning ± Dynamic A* ± Incremental A* (LRTA*) [Appendix] What is Continuous Optimal Path Planning? Supports search as a repetitive online process. Exploits similarities between a series of searches to solve much faster than solving each search starting from scratch. Reuses the identical parts of the previous search tree, while updating differences. Solutions guaranteed to be optimal. On the first search, behaves like traditional algorithms. ± ± Incremental A* A* behaves exactly like A*. [ 1. Generate global path plan from initial map. 2. Repeat until Goal reached, or failure. ± Execute next step of current global path plan. ± Update map based on sensor information. ± ² 1 to 3 orders of magnitude speedup D* behaves exactly like Dijkstra’s . Dynamic A* (aka D*) Stenz, 94] Incrementally update global path plan from map changes . relative to a non-incremental path planner.
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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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l13b_incr_pat_pln - Outline Incremental Path Planning...

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