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Planning Among Movable Obstacles with Artificial Constraints. by: Mike Stilman and James Kuffner. Presented by: Deborah Meduna and Michael Vitus. Motivation. http://www.cs.cmu.edu/~mstilman/mov/WAFR06-Planning.avi. Outline. Problem Definition Main Topics TRANSIT TRANSFER
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Planning Among Movable Obstacles with Artificial Constraints by: Mike Stilman and James Kuffner Presented by: Deborah Meduna and Michael Vitus
Motivation http://www.cs.cmu.edu/~mstilman/mov/WAFR06-Planning.avi
Outline • Problem Definition • Main Topics • TRANSIT • TRANSFER • Algorithm Overview • Artificial Constraints • Obstacle Identification • Constraint Resolution • Results
Problem Definition • Separate configuration spaces for different obstacle locations • Obstacle movement restricted to robot motion • Motion planning restricted to the robot subspace
TRANSIT • Moves the robot along a path while obstacles remain fixed • Valid if and only if path is in the robot’s collision-free space
TRANSFER • Moves the robot and ONE obstacle along a path to a new state • Valid if and only if: • The robot and obstacle paths are in collision-free space • The robot and obstacle do not collide along the path
Problem Scope • Monotone vs. Non-monotone • Monotone: each movable obstacle only needs to be moved once • Non-monotone: can be broken into multiple monotone plans • Presented Planner is Linear-Monotone
Algorithm Overview (1) • Plan a path through the cluttered environment • Allow translation through movable obstacles • Determine the last obstacle that has to be moved
Algorithm Overview (2) • Plan a path to Transfer the last obstacle and Transit the robot to the goal • Adds artificial constraints for earlier timesteps • Resolve conflicts between movable obstacles and artificial constraints
Artificial Constraints • Robot Transit operations create constraints on all obstacle configurations: • Robot motion along path sweeps volume V
Artificial Constraints • Robot Transfer operations create constraints on non-moving obstacle configurations
Reverse Search - Motivation • Assembly planning: • Much smaller branching factor due to actual constraints • Movable obstacles: • Final configuration not pre-determined • Must use forward search • Use reverse search for the ordering of which obstacles to move • Transfer of the last obstacle is performed first • Adds artificial constraints
Obstacle Identification • Identifies last obstacle to be manipulated prior to reaching the goal or sub-goal • Utilize relaxed planner, Plast, allowing paths through movable obstacles • Select OL, last obstacle in collision with path
Constraint Resolution • Plans a Transfer path for OL and the following Transit path to the goal • The two paths form artificial constraints • No obstacles scheduled earlier in time than OL can be within the two swept volumes
Example • Show movie from CMU http://www.cs.cmu.edu/~mstilman/mov/WAFR06-Execution.avi
Results • Figures 1, 2 and 4 not solve-able by existing planners 1 4 2 3
Conclusions • Future Work: • Include accessibility constraints • Incorporate heuristics for generating Transfer paths