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Randomized Planning for Short Inspection Paths
Tim Danner Lydia E. Kavraki
Department of Computer Science
Rice University
Outline• Introduction• Art Gallery and Watchman Route Problems• Adding Realism• 2-D Algorithm
– Selecting Guards– Connecting Guards– Results
• 3-D Algorithm– Differences– Preliminary Results
• Future Work
IntroductionProblem Definition
• Robot with vision capabilities
• Workspace W
• Path p
• Boundary dW W
dW
p
IntroductionUses
• Autonomous inspection of spacecraft exterior
• Flying camera building inspection
• Virtual reality architecture walkthrough
Art Gallery Problem
• Find minimal number of positions for guards to stand so that every point in a gallery is “visible” to at least one guard
• “Visible” is defined as the line of sight between the guard and the point lies entirely in W
Watchman Route Problem
• For workspace W, find the shortest path p in W such that every point on the boundary dW can be seen by a point on path p
Adding Realism
• “Straight-line” visibility is not very realistic for real sensors
• Length of line of sight must have a maximum• Angle of incidence of line of sight must have a
maximum – 60 degrees is a typical value• System is adaptable for different sensors• 2-D vs 3-D
2-D Algorithm
• Sensing with real sensors is time consuming
• Two parts to the algorithm:– Solve “art gallery problem” to find locations for
sensing locations - “guards”– Connect the guards with a short path, the
“watchman route”
2-D AlgorithmSelecting the Guards
• True minimal set of guards is an NP-hard problem
• Randomized planner is used in this case
• Uses Gonzalez-Banos and Latombe’s randomized, incremental algorithm
2-D AlgorithmSelecting the Guards
• Main structure is a loop• At each iteration, a point x on the border
dW of W that is not yet guarded is chosen randomly
• Construct region which can see x (same as region which x can see)
• Apply two range constraints: limited length line of sight and angle of incidence
2-D AlgorithmSelecting the Guards
• Sample region k times, evaluating each point as a possible new guard
• Sample which can cover the largest portion of the new length of border is chosen as the new guard and guarded border is updated
• Loop repeats until the entire border is guarded
2-D AlgorithmSelecting the Guards
• One problematic case is sharp interior angles
• A “disproportionately large” number of guards may be needed and hard to place
• Incremental loop can be terminated
2-D AlgorithmConnecting the Guards
• Basically, find an order to connect guards out of a possible n! orders
• Connect guards using a graph algorithm
• In this manner, the problem becomes a “traveling salesman problem”
2-D AlgorithmConnecting the Guards
• Actually use an approximation to the TSP – pre-order walk of a Minimum Spanning Tree
• Applies in cases where the triangle inequality holds, which is the case for graphs in R2 and R3 (which our graph of guards is)
• Path length is bounded by 2X actual TSP for complete graphs
• If workspace is connected, then the graph is complete (for an inspection path to exist, the space must be connected)
2-D AlgorithmConnecting the Guards
• Shortest Paths Graph (SPG)– One node for each guard– One edge for each pair of guards– Weight is assigned to each edge (i,j) that is
equal to the shortest collision-free path from point i to j
– May be straight line or more complex
2-D AlgorithmConnecting the Guards
• Shortest path between two points is done by constructing and searching another graph, the workspace-guard roadmap
• Workspace-guard roadmap has one node for each vertex on dW and one node for each guard
• Has an edge between a pair of nodes i and j if and only if it is collision-free
2-D AlgorithmConnecting the Guards
• Complete graph has n2 edges, but we can use a shortcut
• Only connect close points, by dividing workspace into rectangular grid
• About 10 nodes per cell
• Connection is made with a moving 3X3 window
2-D AlgorithmNote
• The default is to inspect the interior
• To inspect an exterior, surround entire workspace with a rectangle and mark it guarded
W
W
2-D AlgorithmExperimental Results
• Most computing time spent creating visibility polygons
Guard GuardSelection Connection
Figure # Edges Time (s) Time (s)1 9 0.55 0.072 36 4.74 1.023 1026 729.79 328.13
Figure 1 Figure 2
Figure 3
3-D Algorithm
• Necessary for real workspaces
• Algorithm is very similar
• Difficulty – computing visibility polyhedrons in 3-D instead of visibility polygons in 2-D
3-D AlgorithmSelecting the Guards
• Visual constraints remain simple
• Two steps will require visibility volumes– Determining a sampling region– Determining what surfaces a sampled point can
see
• However, explicitly computing visibility polyhedron can be avoided
3-D AlgorithmSelecting the Guards
• Determining sampling region utilizes constraint sphere and cone– Compute the intersection of these– Randomly sample this region and test if point is
valid
• Both of these are much easier than computing a visibility polyhedron
3-D AlgorithmSelecting the Guards
• Once points are sampled, need to:– Determine what surfaces can be seen by them– Subtract already guarded surfaces
• Use a front to back checking method, clipping each additional surface with the previous ones
3-D AlgorithmSelecting the Guards
• Complications– Need a way of defining order– Resolving circular problems– Selecting adequate data structure
• Binary Space Partitioning Tree for defining front to back order
3-D AlgorithmConnecting the Guards
• No analog to creating the optimal shortest paths as in 2-D
• Shortest path is most likely not around vertices• Instead of augmenting guard roadmap with
workspace vertices, random planner is used
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