4/21/15CMPS 3130/6130 Computational Geometry1 CMPS 3130/6130 Computational Geometry Spring 2015...
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Transcript of 4/21/15CMPS 3130/6130 Computational Geometry1 CMPS 3130/6130 Computational Geometry Spring 2015...
![Page 1: 4/21/15CMPS 3130/6130 Computational Geometry1 CMPS 3130/6130 Computational Geometry Spring 2015 Motion Planning Carola Wenk.](https://reader036.fdocuments.in/reader036/viewer/2022082405/56649e445503460f94b38237/html5/thumbnails/1.jpg)
4/21/15 CMPS 3130/6130 Computational Geometry 1
CMPS 3130/6130 Computational GeometrySpring 2015
Motion PlanningCarola Wenk
![Page 2: 4/21/15CMPS 3130/6130 Computational Geometry1 CMPS 3130/6130 Computational Geometry Spring 2015 Motion Planning Carola Wenk.](https://reader036.fdocuments.in/reader036/viewer/2022082405/56649e445503460f94b38237/html5/thumbnails/2.jpg)
Robot motion planning• Given: A floor plan (2d polygonal region
with obstacles), and a robot (2D simple polygon)
• Task: Find a collision-free path from start to end
• Robot R is a simple polygon; R=R(0,0)• Let R(x,y) = R+
R translated by
• Add rotations: R(x,y,)
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Configuration space• # parameters = degrees of freedom (DOF)• Parameter space = “Configuration space” C:
– 2D translating: configuration space is C = R2
– 2D translating and rotating: configuration space is C = R2 x [0,2)
• Obstacle P. Its corresponding C-obstacle P’={C | R(• Free space: Placements (= subset of configuration space) where robot does not
intersect any obstacle.
P’
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Translating a point robot• Work space = configuration space• Compute trapezoidal map of disjoint polygonal obstacles in
O(n log n) expected time (where n = total # edges), including point location data structure
• Construct road map in trapezoidal map:– One vertex on each vertical edge– One vertex in center of each trapezoid– Edges between center-vertex and
edge-vertex of same trapezoid– O(n) time and space
4/21/15 CMPS 3130/6130 Computational Geometry 4
![Page 5: 4/21/15CMPS 3130/6130 Computational Geometry1 CMPS 3130/6130 Computational Geometry Spring 2015 Motion Planning Carola Wenk.](https://reader036.fdocuments.in/reader036/viewer/2022082405/56649e445503460f94b38237/html5/thumbnails/5.jpg)
Translating a point robot• Work space = configuration space• Compute trapezoidal map of disjoint polygonal obstacles in
O(n log n) expected time (where n = total # edges), including point location data structure
• Construct road map in trapezoidal map:– One vertex on each vertical edge– One vertex in center of each trapezoid– Edges between center-vertex and
edge-vertex of same trapezoid– O(n) time and space
• Compute path:– Locate trapezoids containing start and end– Traverse this road map using DFS or BFS to
find path from start to end
Theorem: One can preprocess a set of obstacles (with n = total # edges) in O(n log n) expected time, such that for any (start/end) query a collision-free path can be computed in O(n) time. 4/21/15 CMPS 3130/6130 Computational Geometry 5
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Minkowski sums
P’
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Extreme points
4/21/15 CMPS 3130/6130 Computational Geometry 7
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Configuration space with translations and rotations
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Shortest path for robot
Pull rubber band tight:
4/21/15 CMPS 3130/6130 Computational Geometry 9
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Visibility graph
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