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A simple presentation of a the Minkowski Sum applied to 2D geometry.

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- 1. The Minkowski sum (applied to 2d geometry) cloderic.mars@gmail.com http://www.crowdscontrol.net clodericmars
- 2. Formal deﬁnition A and B are two sets A⊕B is the Minkowski sum of A and B A⊕B = {a+b! a∈A, b∈B}
- 3. What if A and B are 2D shapes ? Hard to visualize ? Let’s see some examples...
- 4. Example 1 A is any polygon B is a convex polygon
- 5. A B x y
- 6. A⊕B x y
- 7. Example 2 A is any polygon B is any disc
- 8. A B x y
- 9. A⊕B x y
- 10. Intuitive deﬁnition What is A⊕B ? Take B Dip it into some paint Put its (0,0) on A border Translate it along the A perimeter The painted area is A⊕B
- 11. What can you do with that ? Notably, motion planning
- 12. Free space A is an obstacle any 2D polygon B is a moving object 2D translation : t shape : a convex polygon or a disc t ∈ A⊕-B collision
- 13. Example 1 A is any polygon B is a convex polygon
- 14. A B x y -B
- 15. A⊕-B x y
- 16. x y t t ∉ A⊕-B no collision
- 17. x y t t ∈ A⊕-B collision
- 18. Example 2 A is any polygon B is any disc
- 19. A B=-B x y
- 20. A⊕-B x y
- 21. A⊕-B x y t t ∉ A⊕-B no collision
- 22. A⊕-B x y t t ∈ A⊕-B collision
- 23. How is it computed ?
- 24. Two convex polygons ConvexPolygon minkowskiSum(ConvexPolygon a, ConvexPolygon b) { Vertex[] computedVertices; foreach(Vertex vA in a) { foreach(Vertex vB in b) { computedVertices.push_back(vA+vB); } } return convexHull(computedVertices); }
- 25. Any polygons Method 1 : decomposition decompose in convex polygons compute the sum of each couple the ﬁnal sum is the union of each sub-sum Method 2 : convolution cf. sources
- 26. Polygon offsetting P is a polygon D is a disc of radius r Computing P⊕D = Offsetting P by a radius r Computation Easy for a convex polygon cf. sources
- 27. Sources http://www.cgal.org/Manual/3.4/doc_html/ cgal_manual/Minkowski_sum_2/Chapter_main.html http://wapedia.mobi/en/Minkowski_addition

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