Seminar. Path planning using Voronoi diagrams and B-Splines. Stefano Martina
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1 Seminar Path planning using Voronoi diagrams and B-Splines Stefano Martina 23 may 2016 This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
2 Voronoi diagrams Input: A set of points in plane (or space) called sites Output: A partition of the plane (or space) such that each point of a region is nearer to a certain site respect to the others Martina Stefano (Uni. Firenze) Seminar 23 may / 21
3 Voronoi diagrams Input: A set of points in plane (or space) called sites Output: A partition of the plane (or space) such that each point of a region is nearer to a certain site respect to the others Martina Stefano (Uni. Firenze) Seminar 23 may / 21
4 B-spline B-Splines - example parametric curves follow the shape of a control poligon can interpolate the extremes of the control polygon Martina Stefano (Uni. Firenze) Seminar 23 may / 21
5 B-spline B-Splines - example parametric curves follow the shape of a control poligon can interpolate the extremes of the control polygon Martina Stefano (Uni. Firenze) Seminar 23 may / 21
6 B-spline B-Splines - example parametric curves follow the shape of a control poligon can interpolate the extremes of the control polygon Martina Stefano (Uni. Firenze) Seminar 23 may / 21
7 B-spline B-Splines - example parametric curves follow the shape of a control poligon can interpolate the extremes of the control polygon Martina Stefano (Uni. Firenze) Seminar 23 may / 21
8 B-splines details Order k (= degree + 1) Extended partition (of parametric space [a, b]) T = {t 0,..., t k 2, t k 1,..., t n+1, t n+2,..., t n+k } t 0 t k 2 t k 1 ( a) < < t n+1 ( b) t n+2 t n+k n + 1 basis N i,1 (t) = { 1, if t i t < t i+1 0, otherwise N i,k (t) = ω i,k 1 (t) N i,k 1 (t) + (1 ω i+1,k 1 (t)) N i+1,k 1 (t) ω i,k (t) = t t i t i+k t i. B-spline n S(t) = v i N i,k (t), i=0 Martina Stefano (Uni. Firenze) Seminar 23 may / 21
9 B-splines details Order k (= degree + 1) Extended partition (of parametric space [a, b]) T = {t 0,..., t k 2, t k 1,..., t n+1, t n+2,..., t n+k } t 0 t k 2 t k 1 ( a) < < t n+1 ( b) t n+2 t n+k n + 1 basis N i,1 (t) = { 1, if t i t < t i+1 0, otherwise N i,k (t) = ω i,k 1 (t) N i,k 1 (t) + (1 ω i+1,k 1 (t)) N i+1,k 1 (t) ω i,k (t) = t t i t i+k t i. B-spline n S(t) = v i N i,k (t), i=0 Martina Stefano (Uni. Firenze) Seminar 23 may / 21
10 B-splines details Order k (= degree + 1) Extended partition (of parametric space [a, b]) T = {t 0,..., t k 2, t k 1,..., t n+1, t n+2,..., t n+k } t 0 t k 2 t k 1 ( a) < < t n+1 ( b) t n+2 t n+k n + 1 basis N i,1 (t) = { 1, if t i t < t i+1 0, otherwise N i,k (t) = ω i,k 1 (t) N i,k 1 (t) + (1 ω i+1,k 1 (t)) N i+1,k 1 (t) ω i,k (t) = t t i t i+k t i. B-spline n S(t) = v i N i,k (t), i=0 Martina Stefano (Uni. Firenze) Seminar 23 may / 21
11 B-splines details Order k (= degree + 1) Extended partition (of parametric space [a, b]) T = {t 0,..., t k 2, t k 1,..., t n+1, t n+2,..., t n+k } t 0 t k 2 t k 1 ( a) < < t n+1 ( b) t n+2 t n+k n + 1 basis N i,1 (t) = { 1, if t i t < t i+1 0, otherwise N i,k (t) = ω i,k 1 (t) N i,k 1 (t) + (1 ω i+1,k 1 (t)) N i+1,k 1 (t) ω i,k (t) = t t i t i+k t i. B-spline n S(t) = v i N i,k (t), i=0 Martina Stefano (Uni. Firenze) Seminar 23 may / 21
12 Useful properties of B-spline curves Clamped if t 0 = = t k 1 and t n+1 = = t n+k Continuity C k 2 between polynomials (or C m 1 ) Contained inside the union of convex hulls composed of consecutive k vertexes of control polygon Touch to the segment between k 1 aligned control vertexes Lay down in the segment between k aligned control vertexes Martina Stefano (Uni. Firenze) Seminar 23 may / 21
13 Useful properties of B-spline curves Clamped if t 0 = = t k 1 and t n+1 = = t n+k Continuity C k 2 between polynomials (or C m 1 ) Contained inside the union of convex hulls composed of consecutive k vertexes of control polygon Touch to the segment between k 1 aligned control vertexes Lay down in the segment between k aligned control vertexes Martina Stefano (Uni. Firenze) Seminar 23 may / 21
14 Useful properties of B-spline curves Clamped if t 0 = = t k 1 and t n+1 = = t n+k Continuity C k 2 between polynomials (or C m 1 ) Contained inside the union of convex hulls composed of consecutive k vertexes of control polygon Touch to the segment between k 1 aligned control vertexes Lay down in the segment between k aligned control vertexes Martina Stefano (Uni. Firenze) Seminar 23 may / 21
15 Useful properties of B-spline curves Clamped if t 0 = = t k 1 and t n+1 = = t n+k Continuity C k 2 between polynomials (or C m 1 ) Contained inside the union of convex hulls composed of consecutive k vertexes of control polygon Touch to the segment between k 1 aligned control vertexes Lay down in the segment between k aligned control vertexes Martina Stefano (Uni. Firenze) Seminar 23 may / 21
16 Useful properties of B-spline curves Clamped if t 0 = = t k 1 and t n+1 = = t n+k Continuity C k 2 between polynomials (or C m 1 ) Contained inside the union of convex hulls composed of consecutive k vertexes of control polygon Touch to the segment between k 1 aligned control vertexes Lay down in the segment between k aligned control vertexes Martina Stefano (Uni. Firenze) Seminar 23 may / 21
17 Dijkstra algorithm 1 def dijkstra ( graph, start, end ): 2 path = [] 3 Q = priorityqueue. PQueue () 4 dist = {} 5 prev = {} 6 for node in graph. nodes (): # populate the queue 7 if node!= start : 8 dist [ node ] = inf 9 Q. add (node, inf ) 10 else : 11 dist [ node ] = 0 12 Q. add (node, 0) 13 while True : # main loop 14 u = Q. pop () # take nearest node and remove from queue 15 if u == end or dist [u] == inf : # finished (good or bad ) 16 break 17 # all neighbors still in queue 18 for v in Q. filterget ( lambda node : node in graph. neighbors (u)): 19 tmpdist = dist [u] + graph [u][v][ weight ] 20 if tmpdist < dist [v]: #if distance shorter update values 21 dist [v] = tmpdist 22 prev [v] = u 23 Q.add (v, tmpdist ) # update distance also in queue 24 u = end 25 while u in prev : # backward recreation of path 26 u = prev [u] 27 path [:0] = [u] 28 if path : 29 path [ len ( path ):] = [ end ] 30 path [:0] = [ start ] 31 return path Martina Stefano (Uni. Firenze) Seminar 23 may / 21
18 Background Main problem Path planning from a start point to an end point in 3D space with obstacles using Voronoi diagrams. 1. Distribute points in the surfaces of obstacles and optionally in the surface of bounding box 2. Build Voronoi diagram using those points as source 3. Transform the Voronoi diagram in a graph cells vertexes as nodes cells edges as arcs (infinite edges ignored) 4. Prune the arcs that crosses an obstacle s surface 5. Attach the start and end points to the graph as nodes 6. Calculate the shortest path from start node to end node using Dijkstra s algorithm. Martina Stefano (Uni. Firenze) Seminar 23 may / 21
19 Background Main problem Path planning from a start point to an end point in 3D space with obstacles using Voronoi diagrams. 1. Distribute points in the surfaces of obstacles and optionally in the surface of bounding box 2. Build Voronoi diagram using those points as source 3. Transform the Voronoi diagram in a graph cells vertexes as nodes cells edges as arcs (infinite edges ignored) 4. Prune the arcs that crosses an obstacle s surface 5. Attach the start and end points to the graph as nodes 6. Calculate the shortest path from start node to end node using Dijkstra s algorithm. Martina Stefano (Uni. Firenze) Seminar 23 may / 21
20 Background Main problem Path planning from a start point to an end point in 3D space with obstacles using Voronoi diagrams. 1. Distribute points in the surfaces of obstacles and optionally in the surface of bounding box 2. Build Voronoi diagram using those points as source 3. Transform the Voronoi diagram in a graph cells vertexes as nodes cells edges as arcs (infinite edges ignored) 4. Prune the arcs that crosses an obstacle s surface 5. Attach the start and end points to the graph as nodes 6. Calculate the shortest path from start node to end node using Dijkstra s algorithm. Martina Stefano (Uni. Firenze) Seminar 23 may / 21
21 Background Main problem Path planning from a start point to an end point in 3D space with obstacles using Voronoi diagrams. 1. Distribute points in the surfaces of obstacles and optionally in the surface of bounding box 2. Build Voronoi diagram using those points as source 3. Transform the Voronoi diagram in a graph cells vertexes as nodes cells edges as arcs (infinite edges ignored) 4. Prune the arcs that crosses an obstacle s surface 5. Attach the start and end points to the graph as nodes 6. Calculate the shortest path from start node to end node using Dijkstra s algorithm. Martina Stefano (Uni. Firenze) Seminar 23 may / 21
22 Background Main problem Path planning from a start point to an end point in 3D space with obstacles using Voronoi diagrams. 1. Distribute points in the surfaces of obstacles and optionally in the surface of bounding box 2. Build Voronoi diagram using those points as source 3. Transform the Voronoi diagram in a graph cells vertexes as nodes cells edges as arcs (infinite edges ignored) 4. Prune the arcs that crosses an obstacle s surface 5. Attach the start and end points to the graph as nodes 6. Calculate the shortest path from start node to end node using Dijkstra s algorithm. Martina Stefano (Uni. Firenze) Seminar 23 may / 21
23 Background Main problem Path planning from a start point to an end point in 3D space with obstacles using Voronoi diagrams. 1. Distribute points in the surfaces of obstacles and optionally in the surface of bounding box 2. Build Voronoi diagram using those points as source 3. Transform the Voronoi diagram in a graph cells vertexes as nodes cells edges as arcs (infinite edges ignored) 4. Prune the arcs that crosses an obstacle s surface 5. Attach the start and end points to the graph as nodes 6. Calculate the shortest path from start node to end node using Dijkstra s algorithm. Martina Stefano (Uni. Firenze) Seminar 23 may / 21
24 Background Main problem Path planning from a start point to an end point in 3D space with obstacles using Voronoi diagrams. 1. Distribute points in the surfaces of obstacles and optionally in the surface of bounding box 2. Build Voronoi diagram using those points as source 3. Transform the Voronoi diagram in a graph cells vertexes as nodes cells edges as arcs (infinite edges ignored) 4. Prune the arcs that crosses an obstacle s surface 5. Attach the start and end points to the graph as nodes 6. Calculate the shortest path from start node to end node using Dijkstra s algorithm. Martina Stefano (Uni. Firenze) Seminar 23 may / 21
25 Example x y z Martina Stefano (Uni. Firenze) Seminar 23 may / 21
26 Example x y z Martina Stefano (Uni. Firenze) Seminar 23 may / 21
27 Example x y z Martina Stefano (Uni. Firenze) Seminar 23 may / 21
28 Example x y z Martina Stefano (Uni. Firenze) Seminar 23 may / 21
29 Example x y z Martina Stefano (Uni. Firenze) Seminar 23 may / 21
30 Improvement Idea Make a smoother curve instead of finding the polygonal chain of the shortest path in the structure we can use a B-Spline that interpolate the start and end vertexes use the shortest path found with Dijkstra as control polygon Martina Stefano (Uni. Firenze) Seminar 23 may / 21
31 Improvement Idea Make a smoother curve instead of finding the polygonal chain of the shortest path in the structure we can use a B-Spline that interpolate the start and end vertexes use the shortest path found with Dijkstra as control polygon Martina Stefano (Uni. Firenze) Seminar 23 may / 21
32 Improvement Idea Make a smoother curve instead of finding the polygonal chain of the shortest path in the structure we can use a B-Spline that interpolate the start and end vertexes use the shortest path found with Dijkstra as control polygon Martina Stefano (Uni. Firenze) Seminar 23 may / 21
33 Improvement Idea Make a smoother curve instead of finding the polygonal chain of the shortest path in the structure we can use a B-Spline that interpolate the start and end vertexes use the shortest path found with Dijkstra as control polygon Martina Stefano (Uni. Firenze) Seminar 23 may / 21
34 Problem The control polygon is free from obstacles by construction (the graph is pruned of the arcs that cross an obstacle s surface) But the curve is not guaranteed to be free from obstacles Martina Stefano (Uni. Firenze) Seminar 23 may / 21
35 Problem The control polygon is free from obstacles by construction (the graph is pruned of the arcs that cross an obstacle s surface) But the curve is not guaranteed to be free from obstacles Martina Stefano (Uni. Firenze) Seminar 23 may / 21
36 Problem The control polygon is free from obstacles by construction (the graph is pruned of the arcs that cross an obstacle s surface) But the curve is not guaranteed to be free from obstacles Martina Stefano (Uni. Firenze) Seminar 23 may / 21
37 Problem The control polygon is free from obstacles by construction (the graph is pruned of the arcs that cross an obstacle s surface) But the curve is not guaranteed to be free from obstacles Martina Stefano (Uni. Firenze) Seminar 23 may / 21
38 Solution A B-Spline of order k is contained inside the union of convex hulls composed of consecutive k vertexes of control polygon Idea we can use a quadratic B-Spline (grade 2, order 3) to smooth the path and keep triangles formed by three consecutive points free from obstacles Martina Stefano (Uni. Firenze) Seminar 23 may / 21
39 Solution A B-Spline of order k is contained inside the union of convex hulls composed of consecutive k vertexes of control polygon Idea we can use a quadratic B-Spline (grade 2, order 3) to smooth the path and keep triangles formed by three consecutive points free from obstacles Martina Stefano (Uni. Firenze) Seminar 23 may / 21
40 Solution A B-Spline of order k is contained inside the union of convex hulls composed of consecutive k vertexes of control polygon Idea we can use a quadratic B-Spline (grade 2, order 3) to smooth the path and keep triangles formed by three consecutive points free from obstacles Martina Stefano (Uni. Firenze) Seminar 23 may / 21
41 Solution A B-Spline of order k is contained inside the union of convex hulls composed of consecutive k vertexes of control polygon Idea we can use a quadratic B-Spline (grade 2, order 3) to smooth the path and keep triangles formed by three consecutive points free from obstacles Martina Stefano (Uni. Firenze) Seminar 23 may / 21
42 First implementation A variation of Dijkstra algorithm is developed where: 1. create an ordered triple for each three consecutive nodes in the graph 2. check if the triangle corresponding to each triple intersect an obstacle 3. populate the priority queue with obstacle free triples the initial weight is 0 for triples where the first node is the start node is otherwise 4. pop the triple with lowest weight from the priority queue 5. update the weight and pointer to previous of all neighbouring triples a triple B is subsequent to a triple A if (A[2] = B[1]) (A[3] = B[2]) the weight of a neighbour is W (B) = W (A) + dist(a[1], A[2]) 6. repeat from point 4 until popped a special ending triple or a triple with weight 7. the shortest path (with free triangular convex hull) can be obtained following the previous pointer from the ending triple, and deconstructing the triples Martina Stefano (Uni. Firenze) Seminar 23 may / 21
43 First implementation A variation of Dijkstra algorithm is developed where: 1. create an ordered triple for each three consecutive nodes in the graph 2. check if the triangle corresponding to each triple intersect an obstacle 3. populate the priority queue with obstacle free triples the initial weight is 0 for triples where the first node is the start node is otherwise 4. pop the triple with lowest weight from the priority queue 5. update the weight and pointer to previous of all neighbouring triples a triple B is subsequent to a triple A if (A[2] = B[1]) (A[3] = B[2]) the weight of a neighbour is W (B) = W (A) + dist(a[1], A[2]) 6. repeat from point 4 until popped a special ending triple or a triple with weight 7. the shortest path (with free triangular convex hull) can be obtained following the previous pointer from the ending triple, and deconstructing the triples Martina Stefano (Uni. Firenze) Seminar 23 may / 21
44 First implementation A variation of Dijkstra algorithm is developed where: 1. create an ordered triple for each three consecutive nodes in the graph 2. check if the triangle corresponding to each triple intersect an obstacle 3. populate the priority queue with obstacle free triples the initial weight is 0 for triples where the first node is the start node is otherwise 4. pop the triple with lowest weight from the priority queue 5. update the weight and pointer to previous of all neighbouring triples a triple B is subsequent to a triple A if (A[2] = B[1]) (A[3] = B[2]) the weight of a neighbour is W (B) = W (A) + dist(a[1], A[2]) 6. repeat from point 4 until popped a special ending triple or a triple with weight 7. the shortest path (with free triangular convex hull) can be obtained following the previous pointer from the ending triple, and deconstructing the triples Martina Stefano (Uni. Firenze) Seminar 23 may / 21
45 First implementation A variation of Dijkstra algorithm is developed where: 1. create an ordered triple for each three consecutive nodes in the graph 2. check if the triangle corresponding to each triple intersect an obstacle 3. populate the priority queue with obstacle free triples the initial weight is 0 for triples where the first node is the start node is otherwise 4. pop the triple with lowest weight from the priority queue 5. update the weight and pointer to previous of all neighbouring triples a triple B is subsequent to a triple A if (A[2] = B[1]) (A[3] = B[2]) the weight of a neighbour is W (B) = W (A) + dist(a[1], A[2]) 6. repeat from point 4 until popped a special ending triple or a triple with weight 7. the shortest path (with free triangular convex hull) can be obtained following the previous pointer from the ending triple, and deconstructing the triples Martina Stefano (Uni. Firenze) Seminar 23 may / 21
46 First implementation A variation of Dijkstra algorithm is developed where: 1. create an ordered triple for each three consecutive nodes in the graph 2. check if the triangle corresponding to each triple intersect an obstacle 3. populate the priority queue with obstacle free triples the initial weight is 0 for triples where the first node is the start node is otherwise 4. pop the triple with lowest weight from the priority queue 5. update the weight and pointer to previous of all neighbouring triples a triple B is subsequent to a triple A if (A[2] = B[1]) (A[3] = B[2]) the weight of a neighbour is W (B) = W (A) + dist(a[1], A[2]) 6. repeat from point 4 until popped a special ending triple or a triple with weight 7. the shortest path (with free triangular convex hull) can be obtained following the previous pointer from the ending triple, and deconstructing the triples Martina Stefano (Uni. Firenze) Seminar 23 may / 21
47 First implementation A variation of Dijkstra algorithm is developed where: 1. create an ordered triple for each three consecutive nodes in the graph 2. check if the triangle corresponding to each triple intersect an obstacle 3. populate the priority queue with obstacle free triples the initial weight is 0 for triples where the first node is the start node is otherwise 4. pop the triple with lowest weight from the priority queue 5. update the weight and pointer to previous of all neighbouring triples a triple B is subsequent to a triple A if (A[2] = B[1]) (A[3] = B[2]) the weight of a neighbour is W (B) = W (A) + dist(a[1], A[2]) 6. repeat from point 4 until popped a special ending triple or a triple with weight 7. the shortest path (with free triangular convex hull) can be obtained following the previous pointer from the ending triple, and deconstructing the triples Martina Stefano (Uni. Firenze) Seminar 23 may / 21
48 First implementation A variation of Dijkstra algorithm is developed where: 1. create an ordered triple for each three consecutive nodes in the graph 2. check if the triangle corresponding to each triple intersect an obstacle 3. populate the priority queue with obstacle free triples the initial weight is 0 for triples where the first node is the start node is otherwise 4. pop the triple with lowest weight from the priority queue 5. update the weight and pointer to previous of all neighbouring triples a triple B is subsequent to a triple A if (A[2] = B[1]) (A[3] = B[2]) the weight of a neighbour is W (B) = W (A) + dist(a[1], A[2]) 6. repeat from point 4 until popped a special ending triple or a triple with weight 7. the shortest path (with free triangular convex hull) can be obtained following the previous pointer from the ending triple, and deconstructing the triples Martina Stefano (Uni. Firenze) Seminar 23 may / 21
49 First implementation A variation of Dijkstra algorithm is developed where: 1. create an ordered triple for each three consecutive nodes in the graph 2. check if the triangle corresponding to each triple intersect an obstacle 3. populate the priority queue with obstacle free triples the initial weight is 0 for triples where the first node is the start node is otherwise 4. pop the triple with lowest weight from the priority queue 5. update the weight and pointer to previous of all neighbouring triples a triple B is subsequent to a triple A if (A[2] = B[1]) (A[3] = B[2]) the weight of a neighbour is W (B) = W (A) + dist(a[1], A[2]) 6. repeat from point 4 until popped a special ending triple or a triple with weight 7. the shortest path (with free triangular convex hull) can be obtained following the previous pointer from the ending triple, and deconstructing the triples Martina Stefano (Uni. Firenze) Seminar 23 may / 21
50 First implementation A variation of Dijkstra algorithm is developed where: 1. create an ordered triple for each three consecutive nodes in the graph 2. check if the triangle corresponding to each triple intersect an obstacle 3. populate the priority queue with obstacle free triples the initial weight is 0 for triples where the first node is the start node is otherwise 4. pop the triple with lowest weight from the priority queue 5. update the weight and pointer to previous of all neighbouring triples a triple B is subsequent to a triple A if (A[2] = B[1]) (A[3] = B[2]) the weight of a neighbour is W (B) = W (A) + dist(a[1], A[2]) 6. repeat from point 4 until popped a special ending triple or a triple with weight 7. the shortest path (with free triangular convex hull) can be obtained following the previous pointer from the ending triple, and deconstructing the triples Martina Stefano (Uni. Firenze) Seminar 23 may / 21
51 First implementation A variation of Dijkstra algorithm is developed where: 1. create an ordered triple for each three consecutive nodes in the graph 2. check if the triangle corresponding to each triple intersect an obstacle 3. populate the priority queue with obstacle free triples the initial weight is 0 for triples where the first node is the start node is otherwise 4. pop the triple with lowest weight from the priority queue 5. update the weight and pointer to previous of all neighbouring triples a triple B is subsequent to a triple A if (A[2] = B[1]) (A[3] = B[2]) the weight of a neighbour is W (B) = W (A) + dist(a[1], A[2]) 6. repeat from point 4 until popped a special ending triple or a triple with weight 7. the shortest path (with free triangular convex hull) can be obtained following the previous pointer from the ending triple, and deconstructing the triples Martina Stefano (Uni. Firenze) Seminar 23 may / 21
52 First implementation A variation of Dijkstra algorithm is developed where: 1. create an ordered triple for each three consecutive nodes in the graph 2. check if the triangle corresponding to each triple intersect an obstacle 3. populate the priority queue with obstacle free triples the initial weight is 0 for triples where the first node is the start node is otherwise 4. pop the triple with lowest weight from the priority queue 5. update the weight and pointer to previous of all neighbouring triples a triple B is subsequent to a triple A if (A[2] = B[1]) (A[3] = B[2]) the weight of a neighbour is W (B) = W (A) + dist(a[1], A[2]) 6. repeat from point 4 until popped a special ending triple or a triple with weight 7. the shortest path (with free triangular convex hull) can be obtained following the previous pointer from the ending triple, and deconstructing the triples Martina Stefano (Uni. Firenze) Seminar 23 may / 21
53 First implementation A variation of Dijkstra algorithm is developed where: 1. create an ordered triple for each three consecutive nodes in the graph 2. check if the triangle corresponding to each triple intersect an obstacle 3. populate the priority queue with obstacle free triples the initial weight is 0 for triples where the first node is the start node is otherwise 4. pop the triple with lowest weight from the priority queue 5. update the weight and pointer to previous of all neighbouring triples a triple B is subsequent to a triple A if (A[2] = B[1]) (A[3] = B[2]) the weight of a neighbour is W (B) = W (A) + dist(a[1], A[2]) 6. repeat from point 4 until popped a special ending triple or a triple with weight 7. the shortest path (with free triangular convex hull) can be obtained following the previous pointer from the ending triple, and deconstructing the triples Martina Stefano (Uni. Firenze) Seminar 23 may / 21
54 Example 4 e z 0.15 s x y Martina Stefano (Uni. Firenze) Seminar 23 may / 21
55 Complexity Total (if obstacle area is not a function of the number of obstacles) O(d 2 Ob 2 + d 3 Ob ) The predominant factor is for checking the triples collisions with obstacles if d is constant O( Ob 2 ) But if we focus only on routing (i.e. we construct the graph only once) if d is constant O( Ob log Ob ) (same of Dijkstra with fixed degree) Martina Stefano (Uni. Firenze) Seminar 23 may / 21
56 Complexity Total (if obstacle area is not a function of the number of obstacles) O(d 2 Ob 2 + d 3 Ob ) The predominant factor is for checking the triples collisions with obstacles if d is constant O( Ob 2 ) But if we focus only on routing (i.e. we construct the graph only once) if d is constant O( Ob log Ob ) (same of Dijkstra with fixed degree) Martina Stefano (Uni. Firenze) Seminar 23 may / 21
57 Complexity Total (if obstacle area is not a function of the number of obstacles) O(d 2 Ob 2 + d 3 Ob ) The predominant factor is for checking the triples collisions with obstacles if d is constant O( Ob 2 ) But if we focus only on routing (i.e. we construct the graph only once) if d is constant O( Ob log Ob ) (same of Dijkstra with fixed degree) Martina Stefano (Uni. Firenze) Seminar 23 may / 21
58 Complexity Total (if obstacle area is not a function of the number of obstacles) O(d 2 Ob 2 + d 3 Ob ) The predominant factor is for checking the triples collisions with obstacles if d is constant O( Ob 2 ) But if we focus only on routing (i.e. we construct the graph only once) if d is constant O( Ob log Ob ) (same of Dijkstra with fixed degree) Martina Stefano (Uni. Firenze) Seminar 23 may / 21
59 Complexity Total (if obstacle area is not a function of the number of obstacles) O(d 2 Ob 2 + d 3 Ob ) The predominant factor is for checking the triples collisions with obstacles if d is constant O( Ob 2 ) But if we focus only on routing (i.e. we construct the graph only once) if d is constant O( Ob log Ob ) (same of Dijkstra with fixed degree) Martina Stefano (Uni. Firenze) Seminar 23 may / 21
60 Complexity Total (if obstacle area is not a function of the number of obstacles) O(d 2 Ob 2 + d 3 Ob ) The predominant factor is for checking the triples collisions with obstacles if d is constant O( Ob 2 ) But if we focus only on routing (i.e. we construct the graph only once) if d is constant O( Ob log Ob ) (same of Dijkstra with fixed degree) Martina Stefano (Uni. Firenze) Seminar 23 may / 21
61 Second implementation Reason First implementation interesting for complexity analysis but rejects many paths Add aligned control vertexes when an obstacle intersect a triple new vertexes calculated as linear combination of other vertexes using coefficients from obstacle detection Martina Stefano (Uni. Firenze) Seminar 23 may / 21
62 Second implementation Reason First implementation interesting for complexity analysis but rejects many paths Add aligned control vertexes when an obstacle intersect a triple new vertexes calculated as linear combination of other vertexes using coefficients from obstacle detection Martina Stefano (Uni. Firenze) Seminar 23 may / 21
63 Second implementation Reason First implementation interesting for complexity analysis but rejects many paths Add aligned control vertexes when an obstacle intersect a triple new vertexes calculated as linear combination of other vertexes using coefficients from obstacle detection Martina Stefano (Uni. Firenze) Seminar 23 may / 21
64 Second implementation Reason First implementation interesting for complexity analysis but rejects many paths Add aligned control vertexes when an obstacle intersect a triple new vertexes calculated as linear combination of other vertexes using coefficients from obstacle detection Martina Stefano (Uni. Firenze) Seminar 23 may / 21
65 Second implementation Reason First implementation interesting for complexity analysis but rejects many paths Add aligned control vertexes when an obstacle intersect a triple new vertexes calculated as linear combination of other vertexes using coefficients from obstacle detection Martina Stefano (Uni. Firenze) Seminar 23 may / 21
66 Increase degree Continuity Using quadratic B-Splines means C 1 continuity Not nice If we simply increase the B-Spline degree convex hull is not planar anymore convex hull is formed of union of tetrahedra Solution Add aligned vertexes in control polygon and then increase the degree Martina Stefano (Uni. Firenze) Seminar 23 may / 21
67 Increase degree Continuity Using quadratic B-Splines means C 1 continuity Not nice If we simply increase the B-Spline degree convex hull is not planar anymore convex hull is formed of union of tetrahedra Solution Add aligned vertexes in control polygon and then increase the degree Martina Stefano (Uni. Firenze) Seminar 23 may / 21
68 Increase degree Continuity Using quadratic B-Splines means C 1 continuity Not nice If we simply increase the B-Spline degree convex hull is not planar anymore convex hull is formed of union of tetrahedra Solution Add aligned vertexes in control polygon and then increase the degree Martina Stefano (Uni. Firenze) Seminar 23 may / 21
69 Increase degree Continuity Using quadratic B-Splines means C 1 continuity Not nice If we simply increase the B-Spline degree convex hull is not planar anymore convex hull is formed of union of tetrahedra Solution Add aligned vertexes in control polygon and then increase the degree Martina Stefano (Uni. Firenze) Seminar 23 may / 21
70 Increase degree Continuity Using quadratic B-Splines means C 1 continuity Not nice If we simply increase the B-Spline degree convex hull is not planar anymore convex hull is formed of union of tetrahedra Solution Add aligned vertexes in control polygon and then increase the degree Martina Stefano (Uni. Firenze) Seminar 23 may / 21
71 Increase degree Continuity Using quadratic B-Splines means C 1 continuity Not nice If we simply increase the B-Spline degree convex hull is not planar anymore convex hull is formed of union of tetrahedra Solution Add aligned vertexes in control polygon and then increase the degree Martina Stefano (Uni. Firenze) Seminar 23 may / 21
72 Example: quadratic to quartic (k=3 k=5) Add 2 vertexes per edge Martina Stefano (Uni. Firenze) Seminar 23 may / 21
73 Example: quadratic to quartic (k=3 k=5) Add 2 vertexes per edge Martina Stefano (Uni. Firenze) Seminar 23 may / 21
74 Example: quadratic to quartic (k=3 k=5) Add 2 vertexes per edge Martina Stefano (Uni. Firenze) Seminar 23 may / 21
75 Example: quadratic to quartic (k=3 k=5) Add 2 vertexes per edge Martina Stefano (Uni. Firenze) Seminar 23 may / 21
76 Postprocessing Purpose Simplify the control polygon remove useless turns After Dijkstra For each triple (a, b, c) of consecutive points in path If no obstacles intersect the triangle the triple is simplified to a single edge (a, c) Martina Stefano (Uni. Firenze) Seminar 23 may / 21
77 Postprocessing Purpose Simplify the control polygon remove useless turns After Dijkstra For each triple (a, b, c) of consecutive points in path If no obstacles intersect the triangle the triple is simplified to a single edge (a, c) Martina Stefano (Uni. Firenze) Seminar 23 may / 21
78 Postprocessing Purpose Simplify the control polygon remove useless turns After Dijkstra For each triple (a, b, c) of consecutive points in path If no obstacles intersect the triangle the triple is simplified to a single edge (a, c) Martina Stefano (Uni. Firenze) Seminar 23 may / 21
79 Postprocessing Purpose Simplify the control polygon remove useless turns After Dijkstra For each triple (a, b, c) of consecutive points in path If no obstacles intersect the triangle the triple is simplified to a single edge (a, c) Martina Stefano (Uni. Firenze) Seminar 23 may / 21
80 Postprocessing Purpose Simplify the control polygon remove useless turns After Dijkstra For each triple (a, b, c) of consecutive points in path If no obstacles intersect the triangle the triple is simplified to a single edge (a, c) Martina Stefano (Uni. Firenze) Seminar 23 may / 21
81 Postprocessing Purpose Simplify the control polygon remove useless turns After Dijkstra For each triple (a, b, c) of consecutive points in path If no obstacles intersect the triangle the triple is simplified to a single edge (a, c) Martina Stefano (Uni. Firenze) Seminar 23 may / 21
82 Used technologies Martina Stefano (Uni. Firenze) Seminar 23 may / 21
83 Used technologies Martina Stefano (Uni. Firenze) Seminar 23 may / 21
84 Used technologies Martina Stefano (Uni. Firenze) Seminar 23 may / 21
85 Future improvements Change underlying structure visibility graph rapidly exploring random tree (RRT) other... Improve postprocessing make a symmetric algorithm (path from a to b = path from b to a) Make optimization process try to find the best path that satisfy some constraints max curvature max torsion others... Martina Stefano (Uni. Firenze) Seminar 23 may / 21
86 Future improvements Change underlying structure visibility graph rapidly exploring random tree (RRT) other... Improve postprocessing make a symmetric algorithm (path from a to b = path from b to a) Make optimization process try to find the best path that satisfy some constraints max curvature max torsion others... Martina Stefano (Uni. Firenze) Seminar 23 may / 21
87 Future improvements Change underlying structure visibility graph rapidly exploring random tree (RRT) other... Improve postprocessing make a symmetric algorithm (path from a to b = path from b to a) Make optimization process try to find the best path that satisfy some constraints max curvature max torsion others... Martina Stefano (Uni. Firenze) Seminar 23 may / 21
88 Future improvements Change underlying structure visibility graph rapidly exploring random tree (RRT) other... Improve postprocessing make a symmetric algorithm (path from a to b = path from b to a) Make optimization process try to find the best path that satisfy some constraints max curvature max torsion others... Martina Stefano (Uni. Firenze) Seminar 23 may / 21
89 Future improvements Change underlying structure visibility graph rapidly exploring random tree (RRT) other... Improve postprocessing make a symmetric algorithm (path from a to b = path from b to a) Make optimization process try to find the best path that satisfy some constraints max curvature max torsion others... Martina Stefano (Uni. Firenze) Seminar 23 may / 21
90 Future improvements Change underlying structure visibility graph rapidly exploring random tree (RRT) other... Improve postprocessing make a symmetric algorithm (path from a to b = path from b to a) Make optimization process try to find the best path that satisfy some constraints max curvature max torsion others... Martina Stefano (Uni. Firenze) Seminar 23 may / 21
91 Future improvements Change underlying structure visibility graph rapidly exploring random tree (RRT) other... Improve postprocessing make a symmetric algorithm (path from a to b = path from b to a) Make optimization process try to find the best path that satisfy some constraints max curvature max torsion others... Martina Stefano (Uni. Firenze) Seminar 23 may / 21
92 Future improvements Change underlying structure visibility graph rapidly exploring random tree (RRT) other... Improve postprocessing make a symmetric algorithm (path from a to b = path from b to a) Make optimization process try to find the best path that satisfy some constraints max curvature max torsion others... Martina Stefano (Uni. Firenze) Seminar 23 may / 21
93 Future improvements Change underlying structure visibility graph rapidly exploring random tree (RRT) other... Improve postprocessing make a symmetric algorithm (path from a to b = path from b to a) Make optimization process try to find the best path that satisfy some constraints max curvature max torsion others... Martina Stefano (Uni. Firenze) Seminar 23 may / 21
94 Future improvements Change underlying structure visibility graph rapidly exploring random tree (RRT) other... Improve postprocessing make a symmetric algorithm (path from a to b = path from b to a) Make optimization process try to find the best path that satisfy some constraints max curvature max torsion others... Martina Stefano (Uni. Firenze) Seminar 23 may / 21
95 Future improvements Change underlying structure visibility graph rapidly exploring random tree (RRT) other... Improve postprocessing make a symmetric algorithm (path from a to b = path from b to a) Make optimization process try to find the best path that satisfy some constraints max curvature max torsion others... Martina Stefano (Uni. Firenze) Seminar 23 may / 21
96 The End. Questions? Thank you! Martina Stefano (Uni. Firenze) Seminar 23 may / 21
97 The End. Questions? Thank you! Martina Stefano (Uni. Firenze) Seminar 23 may / 21
98 The End. Questions? Thank you! Martina Stefano (Uni. Firenze) Seminar 23 may / 21
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