# Exact Cell Decomposition Methods

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1 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 1 ] Exact Cell Decomposition Methods Acknowledgement: arts of these course notes are based on notes from courses given by Jean-Claude Latombe at Stanford University (and Chapter 5 in his text Robot Motion lanning, Kluwer, 1991), O. Burçhan Bayazıt at Washington University in St. Louis. Seth Hutchinson at the University of Illinois at Urbana-Champaign, and Leo Joskowicz at Hebrew University.

2 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 2 ] Cell Decomposition Methods preprocessing: represent C free as a collection of cells (connected regions of C free ) = planning between configurations in the same cell should be easy build connectivity graph representing adjacency relations between cells = cells adjacent if can move directly between them query processing: 1. locate cells k init and k goal containing start and goal configurations 2. search the connectivity graph for a channel or sequence of adjacent cells connecting k init and k goal 3. find a path that is contained in the channel of cells Two major variants of methods: exact cell decomposition: set of cells exactly covers C free complicated cells with irregular boundaries (contact constraints) harder to compute approximate cell decomposition: set of cells approximately covers C free simpler cells with more regular boundaries easier to compute

3 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 3 ] Exact Cell Decomposition Methods Idea: decompose C free into a collection K of non-overlapping cells such that the union of all the cells exactly equals the free C-space, i.e., C free = k K k Cell Characteristics: geometry of cells should be simple so that it is easy to compute a path between any two configurations in a cell it should be pretty easy to test the adjacency of two cells, i.e., whether they share a boundary it should be pretty easy to find a path crossing the portion of the boundary shared by two adjacent cells Thus, cell boundaries correspond to criticalities in C, i.e., something changes when a cell boundary is crossed. No such criticalities in C occur within a cell. The main drawback of exact cell decomposition methods is the difficulty of computing the decomposition... a simple, efficient sweep-line approach works when C = IR 2 (no rotation) and CB is a polygonal region, i.e., each C-obstacle CB i is a polygon a bit harder, but still tractable, when C = IR 2 SO(2) (rotation) a method is known for the general case impractical, but important as existance proof for a general path planning method

4 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 4 ] Exact Cell Decomposition when C = IR 2 Assumptions: the robot A and all obstacles B i are polygons, and C free is bounded (not really necessary, just easier). Note, this implies that CB is a polygonal region. Definitions: A convex polygonal decomposition K of C free is a finite collection of convex polygons, called cells, such that the interiors of any two cells do not intersect and the union of all cells is C free. Two cells k, k K are adjacent iff k k is a line segment of non-zero length (i.e., not a single point) The connectivity graph associated with a convex polygonal decomposition K of C free is an undirected graph G where nodes in G correspond to cells in K nodes connected by edge in G iff corresponding cells adjacent in K CB CB 3 8 Connectivity Graph G CB 2 9 cells

5 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 5 ] ath lanning with a Convex olygonal Decomposition input: configurations q init and q goal, and CB which is a polygonal region output: a path in C free connecting q init and q goal 1. Build K, the convex polygonal decomposition of CB 2. Construct the connectivity graph G of K 3. locate the cells k init and k goal in K containing q init and q goal 4. find a path in G between the nodes corresponding to k init and k goal corresponds to a sequence of cells forming a channel in C free 5. find a free path from q init to q goal in the channel 1 2 q init CB CB 3 8 path in G (channel) CB 2 9 q goal

6 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 6 ] Getting a path from a channel Let k 1, k 2,..., k p denote the channel in C free, where k 1 = k init and k p = k goal. Goal: find a path (1-dimensional curve) from q init to q goal that is contained in the interior of the channel (and thus in C free ) Note: since each k i is convex, the straight line between any two configurations (points) in k i lies in k i (and thus in C free ) Idea: use midpoints of cell boundaries as crossing points between cells Let Q i denote the midpoint of the line segment common to both k i and k i+1 in the channel, i.e., Q i is the midpoint of k i k i+1 Then, q init, Q 1, Q 2,..., Q p 1, q goal is a path in the channel CB 1 q init CB 3 free path in channel q goal CB 2 q goal Caveat: if Q i 1 Q i lies in k i, then the path above is only semi-free (i.e., it has contact). If we want a free path, then we need to insert another point between Q i 1 and Q i that lies in the interior of k i

7 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 7 ] Building K Trapezoidal Decomposition Basic Idea: at every vertex of CB, extend a vertical line up and down in C free until it touches a C-obstacle or the boundary of C free CB 3 CB 1 CB 2 sweepline Algorithm: build Trapezoidal Decomposition input: boundary representation of CB output: trapezoidal decomposition of CB 1. sort vertices of CB by x-coordinate (assume general position unique xs) 2. sweep a vertical line L from left to right, stopping at each vertex v in CB note: v is incident on exactly two edges e 1 and e 2 in CB case 1: e 1 and e 2 both lie to the right of L close this cell v L open this cell open this cell e 2 e 1 CB i CB i v L e e 1 open this cell 2 (a) (b)

8 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 8 ] Building K Trapezoidal Decomposition (cont d) case 2: exactly one of e 1 and e 2 lies to the right of L close this cell open this cell e 1 e 2 v CB i L case 3: e 1 and e 2 both lie to the left of L CB i e 2 close this cell e 1 close this cell L v open this cell e 1 close this cell e 2 L v CB i (a) (b) Complexity: O(n log n), where n is number of vertices in CB initial sorting O(n log n) if maintain a sorted list of cells currently intersected by L, then each event processed in O(log n) time (find cell containing v and update L s list of intersected cells) O(n log n) time for all events can determine adjacency relations between cells (and G) during sweep (and also cells containing q init and q goal )

9 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 9 ] Ex: Trapezoidal Decomposition Sweepline Algorithm CB CB CB Events: a b c d e f g h i j k l m n o event a (case 1(a)): just prior to event a, L intersects cell 1 at event a we close cell 1 and open cells 2 and 3 just after event a, L intersects cells 2 and 3 (in that order) event b (case 1(b)): just prior to event b, L intersects cells 2 and 3 (in that order) at event b we open cell 4 just after event b, L intersects cells 2, 4, and 3 (in that order) event c (case 3(a)): just prior to event c, L intersects cells 2, 4, and 3 (in that order) at event c we close cells 2 and 4 and open cell 5 just after event c, L intersects cells 5 and 3 (in that order) event e (case 2): just prior to event e, L intersects cells 5, 6, and 7 (in that order) at event e we close cell 6 and open cell 8 just after event e, L intersects cells 5, 8, and 7 (in that order)

10 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 10] Summary Convex olygonal Decomposition when C = IR 2 When C = IR 2 and CB is a polygonal region: the trapezoidal decomposition is an exact cell decomposition of C free trapezoidal decomposition can be built in O(n log n) time [Chazelle, 1987] trapezoidal decomposition is not an optimal convex decomposition (i.e., it does not have a minimal number of cells) it is N-Hard to compute an optimal convex decomposition of a polygon (C free ) with holes (CB i ) [Lingas, 1982] the trapezoidal (vertical) decomposition can be extended to the case when C = IR 3 use sweep-plane

11 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 11] Exact Cell Decomposition for moving a ladder in the plane We ll now consider how to compute an exact cell decomposition of C free for a harder, but still very particular case: the robot A is a line segment of length d (called a ladder, bar, or rod) which can translate and rotate the workspace W = IR 2, and obstacles are polygons so B is a polygonal region (we ll assume B is a manifold with boundary) the configuration space C = IR 2 [0, 2π) y (1,1,π/4) Q (4,3,3π/2) (1,1,0) Q Q F W x The Robot A: is a line segment Q of length d is A s reference point (i.e., = O A, origin of F A ) the x-axis of F A coincides with the line containing Q parameterize configurations of A by (x, y, θ) (x, y) IR 2 position of θ [0, 2π) offset of Q from F W s x-axis (horizonal)

12 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 12] The Critical Curve Method of Schwartz and Sharir The algorithm we ll look at uses the critical curve technique and is due to Schwartz and Sharir (more efficient methods exist, but this is a nice example) Basic Idea: decompose the set of positions of (the x-y plane corresponding to positions of A s origin) into two-dimensional non-critical regions the structure of the C-obstacles is constant above (in θ direction) a non-critical region, i.e., the set of C-surfaces intersected by a line perpendicular to x-y plane does not change in a non-critical region boundaries (in x-y plane) of non-critical regions are critical curves lift 2d non-critical regions into 3d cells (cylinders) compute adjacency relationships between cylinders (connectivity graph G) An example with two-dimensional C-space θ CB1 cylinders CB2 x critical curves non-critical regions critical curves are points on x-axis non-critical regions are intervals on x-axis cylinders are rectangles

13 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 13] Critical Curves for three-dimensional C An example with C = IR 2 [0, 2π) and spherical C-obstacles θ CB1 CB3 possible contact with B1 y CB2 critical curves no possible contact possible contact with B1 or B2 x 3 critical curves are obtained by projecting the spheres onto the x-y plane critical curves partition the x-y plane into the 5 non-critical regions within each non-critical region, a line perpendicular to the x-y plane intersects the same set of C-surfaces at a critical curve, the set of C-surfaces intersected by a line perpendicular to the x-y plane changes within a non-critical region, the value of θ will determine which, if any, of the C-obstacles overlap with A, i.e., the non-critical region only identifies which types of contact are possible, not what contacts actually will occur

14 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 14] Critical Curves for a Ladder Returning now to the segment Q translating and rotating amongst polygonal obstacles in the plane... CB IR 2 [0, 2π) is bounded by faces, each of which is a ruled surface corresponding to some type of contact Type-A surface: segment Q contains an obstacle vertex Type-B surface: or Q is contained in an obstacle edge The critical curves are projections onto the x-y plane of the following curves that live in IR 2 [0, 2π) 1. projections of curves that bound C-obstacle faces (i.e., C-obstacle edges) 2. projections of curves in C-obstacle faces where the tangent plane to the face is perpendicular to the x-y plane θ face 1 CB face 2 y critical curve (tangent plane to face 1 is perpendicular to x-y plane) R3 R2 R1 critical curve (boundary face 1 & 2) example: 2 critical curves partition x-y plane into 3 non-critical regions: In R1, 2 possible kinds of contact (face 1 or face 2) In R2, 1 possible contact (face 2 only) In R3, no possible contact Key: critical curves are places where possible kinds of contact change x

15 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 15] An Example Key: critical curves are places where possible kinds of contact change, i.e., one type becomes impossible and another type becomes possible So, to find the critical curves (and non-critical regions) we need to look for (x, y) positions of where the types of contact can change Example: B is a closed half-plane bounded by a line L1 d Q B Q Q R1 R2 R3 Q L1 (y=y0) L2 (y=y0-d) if is on the far side of L2, then no contact can occur if is on L2, then Q-B contact possible if is between L1 and L2, then Q-B contact possible if is on L1, then -B contact, and Q-B possible two critical curves L1 (y = y 0 ): -B contact and Q-B contact possible L2 (y = y 0 d): Q-B contact possible three non-critical regions R1 (completely in CB): {(x, y) IR 2 y > y 0 } R2 (Q-B contact possible): {(x, y) IR 2 y 0 d < y < y 0 } R3 (no contact possible): {(x, y) IR 2 y < y 0 }

16 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 16] A Taxonomy of Critical Curves Two ways to get critical curves compute a boundary representation of CB, project all the edges onto the x-y plane, and find all faces with tangent planes perpendicular to the x-y plane and project the curves in these faces to x-y plane 2. construct the critical curves by working directly with the obstacles in W consider all possible contacts (i.e., vertices/edges of each B i and A) Schwartz and Sharir (1983) took the latter approach. They studied the problem of moving the ladder A = Q in the plane and came up a taxonomy of critical curves. Type-0 Curves: Let E be an obstacle edge. E is a Type-0 critical curve. Type-0 critical curve E B if is inside an obstacle, then (x, y, θ) CB for all values of θ if is outside an obstacle, then there may be some values of θ where (x, y, θ) CB

17 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 17] Taxonomy of Critical Curves (cont d) Type-1 Curves: Let E be an obstacle edge. The straight line segment at distance d from and parallel to E is a Type-1 critical curve. Type-1 critical curve E B if is inside this segment (between it and E), then contact between Q and E might occur for some values of θ if is outside this segment, then no contact between Q and E is possible for any value of θ

18 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 18] Taxonomy of Critical Curves (cont d) Type-2 Curves: Let X be an obstacle vertex. The circular arc of radius d centered at X and bounded by the supporting half lines of the obstacle edges incident to X is a Type-2 critical curve. x Type-2 critical curves B y if is inside this arc, then contact between Q and X might occur for some values of θ if is outside this arc, then no contact between Q and X is possible for any value of θ

19 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 19] Taxonomy of Critical Curves (cont d) Type-3 Curves: Let E be an obstacle edge, and let X be a convex vertex that is an endpoint of E. The straight line segment traced by as Q slides along E and Q is contained in E is a Type-3 critical curve. d Type-3 critical curves if here, no contact with E1 possible x E2 E1 B if is between this segment and the other edge incident on X, then no contact between Q and E is possible (except at X) if is not between this segment and the other edge incident on X, then contact between Q and E may be possible for some values of θ

20 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 20] Taxonomy of Critical Curves (cont d) Type-4 Curves: Let X 1 and X 2 be convex obstacle vertices ( d apart) such that the line passing through them is tangent to B at both X 1 and X 2. The line segment traced by as Q slides while touching both X 1 and X 2 is a Type-4 critical curve. Type-4 critical curves if here, no contact with x2 possible x1 d x2 d y1 y2 if is between this segment and the edge incident to X 1 (X 2 ) on the far side of B, then no contact between Q and X 2 (X 1 ) is possible if is not between this segment and the edge incident to X 1 (X 2 ) on the far side of B, then contact between Q and X 2 (X 1 ) may be possible for some values of θ

21 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 21] Taxonomy of Critical Curves (cont d) Type-5 Curves: Let E be an obstacle edge and let X be a convex obstacle vertice that is not an endpoint of E. Let h be the distance from X to E. If h < d, the curve traced by as Q moves in a way such that it simultaneously touches E and X, while being tangent to B at X, is a Type-5 critical curve. B2 x Type-1 critical curve (part of conchoid of Nicomedes) E B1 if is inside this curve, then contact between Q and X is not possible but contact with E may be possible for some values of θ if is on this curve, then simultaneous contact between Q and X and E is possible if is outside this curve, then simultaneous contact between Q and X and E is not possible

22 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 22] Taxonomy of Critical Curves (cont d) A closer look at Type-5 Curves y x X h d E k Q Letting the origin be X, and the x-axis parallel to E we have: d 2 = (y + h) 2 + (x + k) 2 (1) y x = h k (2) Thus, substituting hx/y for k in (1) we get (eventually): x 2 = y 2 d 2 (y + h) 1 2 (3) When y 0, this equation represents a Type-5 critical curve (which is a subset of the conchoid of Nicomedes).

23 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 23] Example: An Arrangement of Critical Curves The union of all critical curves is called an arrangement of critical curves. It partitions the x-y plane into a set of non-critical regions bounded by critical curve sections (the edges in the arrangement). B Type-3 curves R6 R1 E1 R4 x R3 R5 E2 R2 Type-2 curve Type-1 curves Each non-critical region corresponds to different possible kinds of contact. For example, when is in region: R1: there is possible contact between Q and E1 (Type-B) R2: there is possible contact between Q and E2 (Type-B) R3: there is possible contact between Q and both E1 and E2 (Type-B) R4: there is possible contact between Q and E1 (Type-B) and possible contact between Q and x (Type-A) R5: there is possible contact between Q and E2 (Type-B) and possible contact between Q and x (Type-A) R6: there is no possible contact

24 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 24] Complexity Critical Curve Arrangements 1. O(n 2 ) critical curves (n is number of vertices of B) O(n) critical curves of Types 0-3 (one edge or one vertex of B) O(n 2 ) critical curves of Types 4-5 (two vertices or vertex/edge of B) 2. each critical curve is algebraic of degree 1 (Types 0, 1, 3, 4), 2 (Type-2), or 4 (Type-5) two critical curves intersect in O(1) points (unless they coincide, in which case we perturb things a bit and we re ok since B is a manifold) 3. O(n 4 ) intersection points (sections) in an arrangement of critical curves O(n 2 ) intersections (sections) on each critical curve (by 1 and 2) Redundant Critical Curves Sometimes a critical curve may be generated by the above rules even if the particular contact is not feasible. Such a curve is called redundant. B2 critical curve of Type-1 due to E is redundant B1 E Because of B 2, Q can never be such that is on the Type-1 critical curve due to E while Q is on E. These will not cause a problem (we will see how to get rid of them easily)

25 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 25] Example: A More Complex Arrangement of Critical Curves

26 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 26] Exact Decomposition of C free Basic Idea: We extend the non-critical regions in the θ direction perpendicular to the x-y plane to form cylinders. Each cylinder is partitioned into a disjoint set of free cells (bounded by C-obstacles). Example: Two-dimensional C-space 2π θ4 CB1 θ3 θ2 cylinder cells in one cylinder θ1 0 (x,y) CB2 x non-critical region critical curves artitioning cylinders into cells... Definition: F (x, y) is the set of all free orientations of Q when is at a non-critical position (x, y), i.e., F (x, y) = {θ (x, y, θ) C free } If all orientations of Q at position (x, y) are free, then F (x, y) = [0, 2π). Otherwise, F (x, y) is a finite collection of open, maximal intervals. e.g., F (x, y) = {(θ 2, θ 3 ), (θ 4, θ 1 )}

27 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 27] Stops What do the intervals in F (x, y) look like in W? θ1 a E1 θ3 θ4 θ2 E2 θ5 b (x,y) c θ6 Definitions: F (x, y) = {(θ 1, θ 2 ), (θ 3, θ 4 ), (θ 5, θ 6 )} A stop is the vertex or edge of B that Q contacts at the limiting orientations in an interval in F (x, y). Each interval in F (x, y) has two stops a clockwise stop and a counterclockwise stop. e.g., b and a are stops for (θ 1, θ 2 ), E1 and E2 are stops for (θ 3, θ 4 ), and E2 and c are stops for (θ 4, θ 5 ) λ 1 (x, y, s) is the value of θ where Q contacts clockwise stop s with in position (x, y). λ 2 (x, y, s ) is the value of θ for counterclockwise stop s e.g., λ 1 (x, y, E1) = θ 3 and λ 2 (x, y, E2) = θ 4 σ(x, y) is the set of all pairs of stops associated with intervals in F (x, y). If F (x, y) = [0, 2π), then σ(x, y) = [Ω, Ω], where Ω is a special symbol e.g., σ(x, y) = {[b, a], [E1, E2], [E2, c]}

28 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 28] Stops within a non-critical region and Cells Fact: Let R be a non-critical region, and let (x, y) and (x, y ) be any two points in R. Then the intervals in F (x, y) and F (x, y ) have the same set of stops, i.e., σ(x, y) = σ(x, y ). Let σ(r) denote this set of stops. This follows from the fact that all points in a non-critical region have the same possible types of contact. Basic Idea: We have so far decomposed the x-y plane into a set of noncritical regions. Our exact decomposition of C free will consist of free cells in the cylinders above the non-critical regions. 2π θ4 θ3 θ2 CB1 cylinder cells in one cylinder θ1 CB2 non-critical region 0 (x,y) x critical curves Definition: Let R be a non-critical region and let [s 1, s 2 ] σ(r). cell(r, s 1, s 2 ) = {(x, y, θ) (x, y) R and θ (λ 1 (x, y, s 1 ), λ 2 (x, y, s 2 ))} is called a cell, i.e., cell(r, s 1, s 2 ) is the set of all points in the cylinder above R such that Q is oriented between the two stops s 1 and s 2. It is an open, connected subset of C free. Note, it is maximal in R s cylinder, but is not necessarily maximal in C free. The set of all such cells forms a decomposition of C free (but not C).

29 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 29] Building a Connectivity Graph (cell adjacencies) Definition: cells cell(r, s 1, s 2 ) and cell(r, s 1, s 2) are adjacent iff (i) the boundaries of R and R share a critical curve segment β, and (ii) (x, y) int(β), (λ 1 (x, y, s 1 ), λ 2 (x, y, s 2 )) (λ 1 (x, y, s 1), λ 2 (x, y, s 2)) Thus, if two cells k and k are adjacent, then any configuration in k can be connected to any configuration in k by a free path whose projection onto the x- y plane crosses β transversally, with constant orientation in some neighborhood of the crossing point. Let R and R be two non-critical regions such that β is a section of the critical curve contained in the boundaries of both R and R. Later we will define a crossing rule for β that will tell us the adjacency relations between the cells contained in the cylinders above R and R. (The crossing rule will be based on σ(r) and σ(r ), i.e., the set of stops for the free orientations in R and R.) But first we ll look at some examples...

30 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 30] Adjacency Example: β is Type-1 critical curve segment Let β be a section of a Type-1 critical curve due to an obstacle edge E, and let R be the region between E and β and R the region on the other side of β. cell(r, s 1, s 2 ) and cell(r, s 1, s 2) are adjacent when can move (directly) from R to R in such a way that Q only translates when crosses β. Cases where cell(r, s 1, s 2 ) and cell(r, s 1, s 2) are adjacent are shown below. Case 1: [s 1, s 2 ] = [Ω, Ω] and [s 1, s 2] = [E, E] Case 2: [s 1, s 2] = [s 1, s 2 ] E R β R [s 1, s 2 ] = [Ω, Ω] [s 1, s 2] = [E, E] E2 E1 E3 E R β R [s 1, s 2 ] = [E1, E3] [s 1, s 2] = [E1, E3] Case 3: [s 1, s 2] = [s 1, E] (or [s 1, s 2] = [E, s 2 ]) E2 E1 E R β [s 1, s 2 ] = [E1, E2] [s 1, s 2] = [E1, E] R

31 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 31] Adjacency Example: β is Type-2 critical curve segment Let β be a section of a Type-2 critical curve due to a concave vertex x of B and adjacent to two edges E1 and E2. Assume the outer angle between E1 and E2 is π/2. Let β be the leftmost segment of the Type-2 critical curve due to x. Cases where cell(r, s 1, s 2 ) and cell(r, s 1, s 2) are adjacent are shown below. Case 1: s 1 = E1, s 1 = E2 and s 2 = s 2 R R β R E1 x E2 β R E1 v x E2 [s 1, s 2 ] = [E1, E1] and [s 1, s 2] = [E2, E1] [s 1, s 2 ] = [E1, v] and [s 1, s 2] = [E2, v] Case 2: [s 1, s 2] = [s 1, s 2 ] R E3 β R E1 x E2 [s 1, s 2 ] = [E3, E1] and [s 1, s 2] = [E3, E1]

32 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 32] Some Non-Adjacent Cells Some cases where cell(r, s 1, s 2 ) and cell(r, s 1, s 2) are not adjacent. E2 E1 E3 E R β R [s 1, s 2 ] = [E1, E3] [s 1, s 2] = [E, E2] R β E3 v E2 [s 1, s 2 ] = [E3, E1] [s 1, s 2] = [E2, v] R E1 x In the situations above there is no way to move between the two configurations while keeping in R R.

33 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 33] A Crossing Rule for β (computing adjacent cells) Let R and R be two non-critical regions such that β is a critical curve section common to the boundaries of both R and R. (Assume β is not redundant and does not coincide with any other critical curve.) Fact: An exhaustive case analysis would show that whenever int(β) is crossed transversally the set σ(x, y) changes in one of the following ways: one new pair of stops appears in σ(x, y) one old pair of stops disappears from σ(x, y) one of the stops in a pair [s 1, s 2 ] currently in σ(x, y) changes (i.e., [s 1, s 2 ] is replaced by [s 1, s 2] and either s 1 = s 1 or s 2 = s 2) We can now state the following rule for determining cell adjacencies (when β is not contained in any other critical curve). Crossing Rule for β 1. for each [s 1, s 2 ] σ(r) σ(r ), cell(r, s 1, s 2 ) is adjacent to cell(r, s 1, s 2 ) (i.e., cells with the same stops) 2. for each [s 1, s 2 ] = σ(r) σ(r ) and [s 1, s 2] = σ(r ) σ(r) (if they exist), cell(r, s 1, s 2 ) is adjacent to cell(r, s 1, s 2). Note that in this case one of the following conditions holds: (a) s 1 = s 1, or (b) s 2 = s 2, or (c) s 1 = s 2 = Ω or s 1 = s 2 = Ω

34 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 34] The Connectivity Graph and Motion lanning Definition: The connectivity graph G corresponding to our exact decomposition of C free into cells (which are contained in the cylinders above the non-critical regions in the x-y plane) is an undirected graph where nodes correspond to cells cell(r, s 1, s 2 ), where R is a non-critical region and [s 1, s 2 ] σ(r) two nodes are connected by an edge iff their corresponding cells are adjacent Motion lanning: 1. build connectivity graph G (does not depend on q init or q goal ) (a) find all the critical curves (b) compute their arrangement (intersect them) (c) find the non-critical regions R and their stops σ(r) (identifying cells cell(r, s 1, s 2 ) in exact decomposition) (d) determine the cell adjacencies (critical curve segments and stops) 2. locate the cells containing q init and q goal 3. find a path in G between the nodes corresponding to the cells containing q init and q goal Fact: This is an exact motion planning method, i.e., there exists a free path connecting q init and q goal iff there exists a path in G between the nodes corresponding to the cells containing q init and q goal.

35 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 35] The Corner Example R13 Q R1 E2 E1 R2 R12 R11 x1 R3 E4 R10 R4 R9 R6 R8 R7 E3 R5 x2 Stops σ(r 1 ) = {[E 1, E 3 ], [E 3, E 1 ]} σ(r 2 ) = {[E 1, E 3 ], [E 3, X 1 ]} σ(r 3 ) = {[E 1, E 3 ], [E 3, E 4 ]} σ(r 4 ) = {[E 1, E 3 ], [E 3, E 4 ], [E 4, X 1 ]} σ(r 5 ) = {[E 1, E 3 ], [E 4, X 1 ]} σ(r 6 ) = {[E 1, E 3 ], [E 4, E 2 ]} σ(r 7 ) = {[E 4, E 3 ]} σ(r 8 ) = {[E 4, E 2 ], [X 1, E 3 ]} σ(r 9 ) = {[E 4, E 2 ], [E 1, E 3 ], [E 3, E 4 ]} σ(r 10 ) = {[E 4, E 2 ], [X 1, E 3 ], [E 3, E 4 ]} σ(r 11 ) = {[E 4, E 2 ], [E 3, E 4 ]} σ(r 12 ) = {[E 4, E 2 ], [X 1, E 4 ]} σ(r 13 ) = {[E 4, E 2 ], [E 2, E 4 ]}

36 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 36] The Corner Example R13 Q E2 R12 E4 R1 E1 R2 R11 x1 R3 R10 R4 R9 R6 R8 R7 E3 R5 x2 Some Adjacent Cells σ(r 4 ) = {[E 1, E 3 ], [E 3, E 4 ], [E 4, X 1 ]} and σ(r 5 ) = {[E 1, E 3 ], [E 4, X 1 ]} cell(r 4, E 1, E 3 ) is adjacent to cell(r 5, E 1, E 3 ) cell(r 4, E 4, X 1 ) is adjacent to cell(r 5, E 4, X 1 ) σ(r 12 ) = {[E 4, E 2 ], [X 1, E 4 ]} and σ(r 13 ) = {[E 4, E 2 ], [E 2, E 4 ]} cell(r 12, E 4, E 2 ) is adjacent to cell(r 13, E 4, E 2 ) cell(r 12, X 1, E 4 ) is adjacent to cell(r 13, E 2, E 4 ) σ(r 6 ) = {[E 1, E 3 ], [E 4, E 2 ]} and σ(r 7 ) = {[E 4, E 3 ]} cell(r 6, E 1, E 3 ) is adjacent to cell(r 7, E 4, E 3 ) cell(r 6, E 4, E 2 ) is adjacent to cell(r 7, E 4, E 3 )

37 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 37] The Corner Example: Connectivity Graph R13 Q R1 E2 E1 R2 R12 R11 x1 R3 E3 E4 R10 R4 R9 R6 R5 R8 R7 x2 R10 x1,e3 R9 E1,E3 R8 x1,e3 R1 E1,E3 R2 E1,E3 R3 E1,E3 R4 E1,E3 R5 E1,E3 R6 E1,E3 R7 E4,E3 R6 E4,E2 R8 E4,E2 R10 E4,E2 R11 E4,E2 R12 E4,E2 R13 E4,E2 R5 E4,x1 R9 E4,E2 R4 E4,x1 R1 E3,E1 R2 E3,x1 R3 E3,E4 R4 E3,E4 R9 E3,E4 R10 E3,E4 R11 E3,E4 R12 x1,e4 R13 E2,E4

38 Randomized Motion lanning Nancy Amato Fall 04, Univ. of adova Exact Cell Decomposition Methods [ 38] Exact Cell Decomposition: The General Case So far we ve considered only special cases. It turns out that the cylindrical decomposition for the ladder can be extended to the general case. In the general case, a cylindrical algebraic decomposition can be built that is an exact cell decomposition of C free suitable for motion planning the only constraint is that both A and B can be described as algebraic sets (e.g., no sin or cos) Collins gave a constructive proof that such a decomposition exists this particular decomposition is called the Collins decomposition algorithm is polynomial in n, the number of polynomials, and their degree, used to describe A and B algorithm is doubly exponential in m, the dimension of C (O(2 2O(m) ) algorithm is recursive, e.g., builds m dimensional cylinders by projecting to m 1-dimensional space and then lifting, and the m 1 dimensional cells are obtained recursively by the same process decomposition has number of cells doubly exponential in m Schwartz and Sharir [1983] described a more efficient way to construct the connectivity graph for the Collins decomposition a more efficient way to test cell adjacencies is it only (!) doubly exponential in m, e.g., O((2 2m ) as opposed to O(2 2O(m) ) Importance of These Results: they establish that a general (exact) motion planning method exists that is polynomial in the geometric complexity. Unfortunately, they are not practical solutions in most cases since they are complex and have high complexity.

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