Search Spaces I. Before we get started... ME/CS 132b Advanced Robotics: Navigation and Perception 4/05/2011

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1 Search Spaces I b Advanced Robotics: Navigation and Perception 4/05/ Before we get started... Website updated with Spring quarter grading policy 30% homework, 20% lab, 50% course project Website updated with new late homework policy 3 late days allowed in Spring quarter 2

2 Part II Part I Reasoning about mobility is important. Search space design is often difficult. Image: [Howard09a] Image: RedTeamRacing.com 3 Search Space Lectures Outline Introductory Topology Configuration Spaces Search Space Design Search Space Design with Differential Constraints Applications Current and Active Research Covers topics in Chapters 3, 4, 5, and 14 in S. LaValle s Planning Algorithms 4

3 Recap of Motion Simulation Nomenclature State, Action, Transition Equation Kinematic Constraints and Models Dynamic Constraints and Models φ v x body L Motion Integration LTI, Euler and Runge-Kutta ρ (x, y) θ 5 Introductory Topology 6

4 Sets and Spaces Open Set Closed Set does not include boundary points includes boundary points Topological Spaces: A collection of open sets of X Three Axioms of Topological Spaces: 1) X and are open sets 2) The intersection of any finite number of open sets is an open set 3) The union of any open sets is an open set 7 Manifolds A topological space manifold if for every exists an open set is a there that: any point behaviors like it should in 1) 2) is homeomorphic to 3) is fixed for all dimensionality A Cartesian product is a new topological space which is not a manifold? 8

5 Higher Dimensional Manifolds unit sphere defined as higher dimensional spheres are defined as n-dimensional real projective space is the set of all lines in that pass through the origin important later homeomorphic (equivalent topology) if all pairs of antipodal points can be identified 9 Homotopy Homotopy is about path equivalence A path is homotopic if one can be continuously warped into another 10

6 Configuration Spaces Geometric Transformations 11 Brief Review of Transforms 2D Homogeneous transforms Rotation by θ and a translation by x and y (in that order) x t y t θ c(θ) s(θ) x t s(θ) c(θ) y t x y 1 = xc(θ) ys(θ) t xs(θ)c(θ) t 1 = x s(var) sin(var) note shorthand: c(var) cos(var) 12

7 Brief Review of Transforms (cont.) 3D Homogeneous transforms Rotation by γ (roll), rotation by β (pitch), rotation by α (yaw), and a translation by x, y, and z (in that order) R z (α) = R y (β) = c(α) s(α) 0 s(α) c(α) c(β) 0 s(β) s(β) 0 c(β) +z +z γ rotate about x rotate about y +z α +z β rotate about z translate in x, y, and z R x (γ) = s(var) sin(var) note shorthand: c(var) cos(var) 13 +z c(γ) s(γ) 0 s(γ) c(γ) Brief Review of Transforms (cont.) 3D Homogeneous transforms Rotation by γ (roll), rotation by β (pitch), rotation by α (yaw), and a translation by x, y, and z (in that order) +z +z γ rotate about x rotate about y +z α +z rotate β about z translate in x, y, and z +z c(α)c(β) c(α)s(β)s(γ) s(α)c(γ) c(α)s(β)c(γ)+s(α)s(γ) x t s(α)c(β) s(α)s(β)s(γ)+c(α)c(γ) s(α)s(β)c(γ) c(α)s(γ) y t s(β) c(β)s(γ) c(β)c(γ) z t s(var) sin(var) note shorthand: c(var) cos(var) 14 x y z 1 = x

8 Issues with Euler Representations Non-zero angles can result in the identity matrix α = β = γ =0 same as α = β = γ = π Non-unique solutions for transforms (destroys topology!) c(α)c(β) c(α)s(β)s(γ) s(α)c(γ) c(α)s(β)c(γ)+s(α)s(γ) x t s(α)c(β) s(α)s(β)s(γ)+c(α)c(γ) s(α)s(β)c(γ) c(α)s(γ) y t s(β) c(β)s(γ) c(β)c(γ) z t z +z γ rotate about x rotate about y +z α +z β rotate about z translate in x, y, and z s(var) sin(var) note shorthand: c(var) cos(var) 15 +z x y z 1 = x Quaternions Four vector with scalar and imaginary components h = a + bi + cj + dk Properties: a, b, c, d R i 2 = j 2 = k 2 = ijk = 1 ν = b c d h H space of all Quaternions h 1 h 2 = {a 1 a 2 ν 1 ν 2,a 1 ν 2 + a 2 ν 1 + ν 1 ν 2 } 16

9 Unit Quaternions Restrict the set of all quaternions to be of unit length homeomorphic h = a + bi + cj + dk a 2 + b 2 + c 2 + d 2 =1 Representing rotations: 2(a 2 + b 2 ) 1 2(bc ad) 2(bd + ac) R(h) = 2(bc + ad) 2(a 2 + c 2 ) 1 2(cd ab) 2(bd ac) 2(cd + ab) 2(a 2 + d 2 ) 1 θ θ θ θ h = cos + v 1 sin i + v 2 sin j + v 3 sin k Unit Quaternions Not even this representation is truly unique h = cos θ + v 1 sin 2 θ i + v 2 sin 2 θ j + v 3 sin 2 Rotations about h and -h are identical (antipodes) θ k 2 18

10 Configuration Spaces Search Space Manifolds 19 SE(2) Configuration Space Translation in (x,y) is a twodimensional manifold Rotation in SO(2) is a onedimensional manifold that wraps around (0,2π) Configuration space is similar to a three-dimensional cylinder M 1 = R 2 M 2 = S 1 +θ C = M 1 M 2 = R 2 S 1 20

11 SE(3) Configuration Space Translation in (x,y,z) is a threedimensional manifold Rotation in SO(3) is a threedimensional manifold with antipodal points in the Quaternion representation Configuration space for SE(3) as the product of the translational and rotational manifolds C = M 1 M 2 = R 3 RP 3 M 1 = R 3 the antipodal points are identified with the unit quaternion representation 21 Search Space Design Concepts 22

12 Metric Spaces Metric Space is a function used to measure the distance between two points in A Metric space is a topological space with a function ρ that has four properties: 1) Non-negativity: 2) Reflexivity: 3) Symmetry: 4) Triangle Inequality: 23 L P Metrics L P Metric are used in for any Three common types of L P Metrics include (Manhattan Metric) (Euclidean Metric) (L Metric) 24

13 Metrics for Cartesian Products Metric spaces can be designed for Cartesian products through linear combinations or Euclidean norms These metric spaces require weighing parameters balance the contribution of different aspects of the metric spaces to 25 Useful Motion Planning Metrics SO(2) Metric using θ SO(3) Metric using Quaternions quaternion coefficients ρ (h 1,h 2 )=min{ρ s (h 1,h 2 ), ρ s (h 1, h 2 )} spherical linear interpolation (slerp) used to determine the proper angular displacement of the quaternion 26

14 Completeness The capacity for a motion planner to find a solution if one exists obstacle Resolution Completeness: As density increases a solution will be found if one exists goal state The search may not terminate if no solution exist Probabilistic Completeness: As the number of samples approaches the probability of finding a solution (if it exists) approaches unity start state 27 Resolution Completeness obstacle obstacle goal state goal state start state start state Increasing Density 28

15 Probabilistic Completeness obstacle goal state obstacle goal state start state start state Increasing Samples 29 Dispersion Dispersion is a measure of the largest gap in the search space Used to help quantify resolution of a search space metric δ(p )=sup{min{ρ(x, p)}} x X p P least upper bound (max if X is closed) Lower dispersion is desirable L 2 30

16 Search Space Design Grids and Lattices 31 Grid 1-Neighborhoods for an n-dimensional C-space (n 1) q = {1} q = {0} q = { 1, 0} q = {0, 1} q = {0, 0} q = {1, 0} q = { 1} q = {0, 1} N 1 (q) ={q + q,..., q + q n,q q 1,...,q q n } 1-Neighborhood (2n total neighbors) 32

17 n =2 Grid 2-Neighborhoods for an n-dimensional C-space (n 2) q = { 1, 1} q = { 1, 0} q = {0, 1} q = {1, 1} q = {0, 0} q = {1, 0} q = {1, 1} q = { 1, 1} q = {0, 1} N 2 (q) ={q ± q i ± q j 1 i, j n, i = j} N 1 (q) 2-Neighborhood n =3 33 Grid k-neighborhoods for an n-dimensional C-space (n k) n =1 n =2 n =3 k-neighborhood 3 n -1 total neighbors 34

18 Expressiveness vs. Efficiency High expressiveness can slow search Low expressiveness can miss solutions obstacle obstacle goal state goal state start state start state 35 Expressiveness vs. Efficiency High expressiveness can slow search Low expressiveness can miss solutions goal state goal state start state start state 36

19 Resolving Resolution Issues Iterative refinement until a solution is found Restart search with a denser representation goal state goal state goal state start state start state Adapt search space to the environment start state More on this in Search Spaces Part II 37 SE(2) Grid Search Space Example 1-Neighborhood connectivity +θ π/2 π/2 π/2 (0, 0, 0)

20 SE(2) Grid Search Space Example 1-Neighborhood connectivity +θ π/2 π/2 π/2 (0, 0, 0) q I =(0.0, 0.0, 0.0) q G =(1.0, 0.2, π/2) 39 SE(2) Grid Search Space Example 1-Neighborhood connectivity +θ π/2 π/2 π/2 (0, 0, 0) q I =(0.0, 0.0, 0.0) q G =(0.6, 0.0, 3π/2) 40

21 Search Space Design Rapidly Exploring Dense Trees 41 Rapidly Exploring Dense Trees Technique uses random or deterministic sampling to grow edges of the search space from branches [LaValle99a] SIMPLE_RDT(q0) Algorithm from Figure 5.16 in [LaValle 06a] 1 G.init(q0); 2 for i = 1 to k do 3 G.add_vertex(α(i)); 4 qn NEAREST(S(G),α(i)); 5 G.add_edge(qn,α(i)); Increasing Iterations 42

22 Rapidly Exploring Dense Trees RDT at Various Stages During Exploration Image from Figure 5.19 in [LaValle 06a] Image from Figure 5.19 in [LaValle 06a] 45 Iterations 2345 Iterations 43 Rapidly Exploring Dense Trees In the presence of obstacles edges expand only to the obstacle boundary Algorithm from Figure 5.21 in [LaValle 06a] RDT(q0) 1 G.init(q0); 2 for i = 1 to k do 3 qn NEAREST(S,α(i)); 4 qs STOPPING-CONFIG(qn,α(i)); 5 if( qs qn ) then 6 G.add_vertex(qs); 7 G.add_edge(qn,qs); obstacle 44

23 Rapidly Exploring Dense Trees Single-Tree Search Tree Expansion Bi-Directional Search Tree Expansion Tree Expansion 45 Search Space Design Roadmaps 46

24 Probabilistic Roadmap (PRM) Motion planning with Probabilistic Roadmaps [Kavraki96a] are a two-step process Preprocessing Phase Node Selection Edge Generation Query Phase obstacle rejected Graph Search new node new edge(s) new node neighborhood (radius method) 47 Visibility Roadmap Visibility Roadmap [Siméon00a] similar in concept to a Probabilistic Roadmap Connectivity attempted to all nodes V (q 1 ) q 1 q 4 q 3 V (q 3 ) Selection based on more restrictive rules obstacle q 6 Guards cannot see other guards Connectors connect distinct regions q 5 connectors V (q 2 ) q 2 guards 48

25 Summary Combining ideas from topology and geometric transformations allows us to define a configuration space for a system in 2 and 3 dimensions Search space design is generally about achieving completeness while and computational cost Deterministic and probabilistic search spaces have different degrees of guaranteed completeness Introduced grids, rapidly-exploring dense trees, and roadmaps for search space design 49 Next Lecture (4/7) Search Spaces II Search Spaces with Differential Constraints Applications Costing C-Space Expansion Binary Valued Obstacle Representations Continuous Valued Obstacle Representation 50

26 Document/Image References [Howard 09a] T.M. Howard, Adaptive Model- Predictive Motion Planning for Navigation in Complex Environments. Ph.D. Thesis, Carnegie Mellon University, August 2009 [LaValle99a] S. M. LaValle and J. J. Kuffner. Randomized kinodynamic planning. In Proceedings IEEE International Conference on Robotics and Automation, pages , [LaValle 06a] S. LaValle, Planning Algorithms. Cambridge: Cambridge University Press. ISBN [Kavraki96a] L. E. Kavraki, P. Svestka, J.-C. Latombe, and M. H. Overmars. Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics & Automation, 12(4): , June [Siméon00a] T. Siméon, J.-P. Laumond, and C. Nissoux. Visibility based probabilistic roadmaps for motion planning. Advanced Robotics, 14(6),