Drake C++ Documentation

Detailed Description

Provides an abstraction for reasoning about geometry in optimization problems, and using optimization problems to solve geometry problems.

This feature is considered to be experimental and may change or be removed at any time, without any deprecation notice ahead of time.

Relationship to other components in Drake.

SceneGraph handles many types of geometry (see Shape). It is specialized to 3D and puts a strong emphasis on efficient implementation of a particular subset of geometry queries, like collision detection and signed-distance queries. SceneGraph also provides a lot of valuable tools for content management, including parsing geometries from file (via multibody::Parser) and Role.

MathematicalProgram has many relevant solvers::Cost / solvers::Constraint for reasoning about geometry (e.g., the LorentzCone or even LinearConstraint). The class and methods in this group add a level of modeling power above these individual constraints (there are many different types of constraints one would write given various optimization on these sets).

The geometry::optimization tools support:


class  AffineSubspace
 An affine subspace (also known as a "flat", a "linear variety", or a "linear manifold") is a vector subspace of some Euclidean space, potentially translated so as to not pass through the origin. More...
class  CartesianProduct
 The Cartesian product of convex sets is a convex set: S = X₁ × X₂ × ⋯ × Xₙ = {(x₁, x₂, ..., xₙ) | x₁ ∈ X₁, x₂ ∈ X₂, ..., xₙ ∈ Xₙ}. More...
class  ConvexSet
 Abstract base class for defining a convex set. More...
class  GraphOfConvexSets
 GraphOfConvexSets (GCS) implements the design pattern and optimization problems first introduced in the paper "Shortest Paths in Graphs of Convex Sets". More...
class  HPolyhedron
 Implements a polyhedral convex set using the half-space representation: {x| A x ≤ b}. More...
class  Hyperellipsoid
 Implements an ellipsoidal convex set represented by the quadratic form {x | (x-center)ᵀAᵀA(x-center) ≤ 1}. More...
class  Intersection
 A convex set that represents the intersection of multiple sets: S = X₁ ∩ X₂ ∩ ... More...
struct  IrisOptions
 Configuration options for the IRIS algorithm. More...
class  MinkowskiSum
 A convex set that represents the Minkowski sum of multiple sets: S = X₁ ⨁ X₂ ⨁ ... More...
class  Point
 A convex set that contains exactly one element. More...
class  Spectrahedron
 Implements a spectrahedron (the feasible set of a semidefinite program). More...
class  VPolytope
 A polytope described using the vertex representation. More...


HPolyhedron Iris (const ConvexSets &obstacles, const Eigen::Ref< const Eigen::VectorXd > &sample, const HPolyhedron &domain, const IrisOptions &options=IrisOptions())
 The IRIS (Iterative Region Inflation by Semidefinite programming) algorithm, as described in. More...
ConvexSets MakeIrisObstacles (const QueryObject< double > &query_object, std::optional< FrameId > reference_frame=std::nullopt)
 Constructs ConvexSet representations of obstacles for IRIS in 3D using the geometry from a SceneGraph QueryObject. More...
HPolyhedron IrisInConfigurationSpace (const multibody::MultibodyPlant< double > &plant, const systems::Context< double > &context, const IrisOptions &options=IrisOptions())
 A variation of the Iris (Iterative Region Inflation by Semidefinite programming) algorithm which finds collision-free regions in the configuration space of plant. More...

Function Documentation

◆ Iris()

HPolyhedron drake::geometry::optimization::Iris ( const ConvexSets obstacles,
const Eigen::Ref< const Eigen::VectorXd > &  sample,
const HPolyhedron domain,
const IrisOptions options = IrisOptions() 

The IRIS (Iterative Region Inflation by Semidefinite programming) algorithm, as described in.

R. L. H. Deits and R. Tedrake, “Computing large convex regions of obstacle-free space through semidefinite programming,” Workshop on the Algorithmic Fundamentals of Robotics, Istanbul, Aug. 2014. http://groups.csail.mit.edu/robotics-center/public_papers/Deits14.pdf

This algorithm attempts to locally maximize the volume of a convex polytope representing obstacle-free space given a sample point and list of convex obstacles. Rather than compute the volume of the polytope directly, the algorithm maximizes the volume of an inscribed ellipsoid. It alternates between finding separating hyperplanes between the ellipsoid and the obstacles and then finding a new maximum-volume inscribed ellipsoid.

obstaclesis a vector of convex sets representing the occupied space.
sampleprovides a point in the space; the algorithm is initialized using a tiny sphere around this point. The algorithm is only guaranteed to succeed if this sample point is collision free (outside of all obstacles), but in practice the algorithm can often escape bad initialization (assuming the require_sample_point_is_contained option is false).
domaindescribes the total region of interest; computed IRIS regions will be inside this domain. It must be bounded, and is typically a simple bounding box (e.g. from HPolyhedron::MakeBox).

The obstacles, sample, and the domain must describe elements in the same ambient dimension (but that dimension can be any positive integer).

◆ IrisInConfigurationSpace()

HPolyhedron drake::geometry::optimization::IrisInConfigurationSpace ( const multibody::MultibodyPlant< double > &  plant,
const systems::Context< double > &  context,
const IrisOptions options = IrisOptions() 

A variation of the Iris (Iterative Region Inflation by Semidefinite programming) algorithm which finds collision-free regions in the configuration space of plant.

See also
Iris for details on the original algorithm. This variant uses nonlinear optimization (instead of convex optimization) to find collisions in configuration space; each potential collision is probabilistically "certified" by restarting the nonlinear optimization from random initial seeds inside the candidate IRIS region until it fails to find a collision in options.num_collision_infeasible_samples consecutive attempts.
plantdescribes the kinematics of configuration space. It must be connected to a SceneGraph in a systems::Diagram.
contextis a context of the plant. The context must have the positions of the plant set to the initialIRIS seed configuration.
optionsprovides additional configuration options. In particular, increasing options.num_collision_infeasible_samples increases the chances that the IRIS regions are collision free but can also significantly increase the run-time of the algorithm. The same goes for options.num_additional_constraints_infeasible_samples.
std::exceptionif the sample configuration in context is infeasible.

◆ MakeIrisObstacles()

ConvexSets drake::geometry::optimization::MakeIrisObstacles ( const QueryObject< double > &  query_object,
std::optional< FrameId reference_frame = std::nullopt 

Constructs ConvexSet representations of obstacles for IRIS in 3D using the geometry from a SceneGraph QueryObject.

All geometry in the scene with a proximity role, both anchored and dynamic, are consider to be fixed obstacles frozen in the poses captured in the context used to create the QueryObject.

When multiple representations are available for a particular geometry (e.g. a Box can be represented as either an HPolyhedron or a VPolytope), then this method will prioritize the representation that we expect is most performant for the current implementation of the IRIS algorithm.