ompl::control Namespace Reference

This namespace contains sampling based planning routines used by planning under differential constraints. More...

Classes
class	Automaton
	A class to represent a deterministic finite automaton, each edge of which corresponds to a World. A system trajectory, by way of project() and worldAtRegion() in PropositionalDecomposition, determines a sequence of Worlds, which are read by an Automaton to determine whether a trajectory satisfies a given specification. More...
class	CompoundControl
	Definition of a compound control. More...
class	CompoundControlSampler
	Definition of a compound control sampler. This is useful to construct samplers for compound controls. More...
class	CompoundControlSpace
	A control space to allow the composition of control spaces. More...
class	Control
	Definition of an abstract control. More...
class	ControlSampler
	Abstract definition of a control sampler. Motion planners that need to sample controls will call functions from this class. Planners should call the versions of sample() and sampleNext() with most arguments, whenever this information is available. More...
class	ControlSpace
	A control space representing the space of applicable controls. More...
class	Decomposition
	A Decomposition is a partition of a bounded Euclidean space into a fixed number of regions which are denoted by integers. More...
class	DirectedControlSampler
	Abstract definition of a directed control sampler. Motion planners that need to sample controls that take the system to a desired direction will call functions from this class. Planners should call the versions of sampleTo() with most arguments, whenever this information is available. If no direction information is available, the use of a ControlSampler is perhaps more appropriate. More...
class	DiscreteControlSampler
	Control space sampler for discrete controls. More...
class	DiscreteControlSpace
	A space representing discrete controls; i.e. there are a small number of discrete controls the system can react to. Controls are represented as integers [lowerBound, upperBound], where lowerBound and upperBound are inclusive. More...
class	EST
	Expansive Space Trees. More...
class	GridDecomposition
	A GridDecomposition is a Decomposition implemented using a grid. More...
class	KPIECE1
	Kinodynamic Planning by Interior-Exterior Cell Exploration. More...
class	LTLPlanner
	A planner for generating system trajectories to satisfy a logical specification given by an automaton, the propositions of which are defined over a decomposition of the system's state space. More...
class	LTLProblemDefinition
class	LTLSpaceInformation
class	MorseControlSpace
	Representation of controls applied in MORSE environments. This is an array of double values. More...
class	MorseSimpleSetup
	Create the set of classes typically needed to solve a control problem when forward propagation is computed with MORSE. More...
class	MorseStatePropagator
	State propagation with MORSE. Only forward propagation is possible. More...
class	ODEAdaptiveSolver
	Adaptive step size solver for ordinary differential equations of the type q' = f(q, u), where q is the current state of the system and u is a control applied to the system. The maximum integration error is bounded in this approach. Solver is the numerical integration method used to solve the equations, and must implement the error stepper concept from boost::numeric::odeint. The default is a fifth order Runge-Kutta Cash-Karp method with a fourth order error bound. More...
class	ODEBasicSolver
	Basic solver for ordinary differential equations of the type q' = f(q, u), where q is the current state of the system and u is a control applied to the system. StateType defines the container object describing the state of the system. Solver is the numerical integration method used to solve the equations. The default is a fourth order Runge-Kutta method. This class wraps around the simple stepper concept from boost::numeric::odeint. More...
class	ODEErrorSolver
	Solver for ordinary differential equations of the type q' = f(q, u), where q is the current state of the system and u is a control applied to the system. StateType defines the container object describing the state of the system. Solver is the numerical integration method used to solve the equations. The default is a fifth order Runge-Kutta Cash-Karp method with a fourth order error bound. This class wraps around the error stepper concept from boost::numeric::odeint. More...
class	ODESolver
	Abstract base class for an object that can solve ordinary differential equations (ODE) of the type q' = f(q,u) using numerical integration. Classes deriving from this must implement the solve method. The user must supply the ODE to solve. More...
class	OpenDEControlSpace
	Representation of controls applied in OpenDE environments. This is an array of double values. More...
class	OpenDEEnvironment
	This class contains the OpenDE constructs OMPL needs to know about when planning. More...
class	OpenDESimpleSetup
	Create the set of classes typically needed to solve a control problem when forward propagation is computed with OpenDE. More...
class	OpenDEStatePropagator
	State propagation with OpenDE. Only forward propagation is possible. More...
class	OpenDEStateSpace
	State space representing OpenDE states. More...
class	OpenDEStateValidityChecker
	The simplest state validity checker: all states are valid. More...
class	PathControl
	Definition of a control path. More...
class	PDST
	Path-Directed Subdivision Tree. More...
class	PlannerData
	Object containing planner generated vertex and edge data. It is assumed that all vertices are unique, and only a single directed edge connects two vertices. More...
class	PlannerDataEdgeControl
	Representation of an edge in PlannerData for planning with controls. This structure encodes a specific control and a duration to apply the control. More...
class	PlannerDataStorage
	Object that handles loading/storing a PlannerData object to/from a binary stream. Serialization of vertices and edges is performed using the Boost archive method serialize. Derived vertex/edge classes are handled, presuming those classes implement the serialize method. More...
class	ProductGraph
	A ProductGraph represents the weighted, directed, graph-based Cartesian product of a PropositionalDecomposition object, an Automaton corresponding to a co-safe LTL specification, and an Automaton corresponding to a safe LTL specification. More...
class	PropositionalDecomposition
	A propositional decomposition wraps a given Decomposition with a region-to-proposition assignment operator. Each region in the decomposition has a corresponding World. More...
class	PropositionalTriangularDecomposition
	A PropositionalTriangularDecomposition is a triangulation that ignores obstacles and respects propositional regions of interest. Practically speaking, it is both a TriangularDecomposition and a PropositionalDecomposition, but it is implemented without using multiple inheritance. More...
class	RealVectorControlSpace
	A control space representing Rn. More...
class	RealVectorControlUniformSampler
	Uniform sampler for the Rn state space. More...
class	RRT
	Rapidly-exploring Random Tree. More...
class	SimpleDirectedControlSampler
	Implementation of a simple directed control sampler. This is a basic implementation that does not actually take direction into account and builds upon ControlSampler. Instead, a set of k random controls are sampled, and the control that gets the system closest to the target state is returned. More...
class	SimpleSetup
	Create the set of classes typically needed to solve a control problem. More...
class	SpaceInformation
	Space information containing necessary information for planning with controls. setup() needs to be called before use. More...
class	SST
class	StatePropagator
	Model the effect of controls on system states. More...
class	SteeredControlSampler
	Abstract definition of a steered control sampler. It uses the steering function in a state propagator to find the controls that drive from one state to another. More...
class	Syclop
	Synergistic Combination of Layers of Planning. More...
class	SyclopEST
	SyclopEST is Syclop with EST as its low-level tree planner. More...
class	SyclopRRT
	SyclopRRT is Syclop with RRT as its low-level tree planner. More...
class	TriangularDecomposition
	A TriangularDecomposition is a triangulation that ignores obstacles. More...
class	World
	A class to represent an assignment of boolean values to propositions. A World can be partially restrictive, i.e., some propositions do not have to be assigned a value, in which case it can take on any value. Our notion of a World is similar to a set of truth assignments in propositional logic. More...

Typedefs
using	ControlSamplerAllocator = std::function< ControlSamplerPtr(const ControlSpace *)>
	Definition of a function that can allocate a control sampler.
using	DirectedControlSamplerAllocator = std::function< DirectedControlSamplerPtr(const SpaceInformation *)>
	Definition of a function that can allocate a directed control sampler.
using	StatePropagatorFn = std::function< void(const base::State *, const Control *, const double, base::State *)>
	A function that achieves state propagation.

Enumerations
enum	ControlSpaceType { CONTROL_SPACE_UNKNOWN = 0, CONTROL_SPACE_REAL_VECTOR = 1, CONTROL_SPACE_DISCRETE = 2, CONTROL_SPACE_TYPE_COUNT }
	The type of a control space. More...

Functions
std::ostream &	operator<< (std::ostream &out, const ProductGraph::State &s)

Detailed Description

This namespace contains sampling based planning routines used by planning under differential constraints.

Enumeration Type Documentation

ControlSpaceType

enum ompl::control::ControlSpaceType

The type of a control space.

Enumerator
CONTROL_SPACE_UNKNOWN	Unset type; this is the default type.
CONTROL_SPACE_REAL_VECTOR	ompl::control::RealVectorControlSpace
CONTROL_SPACE_DISCRETE	ompl::control::DiscreteControlSpace
CONTROL_SPACE_TYPE_COUNT	Number of control space types; To add new types, use values that are larger than the count.

Definition at line 109 of file ControlSpaceTypes.h.