Inheritance diagram for StateVectorTpl< _Scalar >:

Public Types
typedef MathBaseTpl< Scalar >	MathBase

typedef MathBase::MatrixXs	MatrixXs

typedef _Scalar	Scalar

typedef MathBase::VectorXs	VectorXs

Public Types inherited from StateAbstractTpl< _Scalar >
typedef MathBaseTpl< Scalar >	MathBase

typedef MathBase::MatrixXs	MatrixXs

typedef MathBase::VectorXs	VectorXs

Public Member Functions
	StateVectorTpl (const std::size_t nx)

virtual void	diff (const Eigen::Ref< const VectorXs > &x0, const Eigen::Ref< const VectorXs > &x1, Eigen::Ref< VectorXs > dxout) const
	Compute the state manifold differentiation.

virtual void	integrate (const Eigen::Ref< const VectorXs > &x, const Eigen::Ref< const VectorXs > &dx, Eigen::Ref< VectorXs > xout) const
	Compute the state manifold integration.

virtual void	Jdiff (const Eigen::Ref< const VectorXs > &, const Eigen::Ref< const VectorXs > &, Eigen::Ref< MatrixXs > Jfirst, Eigen::Ref< MatrixXs > Jsecond, const Jcomponent firstsecond=both) const
	Compute the Jacobian of the state manifold differentiation.

virtual void	Jintegrate (const Eigen::Ref< const VectorXs > &x, const Eigen::Ref< const VectorXs > &dx, Eigen::Ref< MatrixXs > Jfirst, Eigen::Ref< MatrixXs > Jsecond, const Jcomponent firstsecond=both, const AssignmentOp=setto) const
	Compute the Jacobian of the state manifold integration.

virtual void	JintegrateTransport (const Eigen::Ref< const VectorXs > &x, const Eigen::Ref< const VectorXs > &dx, Eigen::Ref< MatrixXs > Jin, const Jcomponent firstsecond) const
	Parallel transport from integrate(x, dx) to x.

virtual VectorXs	rand () const
	Generate a random state.

virtual VectorXs	zero () const
	Generate a zero state.

Public Member Functions inherited from StateAbstractTpl< _Scalar >
	StateAbstractTpl (const std::size_t nx, const std::size_t ndx)
	Initialize the state dimensions.

VectorXs	diff_dx (const Eigen::Ref< const VectorXs > &x0, const Eigen::Ref< const VectorXs > &x1)
	Compute the state manifold differentiation.

bool	get_has_limits () const
	Indicate if the state has defined limits.

const VectorXs &	get_lb () const
	Return the state lower bound.

std::size_t	get_ndx () const
	Return the dimension of the tangent space of the state manifold.

std::size_t	get_nq () const
	Return the dimension of the configuration tuple.

std::size_t	get_nv () const
	Return the dimension of tangent space of the configuration manifold.

std::size_t	get_nx () const
	Return the dimension of the state tuple.

const VectorXs &	get_ub () const
	Return the state upper bound.

VectorXs	integrate_x (const Eigen::Ref< const VectorXs > &x, const Eigen::Ref< const VectorXs > &dx)
	Compute the state manifold integration.

std::vector< MatrixXs >	Jdiff_Js (const Eigen::Ref< const VectorXs > &x0, const Eigen::Ref< const VectorXs > &x1, const Jcomponent firstsecond=both)

std::vector< MatrixXs >	Jintegrate_Js (const Eigen::Ref< const VectorXs > &x, const Eigen::Ref< const VectorXs > &dx, const Jcomponent firstsecond=both)
	Compute the Jacobian of the state manifold integration.

void	set_lb (const VectorXs &lb)
	Modify the state lower bound.

void	set_ub (const VectorXs &ub)
	Modify the state upper bound.

Additional Inherited Members
Public Attributes inherited from StateAbstractTpl< _Scalar >
EIGEN_MAKE_ALIGNED_OPERATOR_NEW typedef _Scalar	Scalar

Protected Member Functions inherited from StateAbstractTpl< _Scalar >
void	update_has_limits ()

Protected Attributes inherited from StateAbstractTpl< _Scalar >
bool	has_limits_
	Indicates whether any of the state limits is finite.

VectorXs	lb_
	Lower state limits.

std::size_t	ndx_
	State rate dimension.

std::size_t	nq_
	Configuration dimension.

std::size_t	nv_
	Velocity dimension.

std::size_t	nx_
	State dimension.

VectorXs	ub_
	Upper state limits.

Detailed Description

template<typename _Scalar>
class crocoddyl::StateVectorTpl< _Scalar >

Definition at line 19 of file euclidean.hpp.

Member Typedef Documentation

◆ Scalar

template<typename _Scalar >

typedef _Scalar Scalar

Definition at line 21 of file euclidean.hpp.

◆ MathBase

template<typename _Scalar >

typedef MathBaseTpl<Scalar> MathBase

Definition at line 22 of file euclidean.hpp.

◆ VectorXs

template<typename _Scalar >

typedef MathBase::VectorXs VectorXs

Definition at line 23 of file euclidean.hpp.

◆ MatrixXs

template<typename _Scalar >

typedef MathBase::MatrixXs MatrixXs

Definition at line 24 of file euclidean.hpp.

Member Function Documentation

◆ zero()

template<typename _Scalar >

virtual VectorXs zero ( ) const

virtual

Generate a zero state.

Implements StateAbstractTpl< _Scalar >.

◆ rand()

template<typename _Scalar >

virtual VectorXs rand ( ) const

virtual

Generate a random state.

Implements StateAbstractTpl< _Scalar >.

◆ diff()

template<typename _Scalar >

virtual void diff	(	const Eigen::Ref< const VectorXs > &	x0,
		const Eigen::Ref< const VectorXs > &	x1,
		Eigen::Ref< VectorXs >	dxout
	)		const

virtual

Compute the state manifold differentiation.

The state differentiation is defined as:

\begin{equation*} \delta\mathbf{x} = \mathbf{x}_{1} \ominus \mathbf{x}_{0}, \end{equation*}

where \(\mathbf{x}_{1}\), \(\mathbf{x}_{0}\) are the current and previous state which lie in a manifold \(\mathcal{M}\), and \(\delta\mathbf{x} \in T_\mathbf{x} \mathcal{M}\) is the rate of change in the state in the tangent space of the manifold.

Parameters

[in]	x0	Previous state point (size `nx`)
[in]	x1	Current state point (size `nx`)
[out]	dxout	Difference between the current and previous state points (size `ndx`)

Implements StateAbstractTpl< _Scalar >.

◆ integrate()

template<typename _Scalar >

virtual void integrate	(	const Eigen::Ref< const VectorXs > &	x,
		const Eigen::Ref< const VectorXs > &	dx,
		Eigen::Ref< VectorXs >	xout
	)		const

virtual

Compute the state manifold integration.

The state integration is defined as:

\begin{equation*} \mathbf{x}_{next} = \mathbf{x} \oplus \delta\mathbf{x}, \end{equation*}

where \(\mathbf{x}\), \(\mathbf{x}_{next}\) are the current and next state which lie in a manifold \(\mathcal{M}\), and \(\delta\mathbf{x} \in T_\mathbf{x} \mathcal{M}\) is the rate of change in the state in the tangent space of the manifold.

Parameters

[in]	x	State point (size `nx`)
[in]	dx	Velocity vector (size `ndx`)
[out]	xout	Next state point (size `nx`)

Implements StateAbstractTpl< _Scalar >.

◆ Jdiff()

template<typename _Scalar >

virtual void Jdiff	(	const Eigen::Ref< const VectorXs > &	x0,
		const Eigen::Ref< const VectorXs > &	x1,
		Eigen::Ref< MatrixXs >	Jfirst,
		Eigen::Ref< MatrixXs >	Jsecond,
		const Jcomponent	firstsecond = `both`
	)		const

virtual

Compute the Jacobian of the state manifold differentiation.

The state differentiation is defined as:

\begin{equation*} \delta\mathbf{x} = \mathbf{x}_{1} \ominus \mathbf{x}_{0}, \end{equation*}

where \(\mathbf{x}_{1}\), \(\mathbf{x}_{0}\) are the current and previous state which lie in a manifold \(\mathcal{M}\), and \(\delta\mathbf{x} \in T_\mathbf{x} \mathcal{M}\) is the rate of change in the state in the tangent space of the manifold.

The Jacobians lie in the tangent space of manifold, i.e. \(\mathbb{R}^{\textrm{ndx}\times\textrm{ndx}}\). Note that the state is represented as a tuple of nx values and its dimension is ndx. Calling \(\boldsymbol{\Delta}(\mathbf{x}_{0}, \mathbf{x}_{1}) \), the difference function, these Jacobians satisfy the following relationships:

\(\boldsymbol{\Delta}(\mathbf{x}_{0},\mathbf{x}_{0}\oplus\delta\mathbf{y}) - \boldsymbol{\Delta}(\mathbf{x}_{0},\mathbf{x}_{1}) = \mathbf{J}_{\mathbf{x}_{1}}\delta\mathbf{y} + \mathbf{o}(\mathbf{x}_{0})\).
\(\boldsymbol{\Delta}(\mathbf{x}_{0}\oplus\delta\mathbf{y},\mathbf{x}_{1}) - \boldsymbol{\Delta}(\mathbf{x}_{0},\mathbf{x}_{1}) = \mathbf{J}_{\mathbf{x}_{0}}\delta\mathbf{y} + \mathbf{o}(\mathbf{x}_{0})\),

where \(\mathbf{J}_{\mathbf{x}_{1}}\) and \(\mathbf{J}_{\mathbf{x}_{0}}\) are the Jacobian with respect to the current and previous state, respectively.

Parameters

[in]	x0	Previous state point (size `nx`)
[in]	x1	Current state point (size `nx`)
[out]	Jfirst	Jacobian of the difference operation relative to the previous state point (size `ndx` \(\times\)`ndx`)
[out]	Jsecond	Jacobian of the difference operation relative to the current state point (size `ndx` \(\times\)`ndx`)
[in]	firstsecond	Argument (either x0 and / or x1) with respect to which the differentiation is performed.

Implements StateAbstractTpl< _Scalar >.

◆ Jintegrate()

template<typename _Scalar >

virtual void Jintegrate	(	const Eigen::Ref< const VectorXs > &	x,
		const Eigen::Ref< const VectorXs > &	dx,
		Eigen::Ref< MatrixXs >	Jfirst,
		Eigen::Ref< MatrixXs >	Jsecond,
		const Jcomponent	firstsecond = `both`,
		const AssignmentOp	op = `setto`
	)		const

virtual

Compute the Jacobian of the state manifold integration.

The state integration is defined as:

\begin{equation*} \mathbf{x}_{next} = \mathbf{x} \oplus \delta\mathbf{x}, \end{equation*}

where \(\mathbf{x}\), \(\mathbf{x}_{next}\) are the current and next state which lie in a manifold \(\mathcal{M}\), and \(\delta\mathbf{x} \in T_\mathbf{x} \mathcal{M}\) is the rate of change in the state in the tangent space of the manifold.

The Jacobians lie in the tangent space of manifold, i.e. \(\mathbb{R}^{\textrm{ndx}\times\textrm{ndx}}\). Note that the state is represented as a tuple of nx values and its dimension is ndx. Calling \( \mathbf{f}(\mathbf{x}, \delta\mathbf{x}) \), the integrate function, these Jacobians satisfy the following relationships:

\(\mathbf{f}(\mathbf{x}\oplus\delta\mathbf{y},\delta\mathbf{x})\ominus\mathbf{f}(\mathbf{x},\delta\mathbf{x}) = \mathbf{J}_\mathbf{x}\delta\mathbf{y} + \mathbf{o}(\delta\mathbf{x})\).
\(\mathbf{f}(\mathbf{x},\delta\mathbf{x}+\delta\mathbf{y})\ominus\mathbf{f}(\mathbf{x},\delta\mathbf{x}) = \mathbf{J}_{\delta\mathbf{x}}\delta\mathbf{y} + \mathbf{o}(\delta\mathbf{x})\),

where \(\mathbf{J}_{\delta\mathbf{x}}\) and \(\mathbf{J}_{\mathbf{x}}\) are the Jacobian with respect to the state and velocity, respectively.

Parameters

[in]	x	State point (size `nx`)
[in]	dx	Velocity vector (size `ndx`)
[out]	Jfirst	Jacobian of the integration operation relative to the state point (size `ndx` \(\times\)`ndx`)
[out]	Jsecond	Jacobian of the integration operation relative to the velocity vector (size `ndx` \(\times\)`ndx`)
[in]	firstsecond	Argument (either x and / or dx) with respect to which the differentiation is performed
[in]	op	Assignment operator which sets, adds, or removes the given Jacobian matrix

Implements StateAbstractTpl< _Scalar >.

◆ JintegrateTransport()

template<typename _Scalar >

virtual void JintegrateTransport	(	const Eigen::Ref< const VectorXs > &	x,
		const Eigen::Ref< const VectorXs > &	dx,
		Eigen::Ref< MatrixXs >	Jin,
		const Jcomponent	firstsecond
	)		const

virtual

Parallel transport from integrate(x, dx) to x.

This function performs the parallel transportation of an input matrix whose columns are expressed in the tangent space at \(\mathbf{x}\oplus\delta\mathbf{x}\) to the tangent space at \(\mathbf{x}\) point.

Parameters

[in]	x	State point (size `nx`).
[in]	dx	Velocity vector (size `ndx`)
[out]	Jin	Input matrix (number of rows = `nv`).
[in]	firstsecond	Argument (either x or dx) with respect to which the differentiation of Jintegrate is performed.

Implements StateAbstractTpl< _Scalar >.

The documentation for this class was generated from the following files:

/root/robotpkg/optimization/py-crocoddyl/work/crocoddyl-2.1.0/include/crocoddyl/core/fwd.hpp
/root/robotpkg/optimization/py-crocoddyl/work/crocoddyl-2.1.0/include/crocoddyl/core/states/euclidean.hpp

Public Types

Public Member Functions

Additional Inherited Members

Detailed Description

Member Typedef Documentation

◆ Scalar

◆ MathBase

◆ VectorXs

◆ MatrixXs

Member Function Documentation

◆ zero()

◆ rand()

◆ diff()

◆ integrate()

◆ Jdiff()

◆ Jintegrate()

◆ JintegrateTransport()