Defines task space constraints for use in `KineTrajOpt`.

Classes:

 `PoseConstraint`(wrobot, linkid, target_pose) Yet another way to impose pose constraint from the moment perspective `DirectionConstraint`(wrobot, linkid[, …]) Constraints to keep one direction up `OrientationConstraint`(robot, linkid, R) Keeps a link at some orientation `PositionConstraint`(wrobot, linkid, lcl_pos, …) To constrain the position of a link local position

Functions:

 `MomentDerivative`(m, R, z) Compute the derivative of rotation vector given the value, rotation, and angular velocity
class `klampt.plan.kinetrajopt.trajopt_task_space.``PoseConstraint`(wrobot, linkid, target_pose)[source]

Yet another way to impose pose constraint from the moment perspective

Methods:

 `compute`(x[, grad_level]) Evaluates the constraint function and possibly (determining on grad_level) the Jacobian.
`compute`(x, grad_level=0)[source]

Evaluates the constraint function and possibly (determining on grad_level) the Jacobian.

Parameters
• x (ndarray) – the point to be evaluated

• grad_level (int, optional) – which level of gradient is computed. Defaults to 0.

Returns

The constraint value and optional derivatives, structured as follows:

• If grad_level == 0, it returns a 1-D ndarray of g(x)

• If grad_level == 1, it also returns a 2-D ndarray giving the constraint Jacobian d/dx g(x).

Return type

tuple

`klampt.plan.kinetrajopt.trajopt_task_space.``uncross`(R)[source]
`klampt.plan.kinetrajopt.trajopt_task_space.``Sinc`(x)[source]
`klampt.plan.kinetrajopt.trajopt_task_space.``Sinc_Dx`(x)[source]
`klampt.plan.kinetrajopt.trajopt_task_space.``MomentDerivative`(m, R, z)[source]

Compute the derivative of rotation vector given the value, rotation, and angular velocity

Parameters
• m (arr) – the rotation vector

• R (arr) – the rotation matrix

• z (arr) – the angular velocity

Returns

the dereivative

Return type

dm ([arr])

class `klampt.plan.kinetrajopt.trajopt_task_space.``DirectionConstraint`(wrobot, linkid, lcl_dir=[0, 0, 1], world_dir=[0, 0, 1])[source]

Constraints to keep one direction up

Methods:

 `compute`(x[, grad_level]) Evaluates the constraint function and possibly (determining on grad_level) the Jacobian.
`compute`(x, grad_level=0)[source]

Evaluates the constraint function and possibly (determining on grad_level) the Jacobian.

Parameters
• x (ndarray) – the point to be evaluated

• grad_level (int, optional) – which level of gradient is computed. Defaults to 0.

Returns

The constraint value and optional derivatives, structured as follows:

• If grad_level == 0, it returns a 1-D ndarray of g(x)

• If grad_level == 1, it also returns a 2-D ndarray giving the constraint Jacobian d/dx g(x).

Return type

tuple

class `klampt.plan.kinetrajopt.trajopt_task_space.``OrientationConstraint`(robot, linkid, R)[source]

Keeps a link at some orientation

Methods:

 `compute`(x[, grad_level]) Evaluates the constraint function and possibly (determining on grad_level) the Jacobian.
`compute`(x, grad_level=0)[source]

Evaluates the constraint function and possibly (determining on grad_level) the Jacobian.

Parameters
• x (ndarray) – the point to be evaluated

• grad_level (int, optional) – which level of gradient is computed. Defaults to 0.

Returns

The constraint value and optional derivatives, structured as follows:

• If grad_level == 0, it returns a 1-D ndarray of g(x)

• If grad_level == 1, it also returns a 2-D ndarray giving the constraint Jacobian d/dx g(x).

Return type

tuple

class `klampt.plan.kinetrajopt.trajopt_task_space.``PositionConstraint`(wrobot, linkid, lcl_pos, world_pos)[source]

To constrain the position of a link local position

Methods:

 `compute`(x[, grad_level]) Evaluates the constraint function and possibly (determining on grad_level) the Jacobian.
`compute`(x, grad_level=0)[source]

Evaluates the constraint function and possibly (determining on grad_level) the Jacobian.

Parameters
• x (ndarray) – the point to be evaluated

• grad_level (int, optional) – which level of gradient is computed. Defaults to 0.

Returns

The constraint value and optional derivatives, structured as follows:

• If grad_level == 0, it returns a 1-D ndarray of g(x)

• If grad_level == 1, it also returns a 2-D ndarray giving the constraint Jacobian d/dx g(x).

Return type

tuple