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TermTaskspaceGeometricJacobian #190
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Hi @awa152, yes, this term is going to fail if the Jacobian is singular, therefore it is not optimal to use it. You could consider adding some Tikhonov style regularization scheme to this term, if you wanted. |
I have a few questions regarding the Taskspace costfunction term you provide in the rbd module:
https://github.com/ethz-adrl/control-toolbox/blob/v3.0.2/ct_rbd/include/ct/rbd/robot/costfunction/TermTaskspaceGeometricJacobian.hpp
especially concerning the analytical derivatives.
stateDerivative
method you use the geometric Jacobian to get the gradient. For the position part it seems clear, but is it valid for the orientation if the geometric Jacobian is used instead of an analytical one?In the book Robotics: Modelling, planning and control (Siciliano et. al 2009) I found the orientation error in terms of angle and axis as
. Is this the same?
stateSecondDerivative
method the Jacobian is also used in the Hessian. Does that mean that a Newton step with this costfunction term would fail if the Jacobian is singular?Sorry if my questions are somehow naive and thank you for your help!
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