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Derivative of Jacobian #138

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Hello,

The forward kinematics and jacobian are generated procedurally and numerically within the API, and so we do not have a symbolic function for either of these that we can expose. However, you should be able to numerically differentiate the Jacobian matrix or double-differentiate the forward kinematics. In these cases, you can perturb with a small joint angle perturbation dTheta around the current angle position, and use the resulting matrices to generate your numerical derivative. For example:

dJ(theta)/dTheta = (J(theta + dTheta) - J(theta)) / dTheta

Depending on your requirements, you can make this derivative unbiased by centering your Jacobian computations around theta instead of …

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