IK_NR.step

IK_NR.step(ets, Tep, q)[source]

Performs a single iteration of the Newton-Raphson optimisation method

\[\vec{q}_{k+1} = \vec{q}_k + {^0\mat{J}(\vec{q}_k)}^{-1} \vec{e}_k\]
Parameters:

  • ets (ETS) – The ETS representing the manipulators kinematics

  • Tep (ndarray) – The desired end-effector pose

  • q (ndarray) – The current joint coordinate vector

Raises:

numpy.LinAlgError – If a step is impossible due to a linear algebra error

Return type:

Tuple[float, ndarray]

Returns:

  • E – The new error value

  • q – The new joint coordinate vector