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Copy file name to clipboardExpand all lines: docs/src/examples/robot.md
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@@ -130,13 +130,14 @@ fkine(prob.u0, prob.p, 0)
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!!! note
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The function `fkine` above takes the full state of the robot model, as opposed to only the joint angles.
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### Jacobian
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We can compute the Jacobian ``J`` of the forward-kinematics function using the package ForwardDiff (this Jacobian is often referred to as the _analytical Jacobian_, which in the 6DOF case is different from the _geometrical Jacobian_ that is used in the relation ``v = J\dot{q}``). The Jacobian of the end-effector positional coordinates will be a 3×36 matrix, since we have 36-dimensional state of the robot after simplification. Since the end-effector coordinates do not depend on all the state variables, we may ask which variables it depends on by finding non-zero columns of ``J``
We see that the end-effector position depends on all mechanical angles except for the last one, which is expected since the end-effector origin is on the axis of rotation of joint 6.
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```@setup
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# ### Jacobian # Temporarily deactivated
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# We can compute the Jacobian ``J`` of the forward-kinematics function using the package ForwardDiff (this Jacobian is often referred to as the _analytical Jacobian_, which in the 6DOF case is different from the _geometrical Jacobian_ that is used in the relation ``v = J\dot{q}``). The Jacobian of the end-effector positional coordinates will be a 3×36 matrix, since we have 36-dimensional state of the robot after simplification. Since the end-effector coordinates do not depend on all the state variables, we may ask which variables it depends on by finding non-zero columns of ``J``
# We see that the end-effector position depends on all mechanical angles except for the last one, which is expected since the end-effector origin is on the axis of rotation of joint 6.
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