# ETS.ik_LM

ETS.ik_LM(Tep, q0=None, ilimit=30, slimit=100, tol=1e-06, mask=None, joint_limits=True, k=1.0, method='chan')[source]

Fast levenberg-Marquadt Numerical Inverse Kinematics Solver

A method which provides functionality to perform numerical inverse kinematics (IK) using the Levemberg-Marquadt method. This is a fast solver implemented in C++.

See the Inverse Kinematics Docs Page for more details and for a tutorial on numerical IK, see here.

Parameters:
Return type:

Synopsis

The operation is defined by the choice of the method kwarg.

The step is deined as

$\begin{split}\vec{q}_{k+1} &= \vec{q}_k + \left( \mat{A}_k \right)^{-1} \bf{g}_k \\ % \mat{A}_k &= {\mat{J}(\vec{q}_k)}^\top \mat{W}_e \ {\mat{J}(\vec{q}_k)} + \mat{W}_n\end{split}$

where $$\mat{W}_n = \text{diag}(\vec{w_n})(\vec{w_n} \in \mathbb{R}^n_{>0})$$ is a diagonal damping matrix. The damping matrix ensures that $$\mat{A}_k$$ is non-singular and positive definite. The performance of the LM method largely depends on the choice of $$\mat{W}_n$$.

Chan’s Method

Chan proposed

$\mat{W}_n = λ E_k \mat{1}_n$

where λ is a constant which reportedly does not have much influence on performance. Use the kwarg k to adjust the weighting term λ.

Sugihara’s Method

Sugihara proposed

$\mat{W}_n = E_k \mat{1}_n + \text{diag}(\hat{\vec{w}}_n)$

where $$\hat{\vec{w}}_n \in \mathbb{R}^n$$, $$\hat{w}_{n_i} = l^2 \sim 0.01 l^2$$, and $$l$$ is the length of a typical link within the manipulator. We provide the variable k as a kwarg to adjust the value of $$w_n$$.

Wampler’s Method

Wampler proposed $$\vec{w_n}$$ to be a constant. This is set through the k kwarg.

Examples

The following example gets the ets of a panda robot object, makes a goal pose Tep, and then solves for the joint coordinates which result in the pose Tep using the ikine_LM method.

>>> import roboticstoolbox as rtb
>>> panda = rtb.models.Panda().ets()
>>> Tep = panda.fkine([0, -0.3, 0, -2.2, 0, 2, 0.7854])
>>> panda.ikine_LM(Tep)
IKSolution(q=array([ 1.2871, -0.6995, -1.0234, -2.1592, -0.6372,  1.8055,  1.2807]), success=True, iterations=9, searches=1, residual=2.030855426926995e-10, reason='Success')


Notes

The value for the k kwarg will depend on the method chosen and the arm you are using. Use the following as a rough guide chan, k = 1.0 - 0.01, wampler, k = 0.01 - 0.0001, and sugihara, k = 0.1 - 0.0001

When using the this method, the initial joint coordinates $$q_0$$, should correspond to a non-singular manipulator pose, since it uses the manipulator Jacobian.

This class supports null-space motion to assist with maximising manipulability and avoiding joint limits. These are enabled by setting kq and km to non-zero values.

References

• J. Haviland, and P. Corke. “Manipulator Differential Kinematics Part I: Kinematics, Velocity, and Applications.” arXiv preprint arXiv:2207.01796 (2022).

• J. Haviland, and P. Corke. “Manipulator Differential Kinematics Part II: Acceleration and Advanced Applications.” arXiv preprint arXiv:2207.01794 (2022).

ik_NR
ik_GN