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fix(ik): unify ik_XX/ikine_XX return types and fix IK docs (#652)
* fix(ik): add IKSolution.__getitem__/__repr__, fix docstring typos IKSolution had __iter__ but no __getitem__, so positional indexing (sol[0], sol[1], ...) -- the pattern every existing caller and the old bare-tuple return used -- raised TypeError. Add __getitem__ matching __iter__'s order, and a __repr__ matching the existing custom __str__ instead of the verbose default dataclass repr. Also fixes "Levemberg-Marquadt"/"Marquadt" -> "Levenberg-Marquardt" and "progamming" -> "programming", present throughout this file's docstrings. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> * fix(ik): unify ik_LM/ik_NR/ik_GN's return type with IKSolution ik_LM/ik_NR/ik_GN (the fast C++-backed solvers) returned a bare 5-tuple while ikine_LM/ikine_NR/ikine_GN (the pure-Python solvers) already returned IKSolution -- same family of methods, two different return shapes. Wrap the C++ tuple in IKSolution in both ETS.ik_LM/ ik_NR/ik_GN and their RobotKinematics forwarders, and update the return-type annotations and :returns:/:rtype: docstring fields to match (ikine_LM/ikine_NR/ikine_GN/ikine_QP were missing :returns:/ :rtype: entirely -- added those too). Also: - bidirectionally cross-reference each ik_XX with its ikine_XX counterpart (previously only cross-referenced their C++ siblings) - add a loud warning to ik_LM/ik_NR/ik_GN's docstrings that they require the compiled C++ extension and raise RuntimeError without it (e.g. pure-Python builds, Pyodide/JupyterLite) - fix several copy-paste bugs in RobotKinematics.py found while doing this: ik_GN's own "See Also" listed itself instead of ik_LM/ik_NR, ikine_GN's and ikine_QP's listed the wrong solver class entirely (IK_NR instead of IK_GN/IK_QP), and two runblock examples said "ikine_GN"/"ikine_LM" while actually calling ik_NR/ik_LM/ik_GN - fix "Levemberg-Marquadt"/"Marquadt" -> "Levenberg-Marquardt" and "deined" -> "defined" throughout both files Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> * fix(dhrobot): remove 5 completely broken dead IK methods DHRobot.ik_lm_chan/ik_lm_wampler/ik_lm_sugihara/ik_nr/ik_gn all forwarded to self.ets().<same-name>(...), but ETS has never had methods by these names (only the unified ik_LM/ik_NR/ik_GN, each taking a method= kwarg where relevant) -- every one of these five methods raises AttributeError unconditionally on any call. Their docstrings even have literal ":seealso: TODO" placeholders. No test exercises any of them. Confirmed dead: nothing in tests/ or docs/ references any of the five; the only caller was examples/ik_exp.py (fixed separately). Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> * fix(examples): repair ik_exp.py to use the real ik_NR/ik_GN/ik_LM API Called the now-removed dead ets.ik_nr/ik_gn/ik_lm_chan/ik_lm_wampler/ ik_lm_sugihara methods, which never existed on ETS in the first place (same root cause as the DHRobot dead-method removal). Rewired to the real ik_NR/ik_GN/ik_LM(method=...) API with matching parameter names, switched from raw 5-tuple unpacking to IKSolution attribute access (the old unpacking would have silently broken now that these methods return a 6-field IKSolution instead of a 5-tuple), and dropped several entirely unused imports (fknm, swift, spatialgeometry, sys, and unused typing names). Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> * docs: fix Levenberg-Marquardt spelling in IK docs Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> * test(ik): add regression tests for IKSolution/ik_XX return-type changes Covers IKSolution.__getitem__/__repr__, and that ik_LM/ik_NR/ik_GN now return real IKSolution instances rather than a bare tuple. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> --------- Co-authored-by: Claude Sonnet 5 <noreply@anthropic.com>
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‎docs/source/IK/ik_lm.rst‎

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IK_LM - Levemberg-Marquadt Numerical IK
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---------------------------------------
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IK_LM - Levenberg-Marquardt Numerical IK
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----------------------------------------
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.. currentmodule:: roboticstoolbox.robot.IK
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‎docs/source/intro.rst‎

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@@ -103,7 +103,7 @@ All robots can generate a random joint configuration informed by joint limits, i
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>>> puma.random_q()
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``ikine_LM`` is a generalised iterative numerical solution based on
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Levenberg-Marquadt minimization, and additional status results are also
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Levenberg-Marquardt minimization, and additional status results are also
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returned as part of a named tuple.
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.. warning::

‎examples/ik_exp.py‎

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import numpy as np
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import roboticstoolbox as rtb
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import spatialmath as sm
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import fknm
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import time
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import swift
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import spatialgeometry as sg
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import sys
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from ansitable import ANSITable
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from numpy import ndarray
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from spatialmath import SE3
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from typing import Union, overload, List, Set
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# Our robot and ETS
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robot = rtb.models.Panda()
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ets = robot.ets()
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tol = 1e-6
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# Reject solutions with invalid joint limits
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reject_jl = True
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joint_limits = True
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class IK:
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solvers = [
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# IK(
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# "Newton Raphson",
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# lambda Tep: ets.ik_nr(
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# Tep,
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# q0=None,
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# ilimit=ilimit,
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# slimit=slimit,
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# tol=tol,
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# reject_jl=reject_jl,
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# we=we,
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# use_pinv=False,
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# pinv_damping=0.0,
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# ),
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# ),
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# IK(
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# "Gauss Newton",
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# lambda Tep: ets.ik_gn(
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# Tep,
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# q0=None,
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# ilimit=ilimit,
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# slimit=slimit,
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# tol=tol,
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# reject_jl=reject_jl,
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# we=we,
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# use_pinv=False,
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# pinv_damping=0.0,
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# ),
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# ),
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IK(
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"Newton Raphson Pinv",
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lambda Tep: ets.ik_nr(
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lambda Tep: ets.ik_NR(
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Tep,
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q0=None,
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ilimit=ilimit,
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slimit=slimit,
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tol=tol,
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reject_jl=reject_jl,
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we=we,
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use_pinv=True,
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joint_limits=joint_limits,
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mask=we,
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pinv=True,
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pinv_damping=0.0,
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),
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),
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IK(
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"Gauss Newton Pinv",
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lambda Tep: ets.ik_gn(
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lambda Tep: ets.ik_GN(
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Tep,
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q0=None,
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ilimit=ilimit,
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slimit=slimit,
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tol=tol,
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reject_jl=reject_jl,
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we=we,
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use_pinv=True,
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joint_limits=joint_limits,
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mask=we,
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pinv=True,
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pinv_damping=0.0,
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),
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),
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IK(
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"LM Chan 0.1",
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lambda Tep: ets.ik_lm_chan(
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lambda Tep: ets.ik_LM(
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Tep,
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q0=None,
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ilimit=ilimit,
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slimit=slimit,
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tol=tol,
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reject_jl=reject_jl,
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we=we,
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λ=0.1,
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joint_limits=joint_limits,
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mask=we,
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k=0.1,
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method="chan",
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),
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),
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# IK(
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# "LM Chan 1.0",
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# lambda Tep: ets.ik_lm_chan(
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# Tep,
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# q0=None,
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# ilimit=ilimit,
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# slimit=slimit,
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# tol=tol,
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# reject_jl=reject_jl,
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# we=we,
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# λ=1.0,
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# ),
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# ),
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# IK(
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# "LM Wampler",
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# lambda Tep: ets.ik_lm_wampler(
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# Tep,
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# q0=None,
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# ilimit=ilimit,
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# slimit=slimit,
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# tol=tol,
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# reject_jl=reject_jl,
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# we=we,
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# λ=1e-2,
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# ),
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# ),
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IK(
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"LM Wampler 1e-4",
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lambda Tep: ets.ik_lm_wampler(
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lambda Tep: ets.ik_LM(
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Tep,
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q0=None,
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ilimit=ilimit,
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slimit=slimit,
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tol=tol,
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reject_jl=reject_jl,
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we=we,
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λ=1e-4,
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joint_limits=joint_limits,
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mask=we,
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k=1e-4,
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method="wampler",
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),
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),
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# IK(
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# "LM Wampler 1e-6",
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# lambda Tep: ets.ik_lm_wampler(
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# Tep,
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# q0=None,
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# ilimit=ilimit,
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# slimit=slimit,
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# tol=tol,
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# reject_jl=reject_jl,
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# we=we,
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# λ=1e-6,
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# ),
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# ),
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# IK(
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# "LM Sugihara 0.001",
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# lambda Tep: ets.ik_lm_sugihara(
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# Tep,
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# q0=None,
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# ilimit=ilimit,
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# slimit=slimit,
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# tol=tol,
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# reject_jl=reject_jl,
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# we=we,
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# λ=0.001,
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# ),
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# ),
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# IK(
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# "LM Sugihara 0.01",
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# lambda Tep: ets.ik_lm_sugihara(
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# Tep,
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# q0=None,
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# ilimit=ilimit,
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# slimit=slimit,
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# tol=tol,
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# reject_jl=reject_jl,
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# we=we,
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# λ=0.01,
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# ),
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# ),
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IK(
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"LM Sugihara 0.1",
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lambda Tep: ets.ik_lm_sugihara(
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lambda Tep: ets.ik_LM(
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Tep,
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q0=None,
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ilimit=ilimit,
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slimit=slimit,
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tol=tol,
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reject_jl=reject_jl,
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we=we,
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λ=0.1,
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joint_limits=joint_limits,
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mask=we,
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k=0.1,
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method="sugihara",
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),
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),
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]
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print(i + 1)
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for solver in solvers:
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_, success, iterations, searches, residual = solver.solve(Tep[i])
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sol = solver.solve(Tep[i])
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if success:
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solver.success[i] = success
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solver.iterations[i] = iterations
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solver.searches[i] = searches
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solver.residual[i] = residual
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if sol.success:
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solver.success[i] = sol.success
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solver.iterations[i] = sol.iterations
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solver.searches[i] = sol.searches
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solver.residual[i] = sol.residual
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solver.total_iterations += solver.iterations[i]
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solver.total_searches += solver.searches[i]
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else:

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