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from roboticstoolbox .robot .ET import ET , ET2
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from spatialmath .base import getvector
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from spatialmath import SE3
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- from typing import Union , overload , List , Set
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+ from typing import Union , overload , List , Set , Tuple
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from sys import version_info
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ArrayLike = Union [list , ndarray , tuple , set ]
@@ -1513,7 +1513,7 @@ def ik_lm_chan(
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reject_jl : bool = True ,
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we : Union [ndarray , None ] = None ,
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λ : float = 1.0 ,
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- ) -> tuple [ndarray , int , int , int , float ]:
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+ ) -> Tuple [ndarray , int , int , int , float ]:
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"""
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Numerical inverse kinematics by Levenberg-Marquadt optimization (Chan's Method)
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@@ -1618,7 +1618,7 @@ def ik_lm_wampler(
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reject_jl : bool = True ,
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we : Union [ndarray , None ] = None ,
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λ : float = 1.0 ,
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- ) -> tuple [ndarray , int , int , int , float ]:
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+ ) -> Tuple [ndarray , int , int , int , float ]:
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"""
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Numerical inverse kinematics by Levenberg-Marquadt optimization (Chan's Method)
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@@ -1723,7 +1723,7 @@ def ik_lm_sugihara(
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reject_jl : bool = True ,
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we : Union [ndarray , None ] = None ,
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λ : float = 1.0 ,
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- ) -> tuple [ndarray , int , int , int , float ]:
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+ ) -> Tuple [ndarray , int , int , int , float ]:
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"""
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Numerical inverse kinematics by Levenberg-Marquadt optimization (Chan's Method)
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@@ -1816,7 +1816,9 @@ def ik_lm_sugihara(
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TODO
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"""
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- return IK_LM_Sugihara (self ._fknm , Tep , q0 , ilimit , slimit , tol , reject_jl , we , λ )
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+ return IK_LM_Sugihara (
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+ self ._fknm , Tep , q0 , ilimit , slimit , tol , reject_jl , we , λ
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+ )
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def ik_nr (
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self ,
@@ -1829,7 +1831,7 @@ def ik_nr(
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we : Union [ndarray , None ] = None ,
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use_pinv : int = True ,
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pinv_damping : float = 0.0 ,
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- ) -> tuple [ndarray , int , int , int , float ]:
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+ ) -> Tuple [ndarray , int , int , int , float ]:
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"""
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Numerical inverse kinematics by Levenberg-Marquadt optimization (Chan's Method)
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@@ -1946,7 +1948,7 @@ def ik_gn(
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we : Union [ndarray , None ] = None ,
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use_pinv : int = True ,
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pinv_damping : float = 0.0 ,
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- ) -> tuple [ndarray , int , int , int , float ]:
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+ ) -> Tuple [ndarray , int , int , int , float ]:
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"""
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Numerical inverse kinematics by Levenberg-Marquadt optimization (Chan's Method)
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