Ellipsoid Module¶
- class gpdmink.Ellipsoid.Ellipsoid(a, quat, tc)¶
Bases:
SuperQuadrics- get_closest_point_to_ellipsoid(eB)¶
Compute closest point to another ellipsoid. E. Rimon and S. P. Boyd, “Obstacle collision detection using best ellipsoid fit,” J. Intell. Robot. Syst., vol. 18, no. 2, pp. 105–126, 1997.
- Parameters:
eB (Ellipsoid) – Target ellipsoid object
- Returns:
Tuple containing closest point and related values
- Return type:
tuple
- get_gradient_from_cartesian(x)¶
Convert surface point to its gradient.
- Parameters:
x (numpy.ndarray) – Cartesian point
- Returns:
Gradient vector
- Return type:
numpy.ndarray
- get_gradient_from_hypersphere(u)¶
Convert hypersphere parameter to gradient.
- Parameters:
u (numpy.ndarray) – Hypersphere parameter
- Returns:
Gradient vector
- Return type:
numpy.ndarray
- get_hypersphere_from_gradient(m)¶
Convert gradient to hypersphere parameter.
- Parameters:
m (numpy.ndarray) – Gradient vector
- Returns:
Hypersphere parameter
- Return type:
numpy.ndarray
- get_implicit_function_canonical(x)¶
Compute implicit function value in canonical frame.
- Parameters:
x (numpy.ndarray) – Point in canonical frame
- Returns:
Implicit function value
- Return type:
float
- get_point_from_gradient(m)¶
Convert gradient to surface point in canonical frame.
- Parameters:
m (numpy.ndarray) – Gradient vector
- Returns:
Surface point
- Return type:
numpy.ndarray
- get_point_from_normal(n)¶
Convert normal vector to surface point.
- Parameters:
n (numpy.ndarray) – Normal vector
- Returns:
Surface point
- Return type:
numpy.ndarray