NumPy Lissajous trajectory implementation.
Classes:
| Name | Description |
|---|---|
LissajousTrajectory |
Three-dimensional Lissajous trajectory. |
dataclass
LissajousTrajectory(yaw_from_velocity: bool = True, fixed_yaw: float = 0.0, attitude: tuple[float, float] = (0.0, 0.0), body_rate: tuple[float, float] = (0.0, 0.0), center: tuple[float, float, float] = (0.0, 0.0, 1.0), amplitude: tuple[float, float, float] = (1.0, 1.0, 0.2), frequency: tuple[float, float, float] = (1.0, 2.0, 0.5), phase: tuple[float, float, float] = (0.0, np.pi / 2.0, 0.0), omega: float = 0.5)
Bases: SpatialTrajectory
Three-dimensional Lissajous trajectory.
Methods:
| Name | Description |
|---|---|
__post_init__ |
Cache vector-valued parameters as float32 NumPy arrays. |
sample |
Sample the Lissajous trajectory at time |
sample_heading |
Sample yaw and yaw-rate references for time |
build_target |
Assemble a full 12D target from position, velocity, and acceleration. |
sample_batch |
Sample a batch of timestamps and stack the resulting 12D targets. |
__post_init__()
Cache vector-valued parameters as float32 NumPy arrays.
sample(t: float) -> np.ndarray
Sample the Lissajous trajectory at time t.
sample_heading(t: float, pos: ndarray, vel: ndarray, acc: ndarray) -> tuple[float, float]
Sample yaw and yaw-rate references for time t and translation state.
build_target(t: float, pos: ndarray, vel: ndarray, acc: ndarray) -> np.ndarray
Assemble a full 12D target from position, velocity, and acceleration.
sample_batch(ts: ndarray) -> np.ndarray
Sample a batch of timestamps and stack the resulting 12D targets.