Johnny Fernandes
165 lines
5.3 KiB
Python
165 lines
5.3 KiB
Python
"""Fast 2D LiDAR simulator for the Gymnasium env.
|
|
|
|
Raycasts against sheep (discs) and static world geometry (axis-aligned
|
|
walls + gate posts) so the env reproduces the false-positive cluster
|
|
distribution Webots produces from real 3D geometry.
|
|
|
|
Returns a range array matching the Webots Lidar device:
|
|
180 rays, 140° FOV centred on forward, 12 m max range, 5 mm noise.
|
|
See ``protos/ShepherdDog.proto``.
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
import math
|
|
|
|
import numpy as np
|
|
|
|
|
|
# Match protos/ShepherdDog.proto Lidar device.
|
|
LIDAR_N_RAYS = 180
|
|
LIDAR_FOV = 2.44 # rad ≈ 140°
|
|
LIDAR_MAX_RANGE = 12.0
|
|
LIDAR_NOISE = 0.005 # m, gaussian std
|
|
|
|
# Sheep cross-section in the LiDAR plane (horizontal cylinder approx).
|
|
SHEEP_RADIUS = 0.30
|
|
|
|
|
|
# --- Static world geometry — mirrors worlds/field.wbt ---
|
|
|
|
# Vertical walls: (x, y_min, y_max).
|
|
_VERTICAL_WALLS = (
|
|
( 15.0, -15.0, 15.0), # field east
|
|
(-15.0, -15.0, 15.0), # field west
|
|
( 10.0, -22.0, -15.0), # pen west
|
|
( 13.0, -22.0, -15.0), # pen east
|
|
)
|
|
|
|
# Horizontal walls: (y, x_min, x_max). South wall has a 3 m gap at the gate.
|
|
_HORIZONTAL_WALLS = (
|
|
( 15.0, -15.0, 15.0), # field north
|
|
(-15.0, -15.0, 10.0), # field south-west of gate
|
|
(-15.0, 13.0, 15.0), # field south-east of gate
|
|
(-22.0, 10.0, 13.0), # pen south
|
|
)
|
|
|
|
# Gate posts + field corner pillars, treated as discs at LiDAR height.
|
|
_POSTS_XY = np.array([
|
|
( 10.0, -15.0), ( 13.0, -15.0),
|
|
( 15.0, 15.0), ( 15.0, -15.0),
|
|
(-15.0, 15.0), (-15.0, -15.0),
|
|
], dtype=np.float64)
|
|
POST_RADIUS = 0.25
|
|
|
|
|
|
def ray_angles(n: int = LIDAR_N_RAYS, fov: float = LIDAR_FOV) -> np.ndarray:
|
|
"""Local-frame ray angles, CCW from forward, sweeping +fov/2 → -fov/2.
|
|
|
|
Matches Webots' default Lidar sweep direction.
|
|
"""
|
|
return np.linspace(fov / 2.0, -fov / 2.0, n, dtype=np.float64)
|
|
|
|
|
|
_ANGLES = ray_angles()
|
|
_COS = np.cos(_ANGLES)
|
|
_SIN = np.sin(_ANGLES)
|
|
|
|
|
|
def _raycast_static(
|
|
ox: float, oy: float, cos_w: np.ndarray, sin_w: np.ndarray,
|
|
) -> np.ndarray:
|
|
"""Per-ray distance to the nearest wall or post hit (∞ if none)."""
|
|
n_rays = cos_w.shape[0]
|
|
best = np.full(n_rays, np.inf, dtype=np.float64)
|
|
|
|
EPS = 1e-3
|
|
safe_cos = np.where(np.abs(cos_w) < 1e-9, 1e-9, cos_w)
|
|
safe_sin = np.where(np.abs(sin_w) < 1e-9, 1e-9, sin_w)
|
|
|
|
# Vertical walls (x = const)
|
|
for wx, ymin, ymax in _VERTICAL_WALLS:
|
|
t = (wx - ox) / safe_cos
|
|
y_at = oy + t * sin_w
|
|
valid = (t > EPS) & (y_at >= ymin - EPS) & (y_at <= ymax + EPS)
|
|
cand = np.where(valid, t, np.inf)
|
|
np.minimum(best, cand, out=best)
|
|
|
|
# Horizontal walls (y = const)
|
|
for wy, xmin, xmax in _HORIZONTAL_WALLS:
|
|
t = (wy - oy) / safe_sin
|
|
x_at = ox + t * cos_w
|
|
valid = (t > EPS) & (x_at >= xmin - EPS) & (x_at <= xmax + EPS)
|
|
cand = np.where(valid, t, np.inf)
|
|
np.minimum(best, cand, out=best)
|
|
|
|
# Posts (treat as discs)
|
|
if _POSTS_XY.size:
|
|
px = _POSTS_XY[:, 0] - ox
|
|
py = _POSTS_XY[:, 1] - oy
|
|
t_post = np.outer(px, cos_w) + np.outer(py, sin_w) # (P, N)
|
|
d2 = (px ** 2 + py ** 2)[:, None] # (P, 1)
|
|
perp2 = d2 - t_post ** 2
|
|
R2 = POST_RADIUS ** 2
|
|
hit = (perp2 < R2) & (t_post > 0.0)
|
|
half = np.sqrt(np.clip(R2 - perp2, 0.0, None))
|
|
cand = np.where(hit, t_post - half, np.inf)
|
|
nearest = cand.min(axis=0)
|
|
np.minimum(best, nearest, out=best)
|
|
|
|
return best
|
|
|
|
|
|
def simulate_scan(
|
|
dog_x: float, dog_y: float, dog_heading: float,
|
|
sheep_xy: list[tuple[float, float]],
|
|
noise: float = LIDAR_NOISE,
|
|
max_range: float = LIDAR_MAX_RANGE,
|
|
rng: np.random.Generator | None = None,
|
|
) -> np.ndarray:
|
|
"""Return a (N,) float32 range array. No-hit entries equal ``max_range``.
|
|
|
|
``sheep_xy`` is every sheep (penned or active) in the scene.
|
|
"""
|
|
n_rays = _ANGLES.shape[0]
|
|
|
|
ch, sh = math.cos(dog_heading), math.sin(dog_heading)
|
|
cos_w = ch * _COS - sh * _SIN
|
|
sin_w = sh * _COS + ch * _SIN
|
|
|
|
# Walls + posts
|
|
best = _raycast_static(dog_x, dog_y, cos_w, sin_w)
|
|
|
|
# Sheep discs
|
|
if sheep_xy:
|
|
sx = np.asarray([p[0] for p in sheep_xy], dtype=np.float64) - dog_x
|
|
sy = np.asarray([p[1] for p in sheep_xy], dtype=np.float64) - dog_y
|
|
t = np.outer(sx, cos_w) + np.outer(sy, sin_w)
|
|
s_dist2 = (sx ** 2 + sy ** 2)[:, None]
|
|
perp2 = s_dist2 - t ** 2
|
|
R2 = SHEEP_RADIUS ** 2
|
|
hit = (perp2 < R2) & (t > 0.0)
|
|
half = np.sqrt(np.clip(R2 - perp2, 0.0, None))
|
|
candidate = np.where(hit, t - half, np.inf)
|
|
nearest = candidate.min(axis=0)
|
|
np.minimum(best, nearest, out=best)
|
|
|
|
# Entries with no hit stay at inf → clipped to max_range, matching Webots.
|
|
ranges = np.minimum(best, max_range).astype(np.float32)
|
|
return _add_noise(ranges, noise, rng, max_range)
|
|
|
|
|
|
def _add_noise(ranges: np.ndarray, sigma: float,
|
|
rng: np.random.Generator | None, max_range: float) -> np.ndarray:
|
|
if sigma <= 0.0:
|
|
return ranges
|
|
if rng is None:
|
|
rng = np.random.default_rng()
|
|
hit_mask = ranges < max_range - 1e-3
|
|
n_hit = int(hit_mask.sum())
|
|
if n_hit:
|
|
ranges = ranges.copy()
|
|
ranges[hit_mask] += rng.normal(0.0, sigma, size=n_hit).astype(np.float32)
|
|
np.clip(ranges, 0.0, max_range, out=ranges)
|
|
return ranges
|