Johnny Fernandes
194 lines
6.8 KiB
Python
194 lines
6.8 KiB
Python
"""Fast 2D LiDAR simulator for the Gymnasium env.
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Raycasts against:
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* **Sheep** — discs of radius ``SHEEP_RADIUS``.
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* **Static world geometry** — axis-aligned wall segments and gate
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posts taken from ``worlds/field.wbt``. Without these, demos
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collected in-env would never include the false-positive clusters
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Webots produces from the stone walls and gate-post boxes, and the
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BC student trained on those demos collapses on deployment.
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Returns a range array matching the Webots Lidar device on the dog
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(see ``protos/ShepherdDog.proto``: 180 rays, 140° FOV centred on
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forward, 12 m max range, 5 mm noise).
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"""
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from __future__ import annotations
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import math
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import numpy as np
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# Match protos/ShepherdDog.proto Lidar device.
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LIDAR_N_RAYS = 180
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LIDAR_FOV = 2.44 # rad ≈ 140°
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LIDAR_MAX_RANGE = 12.0
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LIDAR_NOISE = 0.005 # m, gaussian std
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# Sheep modelled as a vertical cylinder; this is the horizontal-section
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# radius the LiDAR plane intersects. Tuned to the proto sheep (~0.45 m
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# body length). The exact value is not load-bearing — the perception
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# clusterer is range-tolerant.
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SHEEP_RADIUS = 0.30
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# ---------------------------------------------------------------------------
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# Static world geometry — must match worlds/field.wbt
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# ---------------------------------------------------------------------------
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# Vertical walls: (x, y_min, y_max). Field east/west walls and the two
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# pen side walls are visible through the open gate.
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_VERTICAL_WALLS = (
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( 15.0, -15.0, 15.0), # field east
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(-15.0, -15.0, 15.0), # field west
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( 10.0, -22.0, -15.0), # pen west
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( 13.0, -22.0, -15.0), # pen east
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)
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# Horizontal walls: (y, x_min, x_max). South wall is split by the 3 m
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# gate at x ∈ [10, 13]; the pen south wall closes the back of the pen.
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_HORIZONTAL_WALLS = (
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( 15.0, -15.0, 15.0), # field north
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(-15.0, -15.0, 10.0), # field south-west of gate
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(-15.0, 13.0, 15.0), # field south-east of gate
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(-22.0, 10.0, 13.0), # pen south
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)
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# Gate posts and field corner pillars treated as vertical cylinders at
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# LiDAR height. Radius 0.25 m comes from the 0.44 × 0.44 m boxes in the
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# wbt — close enough to a circular cross-section for this purpose.
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_POSTS_XY = np.array([
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( 10.0, -15.0), # west gate post
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( 13.0, -15.0), # east gate post
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( 15.0, 15.0), # NE field corner
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( 15.0, -15.0), # SE field corner
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(-15.0, 15.0), # NW field corner
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(-15.0, -15.0), # SW field corner
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], dtype=np.float64)
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POST_RADIUS = 0.25
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def ray_angles(n: int = LIDAR_N_RAYS, fov: float = LIDAR_FOV) -> np.ndarray:
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"""Local-frame ray angles, sweeping from +fov/2 to -fov/2.
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Convention: angle is measured CCW from the dog's forward axis. Ray 0
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points to the dog's left, last ray to the right. Webots' default
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Lidar sweep matches this.
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"""
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return np.linspace(fov / 2.0, -fov / 2.0, n, dtype=np.float64)
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# Cached so we don't rebuild every step.
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_ANGLES = ray_angles()
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_COS = np.cos(_ANGLES)
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_SIN = np.sin(_ANGLES)
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def _raycast_static(
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ox: float, oy: float, cos_w: np.ndarray, sin_w: np.ndarray,
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) -> np.ndarray:
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"""Per-ray distance to nearest wall or post hit (∞ if none).
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Walls are axis-aligned line segments; for each ray we compute t at
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which it crosses the wall's constant-coord plane and check the
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other coord lies in the segment. Posts are circles; same disc
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intersection as for sheep.
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"""
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n_rays = cos_w.shape[0]
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best = np.full(n_rays, np.inf, dtype=np.float64)
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EPS = 1e-3
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safe_cos = np.where(np.abs(cos_w) < 1e-9, 1e-9, cos_w)
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safe_sin = np.where(np.abs(sin_w) < 1e-9, 1e-9, sin_w)
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# Vertical walls (x = const)
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for wx, ymin, ymax in _VERTICAL_WALLS:
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t = (wx - ox) / safe_cos
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y_at = oy + t * sin_w
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valid = (t > EPS) & (y_at >= ymin - EPS) & (y_at <= ymax + EPS)
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cand = np.where(valid, t, np.inf)
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np.minimum(best, cand, out=best)
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# Horizontal walls (y = const)
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for wy, xmin, xmax in _HORIZONTAL_WALLS:
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t = (wy - oy) / safe_sin
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x_at = ox + t * cos_w
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valid = (t > EPS) & (x_at >= xmin - EPS) & (x_at <= xmax + EPS)
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cand = np.where(valid, t, np.inf)
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np.minimum(best, cand, out=best)
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# Posts (treat as discs)
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if _POSTS_XY.size:
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px = _POSTS_XY[:, 0] - ox
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py = _POSTS_XY[:, 1] - oy
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t_post = np.outer(px, cos_w) + np.outer(py, sin_w) # (P, N)
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d2 = (px ** 2 + py ** 2)[:, None] # (P, 1)
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perp2 = d2 - t_post ** 2
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R2 = POST_RADIUS ** 2
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hit = (perp2 < R2) & (t_post > 0.0)
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half = np.sqrt(np.clip(R2 - perp2, 0.0, None))
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cand = np.where(hit, t_post - half, np.inf)
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nearest = cand.min(axis=0)
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np.minimum(best, nearest, out=best)
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return best
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def simulate_scan(
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dog_x: float, dog_y: float, dog_heading: float,
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sheep_xy: list[tuple[float, float]],
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noise: float = LIDAR_NOISE,
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max_range: float = LIDAR_MAX_RANGE,
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rng: np.random.Generator | None = None,
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) -> np.ndarray:
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"""Return a (N,) float32 range array. No-hit entries equal ``max_range``.
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``sheep_xy`` is the list of (x, y) world positions of every sheep in
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the scene (penned and active). Static world geometry (walls and
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posts) is also raycast so demos contain the same false-positive
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clusters Webots produces.
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"""
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n_rays = _ANGLES.shape[0]
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ch, sh = math.cos(dog_heading), math.sin(dog_heading)
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cos_w = ch * _COS - sh * _SIN
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sin_w = sh * _COS + ch * _SIN
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# Walls + posts
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best = _raycast_static(dog_x, dog_y, cos_w, sin_w)
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# Sheep discs
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if sheep_xy:
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sx = np.asarray([p[0] for p in sheep_xy], dtype=np.float64) - dog_x
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sy = np.asarray([p[1] for p in sheep_xy], dtype=np.float64) - dog_y
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t = np.outer(sx, cos_w) + np.outer(sy, sin_w)
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s_dist2 = (sx ** 2 + sy ** 2)[:, None]
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perp2 = s_dist2 - t ** 2
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R2 = SHEEP_RADIUS ** 2
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hit = (perp2 < R2) & (t > 0.0)
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half = np.sqrt(np.clip(R2 - perp2, 0.0, None))
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candidate = np.where(hit, t - half, np.inf)
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nearest = candidate.min(axis=0)
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np.minimum(best, nearest, out=best)
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# Clip to LIDAR_MAX_RANGE; entries that never got a hit stay at inf
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# → clipped down to max_range like the real Webots device.
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ranges = np.minimum(best, max_range).astype(np.float32)
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return _add_noise(ranges, noise, rng, max_range)
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def _add_noise(ranges: np.ndarray, sigma: float,
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rng: np.random.Generator | None, max_range: float) -> np.ndarray:
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if sigma <= 0.0:
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return ranges
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if rng is None:
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rng = np.random.default_rng()
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hit_mask = ranges < max_range - 1e-3
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n_hit = int(hit_mask.sum())
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if n_hit:
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ranges = ranges.copy()
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ranges[hit_mask] += rng.normal(0.0, sigma, size=n_hit).astype(np.float32)
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np.clip(ranges, 0.0, max_range, out=ranges)
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return ranges
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