Checkpoint 2
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Johnny Fernandes
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"""Reynolds-style sheep flocking dynamics.
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This is the per-sheep behavioural step used both by the Webots sheep
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controller (scalar, one sheep at a time) and by the training environment
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(loop over sheep). The numerics are adapted from the original
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``controllers/sheep/flocking.py`` and retuned for the new external-pen
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layout: the south stone wall is intact except in the gate column, so
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sheep can only reach the pen by walking through that 3-m corridor.
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Force stack each step (summed → heading + speed):
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flee — quadratic ramp away from dog within FLEE_DIST
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cohesion — drift toward flock centre, halved while fleeing
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separation — inverse-distance push from peers
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walls — soft repulsion + hard escape band against field walls,
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except inside the gate column where the south wall is
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absent
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wander — small persistent drift for natural idle motion
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A sheep latches to ``penned`` the first time it crosses the gate plane
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into the gate column (handled by callers via ``geometry.is_penned_position``);
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once latched, ``penned=True`` is passed in here and the force stack
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switches to in-pen containment + jitter.
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"""
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import math
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import random
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from herding.geometry import (
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FIELD_X, FIELD_Y,
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PEN_X, PEN_Y,
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GATE_X,
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)
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# --- Speed and force constants ---
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# All speeds here are in wheel rad/s (motor units), matching the existing
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# sheep controller. Conversion to m/s = speed * SHEEP_WHEEL_RADIUS.
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MAX_SPEED = 22.0
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FLEE_SPEED = 20.0
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WANDER_SPEED = 3.0
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WALL_MARGIN = 5.0
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WALL_HARD_MARGIN = 1.0
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WALL_HARD_GAIN = 50.0
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FLEE_DIST = 7.0
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SEPARATION_DIST = 2.5
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COHESION_DIST = 8.0
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PEN_MARGIN = 0.8
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def _peers_iter(peers):
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"""Accept either a {name: (x, y)} dict or an iterable of (x, y) tuples."""
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if isinstance(peers, dict):
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return list(peers.values())
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return list(peers)
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def compute_heading_speed(x, y, penned, dog_xy, peers, wander_angle, rng=None):
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"""Return ``(heading, speed, new_wander_angle)`` for one sheep step.
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``speed`` is in wheel rad/s (motor units), bounded by ``[WANDER_SPEED,
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FLEE_SPEED]``. ``heading`` is the world-frame target heading the sheep
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should aim for (atan2 convention).
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``rng`` is an optional ``random.Random``-compatible object used for
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the wander-jitter. If ``None``, falls back to Python's global module
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(matches Webots controller usage). Pass an env-owned RNG to make
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rollouts deterministic given a seed.
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"""
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fx, fy = 0.0, 0.0
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peer_list = _peers_iter(peers)
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rnd = rng if rng is not None else random
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if penned:
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# --- Pen containment: bounce off the four pen walls ---
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pm = PEN_MARGIN
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if x < PEN_X[0] + pm:
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fx += ((PEN_X[0] + pm - x) / pm) * 15.0
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if x > PEN_X[1] - pm:
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fx -= ((x - (PEN_X[1] - pm)) / pm) * 15.0
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if y < PEN_Y[0] + pm:
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fy += ((PEN_Y[0] + pm - y) / pm) * 15.0
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if y > PEN_Y[1] - pm:
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fy -= ((y - (PEN_Y[1] - pm)) / pm) * 15.0
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# Mild peer separation — penned sheep crowd the corner otherwise.
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for px, py in peer_list:
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dx, dy = px - x, py - y
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d = math.hypot(dx, dy)
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if 0.05 < d < SEPARATION_DIST:
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push = (SEPARATION_DIST - d) / d
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fx -= (dx / d) * push * 2.5
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fy -= (dy / d) * push * 2.5
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if rnd.random() < 0.02:
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wander_angle += rnd.uniform(-0.6, 0.6)
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fx += math.cos(wander_angle) * 0.5
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fy += math.sin(wander_angle) * 0.5
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else:
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# --- Free-roaming sheep in the field ---
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fleeing = False
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if dog_xy is not None:
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ddx = dog_xy[0] - x
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ddy = dog_xy[1] - y
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dist = math.hypot(ddx, ddy)
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if 0.01 < dist < FLEE_DIST:
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fleeing = True
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t = 1.0 - dist / FLEE_DIST
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s = t * t * 20.0
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fx -= (ddx / dist) * s
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fy -= (ddy / dist) * s
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# Cohesion — drift toward flock CoM (peers within COHESION_DIST).
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# Cohesion is *stronger* under flee than at rest (the
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# predator-confusion / safety-in-numbers effect — sheep huddle when
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# threatened). This is what makes shepherding work: the flock stays
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# as one unit through the narrow gate instead of fragmenting.
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cx, cy, cn = 0.0, 0.0, 0
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for px, py in peer_list:
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d = math.hypot(px - x, py - y)
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if 0.3 < d < COHESION_DIST:
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cx += px
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cy += py
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cn += 1
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if cn > 0:
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# Cohesion needs to be comparable to flee at close range to keep
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# the flock together through narrow obstacles like the 3m gate.
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# Flee at 2m has magnitude ~10; cohesion at peer-distance 5m
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# with w=1.5 contributes ~7.5 — same order, so the flock
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# translates as a unit instead of fragmenting under pressure.
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w = 1.5 if fleeing else 0.6
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fx += (cx / cn - x) * w
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fy += (cy / cn - y) * w
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# Separation — inverse-distance push from peers.
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for px, py in peer_list:
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ddx, ddy = px - x, py - y
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d = math.hypot(ddx, ddy)
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if 0.05 < d < SEPARATION_DIST:
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push = (SEPARATION_DIST - d) / d
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fx -= (ddx / d) * push * 2.5
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fy -= (ddy / d) * push * 2.5
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# Wall soft repulsion. The south wall is absent inside the gate
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# column so sheep can be driven through it by the dog.
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if x < FIELD_X[0] + WALL_MARGIN:
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fx += ((FIELD_X[0] + WALL_MARGIN - x) / WALL_MARGIN) * 6.0
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if x > FIELD_X[1] - WALL_MARGIN:
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fx -= ((x - (FIELD_X[1] - WALL_MARGIN)) / WALL_MARGIN) * 6.0
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if y > FIELD_Y[1] - WALL_MARGIN:
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fy -= ((y - (FIELD_Y[1] - WALL_MARGIN)) / WALL_MARGIN) * 6.0
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if y < FIELD_Y[0] + WALL_MARGIN and not (GATE_X[0] <= x <= GATE_X[1]):
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fy += ((FIELD_Y[0] + WALL_MARGIN - y) / WALL_MARGIN) * 6.0
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if not fleeing:
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if random.random() < 0.02:
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wander_angle += random.uniform(-0.6, 0.6)
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fx += math.cos(wander_angle) * 0.5
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fy += math.sin(wander_angle) * 0.5
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# --- Hard escape band — overrides everything when very close to a wall ---
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m, g = WALL_HARD_MARGIN, WALL_HARD_GAIN
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if x - FIELD_X[0] < m:
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fx = max(fx, g * (1.0 - (x - FIELD_X[0]) / m))
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if FIELD_X[1] - x < m:
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fx = min(fx, -g * (1.0 - (FIELD_X[1] - x) / m))
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if FIELD_Y[1] - y < m:
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fy = min(fy, -g * (1.0 - (FIELD_Y[1] - y) / m))
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# South wall hard escape only when not in the gate column and not penned.
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if (not penned) and (y - FIELD_Y[0] < m) and not (GATE_X[0] <= x <= GATE_X[1]):
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fy = max(fy, g * (1.0 - (y - FIELD_Y[0]) / m))
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heading = math.atan2(fy, fx)
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mag = math.hypot(fx, fy)
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speed = max(WANDER_SPEED, min(FLEE_SPEED, mag * 3.0))
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return heading, speed, wander_angle
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