Checkpoint 6
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@@ -24,7 +24,7 @@ from __future__ import annotations
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import math
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from herding.control import modulate_speed_near_sheep
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from herding.control.modulation import modulate_speed_near_sheep
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INITIAL_SCAN_STEPS = 80 # ≈1.3 s at dt=16 ms — full rotation at the +π turn target.
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@@ -24,7 +24,7 @@ flock size and works up to at least n=10 within a 15 000-step budget.
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import math
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from herding.geometry import GATE_Y, PEN_ENTRY, in_pen
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from herding.world.geometry import GATE_Y, PEN_ENTRY, in_pen
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DELTA_DRIVE = 1.5 # standoff behind the target sheep
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@@ -9,7 +9,7 @@ Reference: Strömbom et al. 2014, "Solving the shepherding problem".
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import math
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from herding.geometry import PEN_ENTRY, GATE_Y, in_pen
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from herding.world.geometry import PEN_ENTRY, GATE_Y, in_pen
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# Algorithm parameters. DELTA_DRIVE / DELTA_COLLECT were tightened from
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# the original (4.0 / 2.5) because the new external pen sits ~26 m from
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+1
-1
@@ -31,7 +31,7 @@ Layout (all components normalised so values stay roughly in [-1, 1]):
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import math
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import numpy as np
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from herding.geometry import (
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from herding.world.geometry import (
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FIELD_X, FIELD_Y, PEN_ENTRY, MAX_SHEEP,
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)
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@@ -29,8 +29,8 @@ import math
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import numpy as np
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from herding.geometry import FIELD_X, FIELD_Y, GATE_Y, PEN_X, PEN_Y
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from herding.lidar_sim import (
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from herding.world.geometry import FIELD_X, FIELD_Y, GATE_Y, PEN_X, PEN_Y
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from herding.perception.lidar_sim import (
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LIDAR_FOV, LIDAR_MAX_RANGE, LIDAR_N_RAYS, SHEEP_RADIUS, ray_angles,
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)
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@@ -26,7 +26,7 @@ from __future__ import annotations
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import math
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from herding.geometry import MAX_SHEEP, in_pen, is_penned_position
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from herding.world.geometry import MAX_SHEEP, in_pen, is_penned_position
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GATE_M = 2.5 # m — primary NN gate (recent tracks)
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@@ -51,7 +51,7 @@ is a defensible engineering adaptation of Strömbom's qualitative
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import math
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import random
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from herding.geometry import (
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from herding.world.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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