ykcheungab
bcc55a3b97
朝上判断误用平台轴、接触高度报旋转前数值、多点对准复用缓存姿态，且多点会话被复核入口拒绝；
现按修正计划改正并接通 autocal-multipoint-1.0 只读复核，缺失帧源如实报告而不补零。
离线验证：新回归在 dc5a6f8 上失败、此处通过，panel_check 通过，两截图点接触高度复现
19.0510 / 21.5781 mm；实机项未做，详见 .scratch/autocal-motion-fix/verification.md。
332 lines
14 KiB
Python
332 lines
14 KiB
Python
"""The closed-form solve: exactness, agreement with the sweep, and what it reports.
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The sweep in ``test_kinematics.py`` is the reference this path is checked against.
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It is slow but line-by-line verifiable, and solvers in this file have been
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confidently wrong five times; a fast path with no reference is how that happens.
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"""
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from __future__ import annotations
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import math
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from pathlib import Path
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import numpy as np
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import pytest
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from orisys_autocal.motion.kinematics import (
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Branch,
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PlatformStack,
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rotation_about,
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)
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from orisys_autocal.sensor import load_bundle
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#: The revision the bench confirmed. Skipped rather than failed when absent: the
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#: export lives in a sibling design repo, and a machine without it has not broken
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#: AutoCal.
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BUNDLE = Path(r"D:\orisys_ws\orisys-sensor-design\build\export\finger\r01\compute")
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@pytest.fixture(scope="module")
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def bundle():
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if not BUNDLE.is_dir():
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pytest.skip(f"no sensor export at {BUNDLE}")
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return load_bundle(BUNDLE)
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def _random_units(count: int, seed: int) -> list[np.ndarray]:
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generator = np.random.default_rng(seed)
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vectors = generator.normal(size=(count, 3))
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return [vector / np.linalg.norm(vector) for vector in vectors]
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def _apply(stack: PlatformStack, unit: np.ndarray, yaw_deg: float, pitch_deg: float) -> np.ndarray:
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"""Re-apply a returned pose with the library's own rotations."""
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return (
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rotation_about(stack.pitch_axis, pitch_deg)
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@ rotation_about(stack.yaw_axis, yaw_deg)
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@ unit
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)
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# ------------------------------------------------------------------ exactness
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def test_the_closed_form_is_exact_on_random_normals():
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"""Two atan2 calls should land on the answer, not near it."""
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stack = PlatformStack()
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worst = 0.0
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for unit in _random_units(2000, seed=20240517):
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branches = stack.alignment_branches(unit, stack.tool_direction)
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assert branches, f"no branch found for {unit}"
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worst = max(worst, min(branch.residual_deg for branch in branches))
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assert worst < 1e-4, f"closed form left {worst:.3e} deg on the table"
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def test_every_branch_reproduces_its_own_claimed_residual():
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"""The number reported must be the number the rotations actually give."""
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stack = PlatformStack()
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for unit in _random_units(200, seed=7):
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for branch in stack.alignment_branches(unit, stack.tool_direction):
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landed = _apply(stack, unit, branch.yaw_deg, branch.pitch_deg)
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measured = math.degrees(
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math.acos(
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max(-1.0, min(1.0, float(landed @ stack.tool_direction)))
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)
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)
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# ``acos`` near 1 loses about sqrt(eps) in radians because the
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# derivative blows up there; 1e-5 deg is the floating-point floor,
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# not slack in the answer.
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assert measured == pytest.approx(branch.residual_deg, abs=1e-5)
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# ------------------------------------------------------------------ agreement
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def test_the_closed_form_is_no_worse_than_the_sweep():
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"""The sweep is the reference; the fast path may beat it but never lose."""
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stack = PlatformStack()
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for unit in _random_units(400, seed=99):
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fast = stack._search_alignment(unit, stack.tool_direction) # noqa: SLF001
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branches = stack.alignment_branches(unit, stack.tool_direction)
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best = min(branch.residual_deg for branch in branches)
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assert best <= fast[2] + 1e-9, (
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f"closed form left {best:.6f} deg where the sweep found {fast[2]:.6f}"
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)
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def test_the_two_methods_agree_inside_the_envelope():
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stack = PlatformStack()
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for unit in _random_units(200, seed=1234):
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analytical = stack.solve(np.zeros(3), unit, method="analytical")
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search = stack.solve(np.zeros(3), unit, method="search")
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assert analytical.residual_deg <= search.residual_deg + 1e-6
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for pose in (analytical, search):
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assert abs(pose.yaw_deg) <= stack.yaw_limit_deg + 1e-6
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assert abs(pose.pitch_deg) <= stack.pitch_limit_deg + 1e-6
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# ----------------------------------------------------------- axis equivalence
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def test_the_two_pitch_bindings_are_the_same_family():
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"""Confirmed at the bench: a 90 degree yaw takes one binding to the other.
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``R_y(p)·R_z(t)`` and ``R_x(p)·R_z(t − 90)`` agree to machine precision. So
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which axis is called pitch is a naming and zero-offset choice, not a
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correctness question — the two reach exactly the same set of alignments.
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"""
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unit = np.array([0.3, 0.5, -0.81])
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unit = unit / np.linalg.norm(unit)
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tool = np.array([0.0, 0.0, -1.0])
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worst = 0.0
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for yaw in (-120.0, -45.0, 0.0, 37.0, 90.0, 150.0):
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for pitch in (-90.0, -20.0, 0.0, 15.0, 75.0):
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about_y = rotation_about("y", pitch) @ rotation_about("z", yaw) @ unit
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about_x = rotation_about("x", pitch) @ rotation_about("z", yaw - 90.0) @ unit
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worst = max(worst, abs(float(about_y @ tool) - float(about_x @ tool)))
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assert worst == pytest.approx(0.0, abs=1e-12), (
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f"the two pitch bindings diverge in alignment by {worst:.3e}"
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)
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def test_the_offset_between_the_bindings_is_a_constant_ninety_degrees():
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"""A constant shift, so it is a commissioning zero rather than a correction."""
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unit = np.array([0.3, 0.5, -0.81])
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unit = unit / np.linalg.norm(unit)
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tool = np.array([0.0, 0.0, -1.0])
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for yaw in (-120.0, -30.0, 0.0, 45.0, 120.0):
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for pitch in (-90.0, 0.0, 25.0, 75.0):
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about_y = rotation_about("y", pitch) @ rotation_about("z", yaw) @ unit
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same = rotation_about("x", pitch) @ rotation_about("z", yaw - 90.0) @ unit
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assert float(about_y @ tool) == pytest.approx(float(same @ tool), abs=1e-12)
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# ---------------------------------------------------------------- reporting
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def test_the_normal_is_aligned_to_plus_z_not_minus_z(bundle):
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"""The regression that would have caught the sensor being turned over.
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``approach_sign`` was ``-1``, which asked the solver to bring every normal onto
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**-Z** — the sensing face is the *top* surface of a sensor mounted at the bottom
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of its shell, so it points up, and the tool feeds down onto it. Nothing caught
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it because ``arccos`` is sign-blind: a face turned fully over still scored
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``0.0000`` degrees, and every test here asserted only that the residual was
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small. The failure showed up exclusively as the model flipping upside down.
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So this asserts the *direction* the normal lands in, which is the thing the
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residual cannot express.
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"""
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stack = PlatformStack()
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assert stack.approach_sign == 1
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assert np.allclose(stack.tool_direction, [0.0, 0.0, 1.0])
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for normal in bundle.vertex_normals():
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unit = stack.normal_to_platform(normal)
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unit = unit / np.linalg.norm(unit)
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pose = stack.solve(np.zeros(3), normal)
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# Rotate in the **platform** frame, which is where the residual is measured.
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# ``stage_rotation`` is the same rotation conjugated into the mesh's frame,
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# so using it here compares a sensor-frame vector against a platform-frame
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# target and reports a mismatch that does not exist.
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landed = _apply(stack, unit, pose.yaw_deg, pose.pitch_deg)
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landed = landed / np.linalg.norm(landed)
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# Aligned with the face's aim, not merely parallel to its line.
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assert landed[2] > 0.0, (
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f"the normal landed on {np.round(landed, 3)} — pointing away from +Z"
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)
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def test_the_nominal_pose_is_upright(bundle):
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"""No rotation is the commissioned pose, and it must read as upright."""
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stack = PlatformStack()
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assert stack.uprightness(0.0, 0.0) == pytest.approx(1.0)
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def test_the_surface_normals_point_up_so_the_face_faces_the_tool(bundle):
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"""A sanity check on the geometry the polarity has to agree with.
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The mean normal has a positive platform-Z component: the patch faces upward.
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If this ever inverted, ``approach_sign`` would need revisiting rather than the
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solver.
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"""
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stack = PlatformStack()
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platform = np.array(
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[stack.normal_to_platform(n) for n in bundle.vertex_normals()]
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)
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platform /= np.linalg.norm(platform, axis=1)[:, None]
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assert float(platform[:, 2].mean()) > 0.0, (
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f"mean platform-Z component of the normals is {platform[:, 2].mean():+.3f}"
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)
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def test_no_vertex_ends_turned_over(bundle):
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"""Turning the face over is the failure mode; with the right up axis it is zero.
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``uprightness`` used to compare the platform's Y axis, so 47 of these vertices read
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as turned over even though they only rolled about the vertical. Against the
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post-mount nominal up (the tool axis) no vertex ends inverted, and this now fails
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if the reference regresses rather than reporting a small count as acceptable.
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"""
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stack = PlatformStack()
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normals = bundle.vertex_normals()
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flipped = 0
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for normal in normals:
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pose = stack.solve(np.zeros(3), normal)
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if stack.uprightness(pose.yaw_deg, pose.pitch_deg) < -0.5:
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flipped += 1
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assert flipped == 0, f"{flipped} of {len(normals)} vertices ended turned over"
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def test_a_pose_reports_whether_it_is_upright():
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"""A pose can align the normal and still turn the sensor over."""
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stack = PlatformStack()
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seen = {True: 0, False: 0}
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for unit in _random_units(300, seed=3):
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pose = stack.solve(np.zeros(3), unit)
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seen[pose.upright] += 1
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# Whatever it says must match the measured orientation.
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assert pose.upright == (stack.uprightness(pose.yaw_deg, pose.pitch_deg) >= 0.0)
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assert seen[True] > 0
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def test_an_out_of_range_pose_is_reported_and_never_silently_clamped():
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"""Tight travel must give a measured residual, not a pose the stage cannot reach."""
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stack = PlatformStack(pitch_limit_deg=45.0)
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for unit in _random_units(50, seed=5):
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pose = stack.solve(np.zeros(3), unit)
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assert abs(pose.pitch_deg) <= 45.0 + 1e-6
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assert abs(pose.yaw_deg) <= stack.yaw_limit_deg + 1e-6
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if not pose.in_range:
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assert "travel" in pose.reason
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def test_nothing_is_reported_out_of_range_when_the_travel_is_ample():
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stack = PlatformStack()
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for unit in _random_units(300, seed=11):
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pose = stack.solve(np.zeros(3), unit)
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assert pose.in_range, f"{unit} reported out of range at full travel"
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assert pose.reason == "" or "indeterminate" in pose.reason
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def test_reachability_agrees_with_the_envelope():
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"""Every normal is reachable when the yaw can turn a full half-turn."""
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stack = PlatformStack()
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for unit in _random_units(500, seed=13):
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reachable, why = stack.reachable(unit, stack.tool_direction)
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assert reachable, f"{unit} unreachable: {why}"
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# And with the pitch stage able to turn, no normal is rejected either.
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assert stack.reachable(
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np.array([0.0, 0.0, 1.0]), stack.tool_direction
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)[0]
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# --------------------------------------------------------------------- poles
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@pytest.mark.parametrize(
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"normal",
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[
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np.array([0.0, 1.0, 0.0]), # on the tool axis
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np.array([0.0, -1.0, 0.0]), # on the tool axis, other way
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np.array([1.0, 0.0, 0.0]), # on the yaw plane
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np.array([0.0, 0.0, 1.0]), # on the yaw axis
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],
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)
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def test_poles_still_return_a_usable_pose(normal):
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stack = PlatformStack()
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pose = stack.solve(np.zeros(3), normal)
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assert pose.ok
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assert math.isfinite(pose.yaw_deg) and math.isfinite(pose.pitch_deg)
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landed = _apply(stack, stack.normal_to_platform(normal) / np.linalg.norm(
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stack.normal_to_platform(normal)
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), pose.yaw_deg, pose.pitch_deg)
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measured = math.degrees(
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math.acos(max(-1.0, min(1.0, float(landed @ stack.tool_direction))))
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)
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assert measured <= stack.tolerance_deg + 1e-6
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def test_the_degenerate_normal_is_still_refused():
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stack = PlatformStack()
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pose = stack.solve(np.zeros(3), np.zeros(3))
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assert not pose.ok
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assert "degenerate" in pose.reason
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# ----------------------------------------------------------------- selection
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def test_select_branch_prefers_in_range_then_upright_then_small():
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reachable_upright = Branch(10.0, 5.0, 1e-6, True, True)
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reachable_inverted = Branch(10.0, 5.0, 1e-6, False, True)
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unreachable_upright = Branch(179.0, 5.0, 1e-6, True, False)
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# In range beats everything: an inverted pose the machine can reach is worth
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# more than an upright one it cannot.
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assert PlatformStack.select_branch(
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[unreachable_upright, reachable_inverted, reachable_upright]
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) is reachable_upright
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# Among reachable poses, upright wins.
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assert PlatformStack.select_branch(
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[reachable_inverted, reachable_upright]
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) is reachable_upright
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# Among poses that are out of range and one of them upright, upright wins.
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unreachable_inverted = Branch(179.0, 5.0, 1e-6, False, False)
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assert PlatformStack.select_branch(
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[unreachable_inverted, unreachable_upright]
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) is unreachable_upright
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# A materially better alignment beats an upright pose.
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much_better = Branch(10.0, 5.0, 0.0, False, True)
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worse_upright = Branch(10.0, 5.0, 5.0, True, True)
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assert PlatformStack.select_branch([much_better, worse_upright]) is much_better
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# And with everything else equal, the smaller move wins.
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smaller = Branch(5.0, 5.0, 1e-6, True, True)
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larger = Branch(60.0, 5.0, 1e-6, True, True)
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assert PlatformStack.select_branch([larger, smaller]) is smaller
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def test_an_unknown_method_is_refused():
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stack = PlatformStack()
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with pytest.raises(ValueError):
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stack.solve(np.zeros(3), np.array([0.0, 1.0, 0.0]), method="nonsense")
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