sprint-0: foundations -- data model, seed library, tests, demo
Initial commit. Delivers what the brief calls 'Sprint 0 - Foundations' (see docs/AR_Autopilot_brief.md section 12): - Complete repository structure (arautopilot package + firmware, display, installer, tools placeholders + docs). - Core data model (Pydantic v2): modes, alarms, actuator config, PID config + gain scheduling, vessel config, knob state machine, project config with YAML/JSON serialisation. - Seed library: 2 actuator profiles (hydraulic & electric DC reversible) and 2 default tunings (yacht motor planeo 30 m and 40 m). Conservative literature values, NOT the integrator's production tuning IP. - Firmware skeleton: only src/hal/pinout.h with the 21 I/O contract for the AR-NMEA-IO v1.0 board. No drivers, no main loop. - Studio stubs (real PySide6 app starts in Sprint 4). - pytest suite (80 tests, all green): modes, alarms, actuator, PID (incl. gain interpolation and the +/-50% adaptive bound from brief section 6), vessel, knob state, project config, library loader, end-to-end roundtrip. - examples/sprint0_demo.py - the acceptance demo from the brief. Acceptance criteria met: - pytest green (80/80) - demo creates, saves (YAML + JSON), reloads, and verifies a full ProjectConfig using the seed library - repository ready for tag `sprint-0-approved` See CHANGELOG.md for the detailed scope. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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"""Curated seed library: actuator profiles, default vessel tunings, vessel profiles.
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The contents of this package are **proprietary**. Default PID gains shipped
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here are conservative literature-based starting values intended only for
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bench testing and initial commissioning — they are NOT the integrator's
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production-tuned values.
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"""
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# JSON Schemas
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This directory is reserved for JSON Schemas auto-generated from the Pydantic
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models in `arautopilot.core`. They are useful for:
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- IDE autocompletion / validation when editing actuator profiles or PID
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tunings outside the Studio
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- External tooling that consumes `.appack` configurations
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- Documentation generation
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Schemas are not generated in Sprint 0 but can be produced on demand:
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```python
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from arautopilot.core.actuator_config import ActuatorConfig
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import json
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print(json.dumps(ActuatorConfig.model_json_schema(), indent=2))
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```
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A `tools/regenerate_schemas.py` script will be added later (Sprint 4+).
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{
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"type": "electric_dc_reversible",
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"name": "Generic reversible DC motor with mechanical end-stops (Lewmar / Simpson Lawrence class)",
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"deadband_pct": 4.0,
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"min_useful_pwm_pct": 8.0,
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"asymmetry_stbd_over_port": 1.00,
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"max_rudder_angle_deg": 35.0,
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"max_rate_dps": 5.5,
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"max_current_a": 30.0,
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"feedback_required": true
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}
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{
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"type": "hydraulic_reversible",
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"name": "Generic reversible hydraulic pump (Hynautic / Hypro / Octopus / Vetus / L&S class)",
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"deadband_pct": 7.0,
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"min_useful_pwm_pct": 12.0,
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"asymmetry_stbd_over_port": 1.00,
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"max_rudder_angle_deg": 35.0,
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"max_rate_dps": 4.0,
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"max_current_a": 20.0,
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"feedback_required": true
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}
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# =============================================================================
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# Default PID tuning — 30 m planing motor yacht
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# =============================================================================
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#
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# WARNING — Conservative bench/start values only.
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# These gains come from classical marine control literature (Fossen 2011,
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# Perez 2005) scaled to a 30 m planing motor yacht class. They are intended
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# only for bench testing and the very first sea trial — they MUST be replaced
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# with the integrator's affinated values during commissioning.
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#
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# The "real" tuning libraries are integrator IP and are NOT shipped here.
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#
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# Units:
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# - kp/ki/kd: dimensionless (controller internal units)
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# - frequencies: Hz
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# - rate / deadband: degrees, degrees/second
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#
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# Cascaded loops (brief section 6):
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# - inner: rudder-position controller, 50 Hz
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# - outer: heading controller, 10 Hz
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#
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# Gain scheduling: outer-loop gains interpolated over SOG (knots).
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# Outside the scheduled range the closest endpoint is held.
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# =============================================================================
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schema_version: "0.1.0"
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inner_loop_base:
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kp: 2.5
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ki: 0.15
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kd: 0.30
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inner_loop_freq_hz: 50.0
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outer_loop_base:
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kp: 0.90
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ki: 0.02
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kd: 1.20
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outer_loop_freq_hz: 10.0
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# Three-point schedule covering manoeuvring (5 kn), cruise (15 kn), top (28 kn).
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# At low speed: more kp, less kd (rudder bites more, less inertia anticipation).
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# At high speed: less kp, more kd (gentler corrections, anticipate overshoot).
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gain_schedule:
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- speed_knots: 5.0
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gains: { kp: 1.20, ki: 0.03, kd: 0.80 }
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- speed_knots: 15.0
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gains: { kp: 0.90, ki: 0.02, kd: 1.20 }
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- speed_knots: 28.0
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gains: { kp: 0.55, ki: 0.01, kd: 1.80 }
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rot_feedforward_gain: 1.50
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setpoint_deadband_deg: 0.5
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setpoint_rate_limit_dps: 4.0
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anti_windup_limit: 8.0
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adaptive_enabled: false
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adaptive_max_deviation_pct: 50.0
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# =============================================================================
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# Default PID tuning — 40 m planing motor yacht
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# =============================================================================
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#
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# WARNING — Conservative bench/start values only.
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# Scaled from the 30 m profile to account for the larger vessel: higher mass
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# moment of inertia and longer time constants. Compared to the 30 m profile:
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# - kp slightly lower (slower response per unit rudder)
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# - kd slightly higher (more anticipation needed for the inertia)
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# - setpoint_rate_limit_dps reduced (gentler heading slew)
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#
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# As with the 30 m profile, these are commissioning starting points and
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# must be replaced with the integrator's production tuning.
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# =============================================================================
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schema_version: "0.1.0"
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inner_loop_base:
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kp: 2.20
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ki: 0.12
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kd: 0.35
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inner_loop_freq_hz: 50.0
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outer_loop_base:
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kp: 0.75
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ki: 0.015
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kd: 1.55
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outer_loop_freq_hz: 10.0
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gain_schedule:
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- speed_knots: 5.0
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gains: { kp: 1.00, ki: 0.025, kd: 1.10 }
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- speed_knots: 15.0
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gains: { kp: 0.75, ki: 0.015, kd: 1.55 }
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- speed_knots: 28.0
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gains: { kp: 0.45, ki: 0.008, kd: 2.20 }
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rot_feedforward_gain: 1.80
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setpoint_deadband_deg: 0.5
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setpoint_rate_limit_dps: 3.0
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anti_windup_limit: 8.0
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adaptive_enabled: false
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adaptive_max_deviation_pct: 50.0
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"""Filesystem loader for the seed library.
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Resolves paths inside the installed ``arautopilot.library`` package and
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deserialises actuator profiles (JSON) and default PID tunings (YAML).
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"""
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from __future__ import annotations
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import json
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from importlib import resources
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from pathlib import Path
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from typing import Any
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import yaml
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from arautopilot.core.actuator_config import ActuatorConfig
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from arautopilot.core.pid_config import PidConfig
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_ACTUATOR_PACKAGE = "arautopilot.library.actuators"
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_TUNINGS_PACKAGE = "arautopilot.library.default_tunings"
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def list_actuator_profiles() -> list[str]:
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"""Return the IDs (filename stems) of all bundled actuator profiles."""
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return sorted(
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r.name.removesuffix(".json")
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for r in resources.files(_ACTUATOR_PACKAGE).iterdir()
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if r.is_file() and r.name.endswith(".json")
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)
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def list_default_tunings() -> list[str]:
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"""Return the IDs (filename stems) of all bundled default tunings."""
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return sorted(
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r.name.removesuffix(".yaml")
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for r in resources.files(_TUNINGS_PACKAGE).iterdir()
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if r.is_file() and r.name.endswith(".yaml")
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)
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def load_actuator_profile(profile_id: str) -> ActuatorConfig:
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"""Load and validate one bundled actuator profile by ID."""
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data = _read_json_resource(_ACTUATOR_PACKAGE, f"{profile_id}.json")
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return ActuatorConfig.model_validate(data)
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def load_default_tuning(tuning_id: str) -> PidConfig:
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"""Load and validate one bundled default PID tuning by ID."""
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data = _read_yaml_resource(_TUNINGS_PACKAGE, f"{tuning_id}.yaml")
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return PidConfig.model_validate(data)
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def load_actuator_profile_from_path(path: Path | str) -> ActuatorConfig:
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"""Load an actuator profile from an arbitrary filesystem path (for tests / Studio)."""
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p = Path(path)
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with p.open("r", encoding="utf-8") as f:
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return ActuatorConfig.model_validate(json.load(f))
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def load_default_tuning_from_path(path: Path | str) -> PidConfig:
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"""Load a default PID tuning from an arbitrary filesystem path (for tests / Studio)."""
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p = Path(path)
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with p.open("r", encoding="utf-8") as f:
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return PidConfig.model_validate(yaml.safe_load(f))
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def _read_json_resource(package: str, filename: str) -> dict[str, Any]:
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return json.loads(resources.files(package).joinpath(filename).read_text(encoding="utf-8"))
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def _read_yaml_resource(package: str, filename: str) -> dict[str, Any]:
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return yaml.safe_load(
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resources.files(package).joinpath(filename).read_text(encoding="utf-8")
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)
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