524 lines
17 KiB
Python
524 lines
17 KiB
Python
"""为新版4m直线控制器实验CSV生成轨迹、横向/航向误差和速度响应图。"""
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from __future__ import annotations
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import argparse
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from pathlib import Path
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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SCRIPT_DIR = Path(__file__).resolve().parent
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def configure_matplotlib() -> None:
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"""配置可显示中文和负号的Matplotlib字体。"""
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plt.rcParams["font.sans-serif"] = [
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"Microsoft YaHei",
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"SimHei",
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"Noto Sans CJK SC",
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"Arial Unicode MS",
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"DejaVu Sans",
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]
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plt.rcParams["axes.unicode_minus"] = False
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def numeric_column(
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frame: pd.DataFrame,
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name: str,
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default: float = np.nan,
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) -> np.ndarray:
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"""将CSV列安全转换为浮点数组，缺失列使用指定默认值。"""
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if name not in frame.columns:
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return np.full(len(frame), default, dtype=float)
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return pd.to_numeric(frame[name], errors="coerce").to_numpy(
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dtype=float,
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copy=True,
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)
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def first_finite(values: np.ndarray, default: float) -> float:
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"""读取数组中的第一个有限值。"""
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finite = values[np.isfinite(values)]
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return float(finite[0]) if finite.size else default
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def first_text(frame: pd.DataFrame, name: str, default: str) -> str:
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"""读取文本元数据列中的第一个非空值。"""
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if name not in frame.columns:
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return default
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values = frame[name].dropna().astype(str)
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values = values[values.str.strip() != ""]
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return values.iloc[0] if not values.empty else default
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def fill_reference_series(
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values: np.ndarray,
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fallback: np.ndarray,
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) -> np.ndarray:
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"""前后填充后台采样得到的控制参考值，缺失时使用解析速度曲线。"""
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series = pd.Series(values, dtype=float)
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filled = series.ffill().bfill().to_numpy(
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dtype=float,
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copy=True,
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)
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missing = ~np.isfinite(filled)
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filled[missing] = fallback[missing]
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return filled
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def planned_motion(
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time_seconds: np.ndarray,
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length_meters: float,
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cruise_speed_mps: float,
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acceleration_mps2: float,
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deceleration_mps2: float,
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) -> tuple[np.ndarray, np.ndarray]:
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"""计算从静止出发并在终点静止的梯形或三角形理想时间速度轨迹。"""
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acceleration_distance = (
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cruise_speed_mps**2 / (2.0 * acceleration_mps2)
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)
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deceleration_distance = (
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cruise_speed_mps**2 / (2.0 * deceleration_mps2)
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)
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if acceleration_distance + deceleration_distance <= length_meters:
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peak_speed = cruise_speed_mps
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else:
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peak_speed = np.sqrt(
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2.0
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* length_meters
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/ (1.0 / acceleration_mps2 + 1.0 / deceleration_mps2)
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)
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acceleration_distance = (
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peak_speed**2 / (2.0 * acceleration_mps2)
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)
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deceleration_distance = (
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peak_speed**2 / (2.0 * deceleration_mps2)
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)
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acceleration_time = peak_speed / acceleration_mps2
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deceleration_time = peak_speed / deceleration_mps2
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cruise_distance = max(
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0.0,
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length_meters - acceleration_distance - deceleration_distance,
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)
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cruise_time = cruise_distance / peak_speed
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deceleration_start_time = acceleration_time + cruise_time
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finish_time = deceleration_start_time + deceleration_time
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progress = np.zeros_like(time_seconds, dtype=float)
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speed = np.zeros_like(time_seconds, dtype=float)
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accelerating = time_seconds <= acceleration_time
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progress[accelerating] = (
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0.5 * acceleration_mps2 * time_seconds[accelerating] ** 2
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)
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speed[accelerating] = acceleration_mps2 * time_seconds[accelerating]
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cruising = (
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(time_seconds > acceleration_time)
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& (time_seconds <= deceleration_start_time)
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)
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progress[cruising] = (
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acceleration_distance
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+ peak_speed * (time_seconds[cruising] - acceleration_time)
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)
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speed[cruising] = peak_speed
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decelerating = (
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(time_seconds > deceleration_start_time)
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& (time_seconds <= finish_time)
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)
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remaining_time = finish_time - time_seconds[decelerating]
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progress[decelerating] = (
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length_meters
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- 0.5 * deceleration_mps2 * remaining_time**2
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)
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speed[decelerating] = deceleration_mps2 * remaining_time
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finished = time_seconds > finish_time
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progress[finished] = length_meters
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speed[finished] = 0.0
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return progress, speed
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def load_experiment(csv_path: Path) -> dict[str, object]:
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"""读取新版CSV并构造绘图所需的统一SI单位数据。"""
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frame = pd.read_csv(csv_path, encoding="utf-8-sig")
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if frame.empty:
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raise ValueError("CSV没有任何采样行。")
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time_seconds = numeric_column(frame, "ElapsedSeconds")
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valid_time = np.isfinite(time_seconds)
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frame = frame.loc[valid_time].reset_index(drop=True)
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time_seconds = time_seconds[valid_time]
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if time_seconds.size < 2:
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raise ValueError("CSV中的有效时间采样不足2帧。")
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time_seconds = time_seconds - time_seconds[0]
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state_x = numeric_column(frame, "StateXMeters")
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state_y = numeric_column(frame, "StateYMeters")
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has_processed = numeric_column(frame, "HasProcessedState", 0.0) > 0.5
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processed_valid = has_processed & np.isfinite(state_x) & np.isfinite(state_y)
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raw_x_meters = numeric_column(frame, "DetourX") / 1000.0
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raw_y_meters = numeric_column(frame, "DetourY") / 1000.0
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actual_x = np.where(processed_valid, state_x, raw_x_meters)
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actual_y = np.where(processed_valid, state_y, raw_y_meters)
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valid_position = np.isfinite(actual_x) & np.isfinite(actual_y)
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if np.count_nonzero(valid_position) < 2:
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raise ValueError("CSV中没有足够的有效车辆位置。")
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start = np.array(
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[
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first_finite(numeric_column(frame, "ReferenceStartX"), np.nan),
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first_finite(numeric_column(frame, "ReferenceStartY"), np.nan),
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],
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dtype=float,
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) / 1000.0
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end = np.array(
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[
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first_finite(numeric_column(frame, "ReferenceEndX"), np.nan),
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first_finite(numeric_column(frame, "ReferenceEndY"), np.nan),
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],
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dtype=float,
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) / 1000.0
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if not np.all(np.isfinite(start)) or not np.all(np.isfinite(end)):
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raise ValueError("CSV缺少有效的参考起点或终点。")
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line = end - start
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length_meters = float(np.linalg.norm(line))
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if length_meters <= 1e-6:
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raise ValueError("参考直线长度必须大于0。")
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tangent = line / length_meters
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left_normal = np.array([-tangent[1], tangent[0]])
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displacement = np.column_stack([actual_x, actual_y]) - start
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# 与C# TrajectoryProjector保持一致：轨迹位于车辆左侧时为正。
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derived_lateral_error = -(displacement @ left_normal)
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recorded_lateral_error = numeric_column(
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frame,
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"ControlLateralErrorMeters",
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)
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has_control_reference = (
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numeric_column(frame, "HasControlReference", 0.0) > 0.5
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)
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recorded_lateral_valid = (
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has_control_reference & np.isfinite(recorded_lateral_error)
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)
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lateral_error = (
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np.where(recorded_lateral_valid, recorded_lateral_error, np.nan)
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if np.any(recorded_lateral_valid)
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else derived_lateral_error
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)
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state_yaw = numeric_column(frame, "StateYawRadians")
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raw_yaw = np.deg2rad(numeric_column(frame, "DetourTheta"))
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actual_yaw = np.where(
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has_processed & np.isfinite(state_yaw),
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state_yaw,
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raw_yaw,
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)
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reference_yaw = np.arctan2(tangent[1], tangent[0])
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derived_heading_error = np.arctan2(
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np.sin(reference_yaw - actual_yaw),
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np.cos(reference_yaw - actual_yaw),
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)
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recorded_heading_error = numeric_column(
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frame,
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"ControlHeadingErrorRadians",
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)
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recorded_heading_valid = (
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has_control_reference & np.isfinite(recorded_heading_error)
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)
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heading_error = (
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np.where(recorded_heading_valid, recorded_heading_error, np.nan)
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if np.any(recorded_heading_valid)
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else derived_heading_error
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)
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# 投影定义满足：参考点 = 车体位置 + 横向误差 × 参考航向左法向。
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# 因此无需假设轨迹类型，即可从有效控制周期还原车辆实际使用的参考轨迹。
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projected_reference_yaw = actual_yaw + heading_error
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reference_x = (
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actual_x - lateral_error * np.sin(projected_reference_yaw)
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)
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reference_y = (
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actual_y + lateral_error * np.cos(projected_reference_yaw)
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)
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valid_reference_position = (
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has_control_reference
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& np.isfinite(reference_x)
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& np.isfinite(reference_y)
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)
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cruise_speed = first_finite(
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numeric_column(frame, "ReferenceSpeed"),
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0.30,
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)
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acceleration = first_finite(
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numeric_column(
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frame,
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"ReferenceAccelerationMetersPerSecondSquared",
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),
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0.20,
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)
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deceleration = first_finite(
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numeric_column(
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frame,
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"ReferenceDecelerationMetersPerSecondSquared",
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),
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0.20,
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)
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if acceleration <= 0.0:
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acceleration = 0.20
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if deceleration <= 0.0:
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deceleration = 0.20
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_, ideal_speed = planned_motion(
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time_seconds,
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length_meters,
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cruise_speed,
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acceleration,
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deceleration,
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)
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reference_speed = fill_reference_series(
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numeric_column(frame, "ControlReferenceSpeedMetersPerSecond"),
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ideal_speed,
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)
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if np.any(has_control_reference):
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reference_speed[~has_control_reference] = np.nan
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actual_speed = numeric_column(frame, "StateBodyVxMetersPerSecond")
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velocity_valid = (
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numeric_column(frame, "StateVelocityEstimateValid", 0.0) > 0.5
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)
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actual_speed[~velocity_valid] = np.nan
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command_speed = numeric_column(frame, "CommandSpeed")
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return {
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"frame": frame,
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"time": time_seconds,
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"actual_x": actual_x,
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"actual_y": actual_y,
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"valid_position": valid_position,
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"reference_x": reference_x,
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"reference_y": reference_y,
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"valid_reference_position": valid_reference_position,
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"start": start,
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"end": end,
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"length": length_meters,
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"lateral_error": lateral_error,
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"heading_error": heading_error,
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"reference_speed": reference_speed,
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"actual_speed": actual_speed,
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"command_speed": command_speed,
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"controller_name": first_text(
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frame,
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"ControllerName",
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"NewController",
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),
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"trajectory_name": first_text(
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frame,
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"TrajectoryName",
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"Trajectory",
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),
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}
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def finite_rmse(values: np.ndarray) -> float:
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"""计算忽略无效样本后的均方根值。"""
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finite = values[np.isfinite(values)]
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return float(np.sqrt(np.mean(finite**2))) if finite.size else np.nan
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def save_figure(
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fig: plt.Figure,
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destination: Path,
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show: bool,
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) -> None:
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"""保存并关闭一张实验图。"""
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fig.tight_layout()
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fig.savefig(destination, dpi=300, bbox_inches="tight")
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if show:
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plt.show()
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plt.close(fig)
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def plot_experiment(
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csv_path: Path,
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output_directory: Path,
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show: bool,
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) -> list[Path]:
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"""为单份新版控制器CSV生成四类对比图。"""
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data = load_experiment(csv_path)
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output_directory.mkdir(parents=True, exist_ok=True)
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title = f"{data['controller_name']} - {data['trajectory_name']}"
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destinations: list[Path] = []
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valid_position = data["valid_position"]
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fig, axis = plt.subplots(figsize=(9.0, 6.5))
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valid_reference_position = data["valid_reference_position"]
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if np.count_nonzero(valid_reference_position) >= 2:
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axis.plot(
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data["reference_x"][valid_reference_position],
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data["reference_y"][valid_reference_position],
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"--",
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linewidth=2.0,
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label="控制器实际使用的参考轨迹",
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)
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else:
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axis.plot(
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[data["start"][0], data["end"][0]],
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[data["start"][1], data["end"][1]],
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"--",
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linewidth=2.0,
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label="参考起终点连线",
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)
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axis.plot(
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data["actual_x"][valid_position],
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data["actual_y"][valid_position],
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linewidth=1.5,
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label="状态估计后的实际轨迹",
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)
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axis.scatter(*data["start"], color="green", s=45, label="起点")
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axis.scatter(*data["end"], color="red", s=45, label="终点")
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axis.set_aspect("equal", adjustable="box")
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axis.set_xlabel("世界坐标X / m")
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axis.set_ylabel("世界坐标Y / m")
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axis.set_title(f"期望轨迹与实际轨迹对比\n{title}")
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axis.grid(True, alpha=0.3)
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axis.legend()
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destination = output_directory / f"{csv_path.stem}_trajectory.png"
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save_figure(fig, destination, show)
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destinations.append(destination)
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lateral_mm = data["lateral_error"] * 1000.0
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lateral_rmse_mm = finite_rmse(lateral_mm)
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fig, axis = plt.subplots(figsize=(10.0, 5.5))
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axis.plot(data["time"], lateral_mm, linewidth=1.5)
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axis.axhline(0.0, color="black", linewidth=0.8)
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axis.set_xlabel("时间 / s")
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axis.set_ylabel("横向误差 / mm")
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axis.set_title(
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f"横向误差（轨迹在车辆左侧为正）\n{title}，RMSE={lateral_rmse_mm:.2f}mm"
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)
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axis.grid(True, alpha=0.3)
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destination = output_directory / f"{csv_path.stem}_lateral_error.png"
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save_figure(fig, destination, show)
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destinations.append(destination)
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heading_degrees = np.rad2deg(data["heading_error"])
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heading_rmse_degrees = finite_rmse(heading_degrees)
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fig, axis = plt.subplots(figsize=(10.0, 5.5))
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axis.plot(data["time"], heading_degrees, linewidth=1.5)
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axis.axhline(0.0, color="black", linewidth=0.8)
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axis.set_xlabel("时间 / s")
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axis.set_ylabel("航向角偏差 / °")
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axis.set_title(
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"航向角偏差：参考轨迹航向－实际车体航向（逆时针为正）\n"
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f"{title}，RMSE={heading_rmse_degrees:.3f}°"
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)
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axis.grid(True, alpha=0.3)
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destination = output_directory / f"{csv_path.stem}_heading_error.png"
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save_figure(fig, destination, show)
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destinations.append(destination)
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speed_error = data["actual_speed"] - data["reference_speed"]
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speed_rmse = finite_rmse(speed_error)
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fig, axis = plt.subplots(figsize=(10.0, 5.8))
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axis.plot(
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data["time"],
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data["reference_speed"],
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linewidth=1.8,
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label="控制器实际参考速度",
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)
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axis.plot(
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data["time"],
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data["command_speed"],
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"--",
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linewidth=1.3,
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label="纵向控制器下发速度",
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)
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axis.plot(
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data["time"],
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data["actual_speed"],
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linewidth=1.5,
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label="状态估计实际车体纵向速度",
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)
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axis.set_xlabel("时间 / s")
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axis.set_ylabel("速度 / (m/s)")
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axis.set_title(f"参考速度与实际速度对比\n{title}，RMSE={speed_rmse:.4f}m/s")
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axis.grid(True, alpha=0.3)
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axis.legend()
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destination = output_directory / f"{csv_path.stem}_speed_response.png"
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save_figure(fig, destination, show)
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destinations.append(destination)
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print(
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f"{csv_path.name}: 横向RMSE={lateral_rmse_mm:.3f}mm, "
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f"航向RMSE={heading_rmse_degrees:.4f}°, "
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f"速度RMSE={speed_rmse:.5f}m/s"
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)
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for destination in destinations:
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||
print(f"已生成：{destination}")
|
||
return destinations
|
||
|
||
|
||
def discover_csv_files(arguments: list[str]) -> list[Path]:
|
||
"""读取命令行文件；未指定时扫描脚本目录及data子目录中的CSV。"""
|
||
if arguments:
|
||
files = [Path(item).expanduser().resolve() for item in arguments]
|
||
else:
|
||
files = sorted(SCRIPT_DIR.glob("*.csv"))
|
||
files.extend(sorted((SCRIPT_DIR / "data").glob("*.csv")))
|
||
files = [path for path in files if path.is_file()]
|
||
if not files:
|
||
raise FileNotFoundError(
|
||
"没有找到CSV；请传入文件路径，或将文件放到脚本目录/data中。"
|
||
)
|
||
return files
|
||
|
||
|
||
def main() -> None:
|
||
"""解析命令行并批量处理新版控制器实验CSV。"""
|
||
parser = argparse.ArgumentParser(
|
||
description="绘制新版控制器轨迹实验的四类对比图。"
|
||
)
|
||
parser.add_argument("csv", nargs="*", help="需要处理的CSV文件路径。")
|
||
parser.add_argument(
|
||
"--output-dir",
|
||
help="图片输出目录；默认使用脚本目录/plots。",
|
||
)
|
||
parser.add_argument(
|
||
"--show",
|
||
action="store_true",
|
||
help="保存图片后同时显示窗口。",
|
||
)
|
||
arguments = parser.parse_args()
|
||
configure_matplotlib()
|
||
|
||
output_directory = (
|
||
Path(arguments.output_dir).expanduser().resolve()
|
||
if arguments.output_dir
|
||
else SCRIPT_DIR / "plots"
|
||
)
|
||
failed = 0
|
||
for csv_path in discover_csv_files(arguments.csv):
|
||
try:
|
||
plot_experiment(csv_path, output_directory, arguments.show)
|
||
except Exception as exception:
|
||
failed += 1
|
||
print(f"处理失败：{csv_path}：{exception}")
|
||
|
||
if failed:
|
||
raise SystemExit(f"共有{failed}个CSV处理失败。")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|