#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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车辆运动模型校准数据分析工具
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解析$CAL格式日志，提取差速运动模型参数
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"""
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import argparse
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import json
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import re
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import sys
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from pathlib import Path
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from typing import List, Dict, Tuple
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import numpy as np
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import matplotlib
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import matplotlib.pyplot as plt
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from scipy import optimize
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from collections import defaultdict
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# 可选：配置中文字体以避免警告
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# 如果图表中文显示异常，可运行: python tools/fix_matplotlib_font.py 测试
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try:
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import platform
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system = platform.system()
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if system == 'Windows':
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matplotlib.rcParams['font.sans-serif'] = ['Microsoft YaHei', 'SimHei', 'SimSun']
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elif system == 'Darwin':
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matplotlib.rcParams['font.sans-serif'] = ['PingFang SC', 'Heiti SC']
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else:
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matplotlib.rcParams['font.sans-serif'] = ['WenQuanYi Micro Hei', 'Noto Sans CJK SC']
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matplotlib.rcParams['axes.unicode_minus'] = False
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except:
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pass # 字体配置失败也不影响功能，只是可能有警告
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# 添加项目路径
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sys.path.append(str(Path(__file__).parent.parent))
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# ENU坐标已在单片机端计算，不再需要geo转换函数
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class CalibrationData:
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"""校准数据点"""
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def __init__(self, line: str):
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# $CAL,seq,time_ms,state,throttle_pwm,steering_pwm,enu_x,enu_y,enu_z,hdg,pitch,roll,gx,gy,gz,ax,ay,az
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parts = line.strip().split(',')
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if len(parts) < 18:
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raise ValueError(f"Invalid CAL line: {line}")
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self.seq = int(parts[1])
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self.time_ms = int(parts[2])
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self.state = parts[3]
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self.throttle_pwm = int(parts[4])
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self.steering_pwm = int(parts[5])
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# 直接读取ENU坐标（不再需要从经纬度转换）
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self.enu_x = float(parts[6]) # 东向坐标 (m)
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self.enu_y = float(parts[7]) # 北向坐标 (m)
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self.enu_z = float(parts[8]) # 天向坐标 (m)
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self.hdg = float(parts[9])
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self.pitch = float(parts[10])
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self.roll = float(parts[11])
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self.gx = float(parts[12]) / 1000.0 # 毫度/秒 -> 度/秒
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self.gy = float(parts[13]) / 1000.0
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self.gz = float(parts[14]) / 1000.0
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self.ax = float(parts[15]) / 1000.0 # 毫g -> g
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self.ay = float(parts[16]) / 1000.0
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self.az = float(parts[17]) / 1000.0
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# 待计算字段
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self.velocity = 0.0 # 前向速度 m/s
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self.yaw_rate = 0.0 # 角速度 rad/s
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# 兼容性字段（已废弃）
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self.lat = 0.0
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self.lon = 0.0
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self.alt = self.enu_z
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class CalibrationAnalyzer:
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"""校准数据分析器"""
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def __init__(self, log_file: str):
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self.log_file = log_file
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self.data: List[CalibrationData] = []
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self.states_data: Dict[str, List[CalibrationData]] = defaultdict(list)
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def parse_log(self):
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"""解析日志文件"""
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print(f"解析日志文件: {self.log_file}")
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with open(self.log_file, 'r', encoding='utf-8', errors='ignore') as f:
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for line in f:
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if line.startswith('$CAL'):
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try:
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data = CalibrationData(line)
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self.data.append(data)
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except Exception as e:
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print(f"警告: 解析行失败: {e}")
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continue
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if not self.data:
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raise ValueError("未找到有效的$CAL数据")
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print(f"成功解析 {len(self.data)} 条数据点")
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# ENU坐标已在单片机端计算完成，这里不需要原点转换
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print(f"ENU坐标范围: X=[{min(d.enu_x for d in self.data):.2f}, {max(d.enu_x for d in self.data):.2f}], " +
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f"Y=[{min(d.enu_y for d in self.data):.2f}, {max(d.enu_y for d in self.data):.2f}] (m)")
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def compute_enu_and_velocity(self):
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"""计算速度（ENU坐标已在单片机端计算完成）"""
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print("计算速度和角速度...")
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# ENU坐标已在单片机端计算完成，这里只需要计算速度和角速度
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for i, data in enumerate(self.data):
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# 计算速度（相邻点差分）
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if i > 0:
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dt = (data.time_ms - self.data[i-1].time_ms) / 1000.0 # 秒
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if dt > 0:
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dx = data.enu_x - self.data[i-1].enu_x
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dy = data.enu_y - self.data[i-1].enu_y
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data.velocity = np.sqrt(dx**2 + dy**2) / dt
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# 角速度（从航向角差分）
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dhdg = data.hdg - self.data[i-1].hdg
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# 处理角度跳变
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if dhdg > 180:
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dhdg -= 360
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elif dhdg < -180:
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dhdg += 360
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data.yaw_rate = np.deg2rad(dhdg) / dt
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# 按状态分组
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for data in self.data:
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self.states_data[data.state].append(data)
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print(f"数据分组完成，共 {len(self.states_data)} 个状态")
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for state, dlist in self.states_data.items():
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print(f" {state}: {len(dlist)} 个点")
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def analyze_forward_model(self) -> Dict:
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"""分析前向运动模型"""
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print("\n=== 分析前向运动模型 ===")
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results = {}
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# 分析巡航阶段的PWM-速度关系
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cruise_states = ['CRUISE_LOW', 'CRUISE_MID', 'CRUISE_HIGH']
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pwm_velocity_pairs = []
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for state in cruise_states:
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if state in self.states_data:
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dlist = self.states_data[state]
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# 取后半段数据（稳态）
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stable_data = dlist[len(dlist)//2:]
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if stable_data:
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avg_pwm = np.mean([d.throttle_pwm for d in stable_data])
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avg_vel = np.mean([d.velocity for d in stable_data])
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pwm_velocity_pairs.append((avg_pwm, avg_vel))
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print(f"{state}: PWM={avg_pwm:.0f}, 速度={avg_vel:.2f} m/s")
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# 拟合线性模型: v = k * (pwm - pwm_center) + bias
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if len(pwm_velocity_pairs) >= 2:
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pwms = np.array([p[0] for p in pwm_velocity_pairs])
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vels = np.array([p[1] for p in pwm_velocity_pairs])
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# 线性拟合
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def linear_model(pwm, k, bias):
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return k * (pwm - 1500) + bias
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popt, _ = optimize.curve_fit(linear_model, pwms, vels)
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k_forward, bias = popt
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results['pwm_to_velocity'] = {
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'model': 'linear',
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'k': float(k_forward),
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'bias': float(bias),
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'formula': f'v = {k_forward:.6f} * (pwm - 1500) + {bias:.6f}',
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'pwm_velocity_pairs': [(float(p), float(v)) for p, v in pwm_velocity_pairs]
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}
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print(f"PWM-速度模型: v = {k_forward:.6f} * (pwm - 1500) + {bias:.6f}")
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# 分析加速度
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accel_states = ['ACCEL_LOW', 'ACCEL_MID', 'ACCEL_HIGH']
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accel_results = {}
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for state in accel_states:
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if state in self.states_data:
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dlist = self.states_data[state]
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if len(dlist) > 5:
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velocities = [d.velocity for d in dlist]
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times = [(d.time_ms - dlist[0].time_ms) / 1000.0 for d in dlist]
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# 线性拟合速度-时间
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if len(times) > 1:
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coeffs = np.polyfit(times, velocities, 1)
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accel = coeffs[0]
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accel_results[state] = float(accel)
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print(f"{state}: 加速度 = {accel:.3f} m/s²")
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results['acceleration'] = accel_results
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# 分析制动
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if 'BRAKE' in self.states_data:
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dlist = self.states_data['BRAKE']
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if len(dlist) > 5:
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velocities = [d.velocity for d in dlist]
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times = [(d.time_ms - dlist[0].time_ms) / 1000.0 for d in dlist]
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if len(times) > 1:
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coeffs = np.polyfit(times, velocities, 1)
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decel = coeffs[0]
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results['deceleration'] = float(decel)
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print(f"制动减速度: {decel:.3f} m/s²")
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return results
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def analyze_steering_model(self) -> Dict:
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"""分析转向模型"""
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print("\n=== 分析转向模型 ===")
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results = {}
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turn_states = ['TURN_LEFT_LIGHT', 'TURN_RIGHT_LIGHT', 'TURN_LEFT_HEAVY', 'TURN_RIGHT_HEAVY']
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pwm_yawrate_pairs = []
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for state in turn_states:
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if state in self.states_data:
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dlist = self.states_data[state]
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# 取中间段数据（稳态转向）
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start_idx = len(dlist) // 4
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end_idx = 3 * len(dlist) // 4
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stable_data = dlist[start_idx:end_idx]
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if stable_data:
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avg_steering_pwm = np.mean([d.steering_pwm for d in stable_data])
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avg_yaw_rate = np.mean([d.yaw_rate for d in stable_data])
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avg_velocity = np.mean([d.velocity for d in stable_data])
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# 转向PWM偏移量（1500为中心）
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steering_offset = avg_steering_pwm - 1500
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pwm_yawrate_pairs.append((steering_offset, avg_yaw_rate, avg_velocity))
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turning_radius = abs(avg_velocity / avg_yaw_rate) if abs(avg_yaw_rate) > 0.01 else float('inf')
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print(f"{state}: 舵机PWM={avg_steering_pwm:.0f} ({steering_offset:+.0f}), "
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f"角速度={avg_yaw_rate:.3f} rad/s, 速度={avg_velocity:.2f} m/s, "
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f"转向半径={turning_radius:.2f} m")
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# 拟合转向模型: ω = k_steering * steering_offset
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if len(pwm_yawrate_pairs) >= 2:
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offsets = np.array([p[0] for p in pwm_yawrate_pairs])
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yaw_rates = np.array([p[1] for p in pwm_yawrate_pairs])
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# 线性拟合（过原点）
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k_steering = np.sum(offsets * yaw_rates) / np.sum(offsets**2)
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results['pwm_to_yaw_rate'] = {
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'model': 'linear',
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'k': float(k_steering),
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'formula': f'ω = {k_steering:.8f} * (steering_pwm - 1500)',
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'data': [(float(o), float(y), float(v)) for o, y, v in pwm_yawrate_pairs]
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}
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print(f"转向模型: ω = {k_steering:.8f} * (steering_pwm - 1500)")
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return results
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def analyze_delays(self) -> Dict:
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"""分析响应延迟"""
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print("\n=== 分析响应延迟 ===")
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results = {}
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# 检测WARMUP到ACCEL_LOW的响应延迟
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if 'WARMUP' in self.states_data and 'ACCEL_LOW' in self.states_data:
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warmup_last = self.states_data['WARMUP'][-1]
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accel_first = self.states_data['ACCEL_LOW'][0]
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# 找到速度开始上升的点
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threshold_velocity = 0.1 # m/s
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for d in self.states_data['ACCEL_LOW']:
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if d.velocity > threshold_velocity:
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delay_ms = d.time_ms - accel_first.time_ms
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results['throttle_response_delay_ms'] = float(delay_ms)
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print(f"油门响应延迟: {delay_ms} ms")
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break
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return results
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def generate_plots(self, output_dir: Path):
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"""生成可视化图表"""
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print("\n=== 生成可视化图表 ===")
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output_dir.mkdir(parents=True, exist_ok=True)
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# 1. 速度-时间曲线
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fig, ax = plt.subplots(figsize=(14, 6))
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times = [(d.time_ms - self.data[0].time_ms) / 1000.0 for d in self.data]
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velocities = [d.velocity for d in self.data]
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throttles = [d.throttle_pwm for d in self.data]
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ax.plot(times, velocities, 'b-', linewidth=1.5, label='速度 (m/s)')
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ax.set_xlabel('时间 (秒)', fontsize=12)
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ax.set_ylabel('速度 (m/s)', fontsize=12, color='b')
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ax.tick_params(axis='y', labelcolor='b')
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ax.grid(True, alpha=0.3)
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# 双轴显示PWM
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ax2 = ax.twinx()
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ax2.plot(times, throttles, 'r--', linewidth=1, alpha=0.6, label='油门PWM')
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ax2.set_ylabel('油门PWM', fontsize=12, color='r')
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ax2.tick_params(axis='y', labelcolor='r')
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# 标注状态
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current_state = self.data[0].state
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state_start_time = times[0]
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for i, (t, d) in enumerate(zip(times, self.data)):
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if d.state != current_state or i == len(self.data) - 1:
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ax.axvspan(state_start_time, t, alpha=0.1, color='gray')
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ax.text((state_start_time + t) / 2, max(velocities) * 0.9,
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current_state, ha='center', fontsize=8, rotation=0)
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current_state = d.state
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state_start_time = t
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plt.title('速度与油门PWM随时间变化', fontsize=14)
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fig.tight_layout()
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plt.savefig(output_dir / 'velocity_time.png', dpi=150)
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print(f"保存: {output_dir / 'velocity_time.png'}")
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plt.close()
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# 2. 轨迹图
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fig, ax = plt.subplots(figsize=(10, 10))
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xs = [d.enu_x for d in self.data]
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ys = [d.enu_y for d in self.data]
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# 按状态着色
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state_colors = {}
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color_idx = 0
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colors_list = plt.cm.tab10(np.linspace(0, 1, 10))
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for state in self.states_data.keys():
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state_colors[state] = colors_list[color_idx % 10]
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color_idx += 1
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for state, dlist in self.states_data.items():
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xs_state = [d.enu_x for d in dlist]
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ys_state = [d.enu_y for d in dlist]
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ax.plot(xs_state, ys_state, color=state_colors[state],
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linewidth=2, label=state, marker='o', markersize=2)
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ax.set_xlabel('东向 (m)', fontsize=12)
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ax.set_ylabel('北向 (m)', fontsize=12)
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ax.set_title('车辆运动轨迹 (ENU坐标)', fontsize=14)
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ax.grid(True, alpha=0.3)
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ax.axis('equal')
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ax.legend(fontsize=8, loc='best')
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plt.tight_layout()
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plt.savefig(output_dir / 'trajectory.png', dpi=150)
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print(f"保存: {output_dir / 'trajectory.png'}")
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plt.close()
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# 3. PWM-速度关系图
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))
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# 前向速度
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cruise_states = ['CRUISE_LOW', 'CRUISE_MID', 'CRUISE_HIGH']
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for state in cruise_states:
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if state in self.states_data:
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dlist = self.states_data[state]
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stable_data = dlist[len(dlist)//2:]
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pwms = [d.throttle_pwm for d in stable_data]
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vels = [d.velocity for d in stable_data]
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ax1.scatter(pwms, vels, label=state, alpha=0.5, s=20)
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ax1.set_xlabel('油门PWM', fontsize=12)
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ax1.set_ylabel('速度 (m/s)', fontsize=12)
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ax1.set_title('PWM-速度关系', fontsize=14)
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ax1.grid(True, alpha=0.3)
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ax1.legend()
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# 转向角速度
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turn_states = ['TURN_LEFT_LIGHT', 'TURN_RIGHT_LIGHT', 'TURN_LEFT_HEAVY', 'TURN_RIGHT_HEAVY']
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for state in turn_states:
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if state in self.states_data:
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dlist = self.states_data[state]
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start_idx = len(dlist) // 4
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end_idx = 3 * len(dlist) // 4
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stable_data = dlist[start_idx:end_idx]
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steering_offsets = [d.steering_pwm - 1500 for d in stable_data]
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yaw_rates = [d.yaw_rate for d in stable_data]
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ax2.scatter(steering_offsets, yaw_rates, label=state, alpha=0.5, s=20)
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ax2.axhline(0, color='k', linestyle='--', linewidth=0.5)
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ax2.axvline(0, color='k', linestyle='--', linewidth=0.5)
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ax2.set_xlabel('舵机PWM偏移 (相对1500)', fontsize=12)
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ax2.set_ylabel('角速度 (rad/s)', fontsize=12)
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ax2.set_title('舵机PWM-角速度关系', fontsize=14)
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ax2.grid(True, alpha=0.3)
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ax2.legend()
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plt.tight_layout()
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plt.savefig(output_dir / 'pwm_relationships.png', dpi=150)
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print(f"保存: {output_dir / 'pwm_relationships.png'}")
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plt.close()
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# 4. 加速度分析
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fig, ax = plt.subplots(figsize=(12, 6))
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accel_states = ['ACCEL_LOW', 'ACCEL_MID', 'ACCEL_HIGH', 'BRAKE']
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for state in accel_states:
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if state in self.states_data:
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dlist = self.states_data[state]
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times_rel = [(d.time_ms - dlist[0].time_ms) / 1000.0 for d in dlist]
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vels = [d.velocity for d in dlist]
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ax.plot(times_rel, vels, marker='o', markersize=3, label=state, linewidth=2)
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ax.set_xlabel('相对时间 (秒)', fontsize=12)
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ax.set_ylabel('速度 (m/s)', fontsize=12)
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ax.set_title('加速/制动特性', fontsize=14)
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ax.grid(True, alpha=0.3)
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ax.legend()
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plt.tight_layout()
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plt.savefig(output_dir / 'acceleration.png', dpi=150)
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print(f"保存: {output_dir / 'acceleration.png'}")
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plt.close()
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def generate_json_report(self, output_file: Path):
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"""生成JSON参数报告"""
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print("\n=== 生成JSON参数报告 ===")
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forward_model = self.analyze_forward_model()
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steering_model = self.analyze_steering_model()
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delays = self.analyze_delays()
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report = {
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'metadata': {
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'log_file': str(self.log_file),
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'total_samples': len(self.data),
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'duration_seconds': (self.data[-1].time_ms - self.data[0].time_ms) / 1000.0,
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'enu_range': {
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'x_min': min(d.enu_x for d in self.data),
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'x_max': max(d.enu_x for d in self.data),
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'y_min': min(d.enu_y for d in self.data),
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'y_max': max(d.enu_y for d in self.data),
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'note': 'ENU coordinates computed onboard, relative to first RTK fix'
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},
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'states': {state: len(dlist) for state, dlist in self.states_data.items()}
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},
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'differential_drive_model': {
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'forward_model': forward_model,
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'steering_model': steering_model,
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'delays': delays
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},
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'recommended_config': self._generate_recommended_config(forward_model, steering_model, delays)
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}
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with open(output_file, 'w', encoding='utf-8') as f:
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json.dump(report, f, indent=2, ensure_ascii=False)
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print(f"JSON报告已保存: {output_file}")
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return report
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def _generate_recommended_config(self, forward_model: Dict, steering_model: Dict, delays: Dict) -> Dict:
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"""生成推荐的配置参数"""
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config = {}
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# 前向模型参数
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if 'pwm_to_velocity' in forward_model:
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config['MC_CFG_FORWARD_K'] = forward_model['pwm_to_velocity']['k']
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config['MC_CFG_FORWARD_BIAS'] = forward_model['pwm_to_velocity']['bias']
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# 转向模型参数
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if 'pwm_to_yaw_rate' in steering_model:
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config['MC_CFG_STEERING_K'] = steering_model['pwm_to_yaw_rate']['k']
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# 加速度限制
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if 'acceleration' in forward_model:
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max_accel = max(forward_model['acceleration'].values()) if forward_model['acceleration'] else 1.0
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config['MC_CFG_MAX_ACCELERATION'] = max_accel
|
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if 'deceleration' in forward_model:
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config['MC_CFG_MAX_DECELERATION'] = abs(forward_model['deceleration'])
|
|
# 响应延迟
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if 'throttle_response_delay_ms' in delays:
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config['MC_CFG_RESPONSE_DELAY_MS'] = delays['throttle_response_delay_ms']
|
|
return config
|
|
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def main():
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parser = argparse.ArgumentParser(description='车辆运动模型校准数据分析工具')
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parser.add_argument('log_file', type=str, help='校准日志文件路径 (.log或.txt)')
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parser.add_argument('-o', '--output', type=str, default='calibration_results',
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help='输出目录 (默认: calibration_results)')
|
|
args = parser.parse_args()
|
|
log_file = Path(args.log_file)
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if not log_file.exists():
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print(f"错误: 日志文件不存在: {log_file}")
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sys.exit(1)
|
|
output_dir = Path(args.output)
|
|
print("=" * 60)
|
print("车辆运动模型校准数据分析工具")
|
print("=" * 60)
|
|
# 创建分析器
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analyzer = CalibrationAnalyzer(str(log_file))
|
|
# 解析日志
|
analyzer.parse_log()
|
|
# 计算ENU坐标和速度
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analyzer.compute_enu_and_velocity()
|
|
# 生成可视化图表
|
analyzer.generate_plots(output_dir)
|
|
# 生成JSON报告
|
json_file = output_dir / 'calibration_parameters.json'
|
report = analyzer.generate_json_report(json_file)
|
|
# 打印推荐配置
|
print("\n" + "=" * 60)
|
print("推荐的配置参数 (可复制到motion_config.h):")
|
print("=" * 60)
|
for key, value in report['recommended_config'].items():
|
if isinstance(value, float):
|
print(f"#define {key:<35} ({value:.8f}f)")
|
else:
|
print(f"#define {key:<35} ({value})")
|
|
print("\n" + "=" * 60)
|
print("分析完成！")
|
print(f"输出目录: {output_dir.absolute()}")
|
print("=" * 60)
|
|
|
if __name__ == '__main__':
|
main()
|
