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7 Commits

Author SHA1 Message Date
liuhaoyan
db4f4f1fc2 Local control algorithm test only
1.stanley test data accomplishment

2.lqr test data accomplishment
2026-10-09 17:57:17 +08:00
liuhaoyan
e301e7cf67 Stanley control algorithm add
For real vehicle test
2026-09-24 10:32:19 +08:00
liuhaoyan
e02964f617 Tune lqr control algorithm 2026-09-17 16:56:30 +08:00
liuhaoyan
97dd2859e1 Optimize control process
1.fix pp algorithm

2.fix point interpolate yaw
2026-09-16 13:51:19 +08:00
liuhaoyan
fe65324a48 transform path and optimize control algorithm 2026-09-11 17:37:47 +08:00
liuhaoyan
2dc7d7013d Test branch: 2026-09-10 17:23:47 +08:00
liuhaoyan
d63c1435b6 Test branch 2026-09-09 18:23:44 +08:00
288 changed files with 87970 additions and 2832 deletions

2
.gitignore vendored
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logs/
data/
files/
*.png

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auto drive misson center webots simulation:

40
Readme.md Normal file
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# auto drive misson center webots simulation:
lhy本地控制测试分支,无本地控制测试需求请勿使用
## 1, `ubuntu20.04`, `ubuntu22.04`
## 2, 安装 `webots`
`https://github.com/cyberbotics/webots/tags`, 版本: `ubuntu20.04: R2023a, ubuntu22.04:R2025a`
## 3, 创建ros2 工作空间
如 `~/sweet_ws `, 编译 `sweet_webots_robot, robots_rviz_panel, webots_simulation, auto_drive_proj_utils,auto_drive_proj_msg, auto_drive_misson_center`
```shell
cd ~/sweet_ws
colcon build --packages-select sweet_webots_robot --cmake-clean-cache
source install/setup.bash
colcon build --packages-select robots_rviz_panel --cmake-clean-cache
source install/setup.bash
colcon build --packages-select webots_simulation --cmake-clean-cache
source install/setup.bash
colcon build --packages-select auto_drive_proj_utils --cmake-clean-cache
source install/setup.bash
colcon build --packages-select auto_drive_proj_msg --cmake-clean-cache
source install/setup.bash
colcon build --packages-select auto_drive_misson_center --cmake-clean-cache
source install/setup.bash
```
## 4, 运行 webots:
终端执行命令: `webots`
## 5, 运行 webots 与 rviz可视化界面
`ros2 launch webots_simulation robot_launch.py` ,等待rviz2界面打开后,加载配置文件:` File->Open Config->` 选择 `SweetAiRviz.rviz` ,方便起见,打开后,File->Save Config 下次不用再重复加载。该 rviz界面 左右两侧拉开留出视图空间。(test:1701895544295370752.json)
测试版本中path_visualizer pkg目前与launch一起启动,其路径代码可在src/webots_simulation/src/PathVisualizer.cpp 中修改file_name实现
## 6, 模拟 `/start_pose` 服务
另启一个终端,模拟 `/start_pose` 服务,常开,执行脚本(注意路径):`python3 /home/ada/sweet_ws/install/auto_drive_misson_center/share/auto_drive_misson_center/scripts/mock_start_pose_and_publish_rtk.py `
## 7, 运行 mission_center 任务节点
运行 `ros2 launch auto_drive_misson_center mission_center_launch.py` 启动 任务节点。
## 8, 请求 /invoke/missionOn 服务
另启一个终端,执行发送任务命令,这里的任务,必须和 `5` 中的匹配,将1701895544295370752.json内容填充到指令中:
```shell
ros2 service call /invoke/missionOn auto_drive_proj_msg/srv/ReqString "{request: '[{\"name\":\"value\",\"value\":\"https://test.sweetai.cn:4433/load/locationCmdPath/1701895544295370752.json\"}]', value: ''}"
```
> 观察任务轨迹以及rviz2右侧panel面板参数
rviz2 panel:
![界面示意图](./rviz2.jpg)

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error_calculate.py Normal file
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import re
import csv
from pathlib import Path
import numpy as np
import matplotlib.pyplot as plt
# ============================================================
# 参数
# ============================================================
LOG_PATTERN = "path_visualizer.log"
# 每 5 个完整数据取 1 个
SAMPLE_STEP = 1
CSV_FILE = "tracking_error.csv"
# ============================================================
# 1. 查找日志文件
# ============================================================
files = sorted(
Path(".").glob(LOG_PATTERN)
)
if not files:
raise FileNotFoundError(
f"当前目录没有找到:{LOG_PATTERN}"
)
print("找到日志文件:")
for file in files:
print(" ", file)
# ============================================================
# 2. 正则表达式
# ============================================================
# ------------------------------------------------------------
# current pose
#
# [debug] [path_follow]
# [current_pose_x:11.57,
# current_pose_y:-2.13,
# current_pose_yaw:-3.10]
# ------------------------------------------------------------
current_pose_pattern = re.compile(
r"current_pose_x:([-+0-9.eE]+),\s*"
r"current_pose_y:([-+0-9.eE]+),\s*"
r"current_pose_yaw:([-+0-9.eE]+)"
)
# ------------------------------------------------------------
# current path
#
# [debug] [path_follow]
# [current_path_x:11.59,
# current_path_y:-2.12,
# current_path_yaw:-3.10]
# ------------------------------------------------------------
current_path_pattern = re.compile(
r"current_path_x:([-+0-9.eE]+),\s*"
r"current_path_y:([-+0-9.eE]+),\s*"
r"current_path_yaw:([-+0-9.eE]+)"
)
yaw_command_pattern = re.compile(
r"current_cmd_angular_z:([-+0-9.eE]+)"
)
# ============================================================
# 3. 函数:角度归一化
# ============================================================
def normalize_angle(angle):
return (
angle + np.pi
) % (
2.0 * np.pi
) - np.pi
# ============================================================
# 4. 函数:根据 current pose + path 重新计算
# ============================================================
def calculate_errors(
current_x,
current_y,
current_yaw,
path_x,
path_y,
path_yaw
):
# --------------------------------------------------------
# 世界坐标系下的位置差
# --------------------------------------------------------
dx = path_x - current_x
dy = path_y - current_y
# --------------------------------------------------------
# 转换到车辆坐标系
#
# vehicle frame:
#
# x
# ↑
# |
# |
# ● -----> y
#
# 根据当前 yaw 进行旋转
# --------------------------------------------------------
lon_dis = (
np.cos(current_yaw) * dx
+
np.sin(current_yaw) * dy
)
dis = (
-np.sin(current_yaw) * dx
+
np.cos(current_yaw) * dy
)
# --------------------------------------------------------
# 车辆到路径点的欧氏距离
# --------------------------------------------------------
distance = np.sqrt(
dx * dx +
dy * dy
)
# --------------------------------------------------------
# 航向误差
# --------------------------------------------------------
alpha = normalize_angle(
path_yaw - current_yaw
)
# --------------------------------------------------------
# deg
# --------------------------------------------------------
alpha_deg = np.degrees(alpha)
return (
distance,
lon_dis,
dis,
alpha,
alpha_deg
)
# ============================================================
# 5. 读取日志
# ============================================================
all_data = []
yaw_command_data = []
total_raw_count = 0
for file in files:
print()
print("=" * 70)
print(f"读取:{file}")
print("=" * 70)
# --------------------------------------------------------
# 每个文件单独计数
# --------------------------------------------------------
file_data_count = 0
# --------------------------------------------------------
# 临时保存 current pose
# --------------------------------------------------------
pending_pose = None
pending_yaw = None
with file.open(
"r",
encoding="utf-8",
errors="ignore"
) as f:
for line in f:
# =================================================
# current pose
# =================================================
m_pose = current_pose_pattern.search(
line
)
m_yaw = yaw_command_pattern.search(line)
if m_yaw:
pending_yaw = float(m_yaw.group(1))
if file_data_count % SAMPLE_STEP == 0:
yaw_command_data.append(pending_yaw)
if m_pose:
pending_pose = {
"x": float(
m_pose.group(1)
),
"y": float(
m_pose.group(2)
),
"yaw": float(
m_pose.group(3)
),
}
continue
# =================================================
# current path
# =================================================
m_path = current_path_pattern.search(
line
)
if (
m_path is not None
and
pending_pose is not None
):
path_x = float(
m_path.group(1)
)
path_y = float(
m_path.group(2)
)
path_yaw = float(
m_path.group(3)
)
# =================================================
# 重新计算
# =================================================
(
point_distance,
lon_dis,
dis,
alpha,
alpha_deg
) = calculate_errors(
pending_pose["x"],
pending_pose["y"],
pending_pose["yaw"],
path_x,
path_y,
path_yaw
)
# =================================================
# 每 SAMPLE_STEP 个数据取一个
# =================================================
if file_data_count % SAMPLE_STEP == 0:
all_data.append({
"file": file.name,
"current_x":
pending_pose["x"],
"current_y":
pending_pose["y"],
"current_yaw":
pending_pose["yaw"],
"path_x":
path_x,
"path_y":
path_y,
"path_yaw":
path_yaw,
"point_distance":
point_distance,
"lon_dis":
lon_dis,
"dis":
dis,
"alpha":
alpha,
"alpha_deg":
alpha_deg,
})
file_data_count += 1
total_raw_count += 1
# ------------------------------------------------
# 清空
# ------------------------------------------------
pending_pose = None
print(
f"原始完整数据:{file_data_count}"
)
print(
f"抽样后数据:"
f"{(file_data_count + SAMPLE_STEP - 1) // SAMPLE_STEP}"
)
# ============================================================
# 6. 检查
# ============================================================
if not all_data:
raise RuntimeError(
"没有解析到 current_pose + current_path 数据。"
)
print()
print("=" * 70)
print("解析完成")
print("=" * 70)
print(
f"原始数据:{total_raw_count}"
)
print(
f"最终数据:{len(all_data)}"
)
# ============================================================
# 7. 提取数据
# ============================================================
current_x = np.array([
d["current_x"]
for d in all_data
])
current_y = np.array([
d["current_y"]
for d in all_data
])
current_yaw = np.array([
d["current_yaw"]
for d in all_data
])
path_x = np.array([
d["path_x"]
for d in all_data
])
path_y = np.array([
d["path_y"]
for d in all_data
])
path_yaw = np.array([
d["path_yaw"]
for d in all_data
])
point_distance = np.array([
d["point_distance"]
for d in all_data
])
lon_dis = np.array([
d["lon_dis"]
for d in all_data
])
dis = np.array([
d["dis"]
for d in all_data
])
alpha = np.array([
d["alpha"]
for d in all_data
])
alpha_deg = np.array([
d["alpha_deg"]
for d in all_data
])
yaw_command = yaw_command_data# 确保长度一致
print(
f"yaw size: {len(yaw_command)}"
)
# ============================================================
# 8. 计算车辆累计行驶距离
# ============================================================
if len(path_x) > 1:
dx = np.diff(
path_x
)
dy = np.diff(
path_y
)
step_distance = np.sqrt(
dx ** 2 +
dy ** 2
)
else:
step_distance = np.array([])
for i in range(len(step_distance)
):
if step_distance[i] > 0.5:
step_distance[i] = 0.0
print(
f"Warning: step_distance[{i}] = {step_distance[i]:.3f} m, which is too large. Setting it to 0.0.")
distance_m = np.zeros(
len(path_x)
)
if len(path_x) > 1:
distance_m[1:] = np.cumsum(
step_distance
)
# ============================================================
# 9. 保存 CSV
# ============================================================
print()
print("=" * 70)
print("保存 CSV")
print("=" * 70)
with open(
CSV_FILE,
"w",
newline="",
encoding="utf-8-sig"
) as f:
writer = csv.writer(f)
writer.writerow([
"distance_m",
"current_x_m",
"current_y_m",
"lateral_error_m",
"heading_error_rad",
"heading_error_deg",
"yaw_command",
])
for i in range(
len(all_data)
):
if i>= len(yaw_command):
break
writer.writerow([
f"{distance_m[i]:.8f}",
f"{current_x[i]:.8f}",
f"{current_y[i]:.8f}",
f"{dis[i]:.8f}",
f"{alpha[i]:.8f}",
f"{alpha_deg[i]:.8f}",
f"{yaw_command[i]:.8f}"
])
print(
f"CSV 已保存:"
f"{Path(CSV_FILE).resolve()}"
)
# ============================================================
# 11. 横向误差
# ============================================================
plt.figure(
figsize=(12, 5)
)
plt.plot(
distance_m,
dis,
linewidth=1.2
)
plt.axhline(
0.0,
linestyle="--",
linewidth=0.8
)
plt.xlabel(
"Distance (m)"
)
plt.ylabel(
"Lateral Error (m)"
)
plt.title(
"Recalculated Lateral Error"
)
plt.grid(True)
plt.tight_layout()
plt.show()
# ============================================================
# 13. 航向误差 rad
# ============================================================
plt.figure(
figsize=(12, 5)
)
plt.plot(
distance_m,
alpha,
linewidth=1.2
)
plt.axhline(
0.0,
linestyle="--",
linewidth=0.8
)
plt.xlabel(
"Distance (m)"
)
plt.ylabel(
"Heading Error (rad)"
)
plt.title(
"Recalculated Heading Error"
)
plt.grid(True)
plt.tight_layout()
plt.show()
# ============================================================
# 14. yaw command rad
# ============================================================
plt.figure(
figsize=(12, 5)
)
plt.plot(
distance_m,
yaw_command,
linewidth=1.2
)
plt.axhline(
0.0,
linestyle="--",
linewidth=0.8
)
plt.xlabel(
"Distance (m)"
)
plt.ylabel(
"Yaw Command (rad)"
)
plt.title(
"Recalculated Yaw Command"
)
plt.grid(True)
plt.tight_layout()
plt.show()
# ============================================================
# 15. 车辆轨迹 + 路径点
# ============================================================
plt.figure(
figsize=(8, 8)
)
plt.plot(
current_x,
current_y,
linewidth=1.2,
label="Current Pose"
)
plt.plot(
path_x,
path_y,
linestyle="--",
linewidth=1.0,
label="Current Path Point"
)
plt.scatter(
current_x[0],
current_y[0],
s=50,
label="Start"
)
plt.scatter(
current_x[-1],
current_y[-1],
s=50,
label="End"
)
plt.xlabel(
"X (m)"
)
plt.ylabel(
"Y (m)"
)
plt.title(
"Current Pose and Path"
)
plt.axis(
"equal"
)
plt.grid(True)
plt.legend()
plt.tight_layout()
plt.show()
# ============================================================
# 16. 误差统计
# ============================================================
print()
print("=" * 70)
print("误差统计")
print("=" * 70)
print(
f"最大横向误差:"
f"{np.max(np.abs(dis)):.6f} m"
)
print(
f"平均绝对横向误差:"
f"{np.mean(np.abs(dis)):.6f} m"
)
print(
f"横向误差 RMSE:"
f"{np.sqrt(np.mean(dis ** 2)):.6f} m"
)
print()
print(
f"最大纵向误差:"
f"{np.max(np.abs(lon_dis)):.6f} m"
)
print(
f"平均绝对纵向误差:"
f"{np.mean(np.abs(lon_dis)):.6f} m"
)
print(
f"纵向误差 RMSE:"
f"{np.sqrt(np.mean(lon_dis ** 2)):.6f} m"
)
print()
print(
f"最大航向误差:"
f"{np.max(np.abs(alpha_deg)):.6f} deg"
)
print(
f"平均绝对航向误差:"
f"{np.mean(np.abs(alpha_deg)):.6f} deg"
)
print(
f"航向误差 RMSE:"
f"{np.sqrt(np.mean(alpha_deg ** 2)):.6f} deg"
)
print()
print(
f"最大车辆-路径点距离:"
f"{np.max(point_distance):.6f} m"
)
print(
f"平均车辆-路径点距离:"
f"{np.mean(point_distance):.6f} m"
)
print(
f"总行驶距离:"
f"{distance_m[-1]:.3f} m"
)

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import csv
import os
import re
import matplotlib.pyplot as plt
import numpy as np
import glob
# files = [
# "stanley_*_*_*_*.csv",
# ]
files = [
"lqr_*_*_*_*_*.csv",
]
data = {}
for pattern in files:
matched_files = glob.glob(pattern)
if not matched_files:
raise FileNotFoundError(
f"找不到文件: {pattern}"
)
matched_files.sort(
key=lambda x: (
float(x.split("_")[1]), # 速度
float(x.split("_")[2]), # 参数1
float(x.split("_")[3]), # 参数2
float(x.split("_")[4]), # 参数2
float(x.split("_")[5].replace(".csv", "")) # 半径
)
)
print(f"匹配到 {len(matched_files)} 个文件:")
for file in matched_files:
if not os.path.exists(file):
raise FileNotFoundError(f"找不到文件: {file}")
distance = []
lateral_error = []
heading_error = []
yaw_command = []
with open(file, "r", encoding="utf-8-sig", newline="") as f:
reader = csv.DictReader(f)
required = {"distance_m", "lateral_error_m", "heading_error_rad","yaw_command"}
if not required.issubset(reader.fieldnames or []):
raise ValueError(
f"{file} 缺少必要列,需要: {required},实际列: {reader.fieldnames}"
)
for row in reader:
try:
distance.append(float(row["distance_m"]))
lateral_error.append(abs(float(row["lateral_error_m"])))
heading_error.append(abs(float(row["heading_error_rad"])))
yaw_command.append(float(row["yaw_command"]))
except (ValueError, TypeError):
continue
name = os.path.splitext(os.path.basename(file))[0]
data[name] = {
"distance": distance,
"lateral_error": lateral_error,
"heading_error": heading_error,
"yaw_command": yaw_command,
}
# 颜色由 matplotlib 自动分配,不手动指定
names = list(data.keys())
# =========================
# 图1:Lateral Error
# =========================
plt.figure(figsize=(12, 6))
for name in names:
plt.plot(
data[name]["distance"][::15],
data[name]["lateral_error"][::15],
label=name,
linewidth=1.5,
)
plt.xlabel("Distance (m)")
plt.ylabel("Lateral Error (m)")
plt.title("Lateral Error Comparison")
plt.grid(True, alpha=0.3)
plt.legend()
plt.tight_layout()
lateral_output = "X_Lateral_error_comparison.png"
plt.savefig(lateral_output, dpi=200)
plt.show()
# =========================
# 图2:Heading Error
# =========================
plt.figure(figsize=(12, 6))
for name in names:
plt.plot(
data[name]["distance"],
data[name]["heading_error"],
label=name,
linewidth=1.5,
)
plt.axhline(0, linewidth=2.0)
plt.xlabel("Distance (m)")
plt.ylabel("Heading Error (rad)")
plt.title("Heading Error Comparison")
plt.grid(True, alpha=0.3)
plt.legend()
plt.tight_layout()
heading_output = "X_Heading_error_comparison.png"
plt.savefig(heading_output, dpi=200)
plt.show()
# =========================
# 图3:Yaw Command
# =========================
plt.figure(figsize=(12, 6))
for name in names:
plt.plot(
data[name]["distance"],
data[name]["yaw_command"],
label=name,
linewidth=1.5,
)
plt.axhline(0, linewidth=2.0)
plt.xlabel("Distance (m)")
plt.ylabel("Yaw Command (rad)")
plt.title("Yaw Command Comparison")
plt.grid(True, alpha=0.3)
plt.legend()
plt.tight_layout()
yaw_output = "X_Yaw_command_comparison.png"
plt.savefig(yaw_output, dpi=200)
plt.show()
print("读取完成:")
for name in names:
print(f"{name}: {len(data[name]['distance'])} points")
print("\n已保存:")
print(lateral_output)
print(heading_output)
print(yaw_output)
print("\n" + "=" * 90)
print(
f"{'Controller':<20}"
f"{'Lat Max(m)':>12}"
f"{'Lat Mean(m)':>13}"
f"{'Lat RMSE(m)':>14}"
f"{'Lat P95(m)':>13}"
f"{'Head Max(deg)':>15}"
f"{'Head RMSE(deg)':>16}"
)
print("=" * 100)
for name in names:
lat = np.array(data[name]["lateral_error"])
head = np.array(data[name]["heading_error"])
print(
f"{name:<20}"
f"{np.max(lat):>12.4f}"
f"{np.mean(lat):>13.4f}"
f"{np.sqrt(np.mean(lat**2)):>14.4f}"
f"{np.percentile(lat, 95):>13.4f}"
f"{np.degrees(np.max(head)):>15.4f}"
f"{np.degrees(np.sqrt(np.mean(head**2))):>16.4f}"
)
print("=" * 100)

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distance_m,current_x_m,current_y_m,lateral_error_m,heading_error_rad,heading_error_deg,yaw_command
0.00000000,0.00000000,0.00000000,0.00000000,0.00000777,0.00044540,0.00001562
0.00000000,0.00000000,0.00000000,0.00000000,0.00000777,0.00044540,0.00001562
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26.03786858,-3.52161211,9.50909370,-0.01923981,0.01468499,0.84138802,0.00000000
26.03786858,-3.52161211,9.50909370,-0.01923981,0.01468499,0.84138802,0.00000000
26.03786858,-3.52153683,9.56837078,-0.02006748,0.01498909,0.85881183,0.00000000
26.03786858,-3.52153683,9.56837078,-0.02006748,0.01498909,0.85881183,0.00000000
26.03786858,-3.52140215,9.61761260,-0.02071830,0.01529296,0.87622199,0.00000000
26.03786858,-3.52140215,9.61761260,-0.02071830,0.01529296,0.87622199,0.00000000
26.03786858,-3.52140215,9.61761260,-0.02071830,0.01529296,0.87622199,0.00000000
26.03786858,-3.52121341,9.65682636,-0.02118852,0.01559647,0.89361165,0.00000000
26.03786858,-3.52121341,9.65682636,-0.02118852,0.01559647,0.89361165,0.00000000
26.03786858,-3.52097697,9.68601657,-0.02147536,0.01589950,0.91097399,0.00000000
26.03786858,-3.52097697,9.68601657,-0.02147536,0.01589950,0.91097399,0.00000000
26.03786858,-3.52097697,9.68601657,-0.02147536,0.01589950,0.91097399,0.00000000
26.03786858,-3.52069898,9.70518549,-0.02157590,0.01620217,0.92831584,0.00000000
26.03786858,-3.52069898,9.70518549,-0.02157590,0.01620217,0.92831584,0.00000000
26.03786858,-3.52038555,9.71433425,-0.02148709,0.01650424,0.94562354,0.00000000
26.03786858,-3.52038555,9.71433425,-0.02148709,0.01650424,0.94562354,0.00000000
26.03786858,-3.52038555,9.71433425,-0.02148709,0.01650424,0.94562354,0.00000000
26.03786858,-3.52021554,9.71512122,-0.02136839,0.01665528,0.95427739,0.00000000
26.03786858,-3.52021554,9.71512122,-0.02136839,0.01665528,0.95427739,0.00000000
26.03786858,-3.52021554,9.71512122,-0.02136839,0.01665528,0.95427739,0.00000000
26.03786858,-3.52021482,9.71508470,-0.02136706,0.01665528,0.95427739,0.00000000
26.03786858,-3.52021482,9.71508470,-0.02136706,0.01665528,0.95427739,0.00000000
26.03786858,-3.52021456,9.71506980,-0.02136655,0.01665528,0.95427739,0.00000000
26.03786858,-3.52021456,9.71506980,-0.02136655,0.01665528,0.95427739,0.00000000
26.03786858,-3.52021456,9.71506980,-0.02136655,0.01665528,0.95427739,0.00000000
26.03786858,-3.52021449,9.71506371,-0.02136638,0.01665528,0.95427739,0.00000000
26.03786858,-3.52021449,9.71506371,-0.02136638,0.01665528,0.95427739,0.00000000
26.03786858,-3.52021449,9.71506119,-0.02136634,0.01665528,0.95427739,0.00000000
26.03786858,-3.52021449,9.71506119,-0.02136634,0.01665528,0.95427739,0.00000000
26.03786858,-3.52021449,9.71506119,-0.02136634,0.01665528,0.95427739,0.00000000
26.03786858,-3.52021452,9.71506013,-0.02136635,0.01665528,0.95427739,0.00000000
26.03786858,-3.52021452,9.71506013,-0.02136635,0.01665528,0.95427739,0.00000000
26.03786858,-3.52021452,9.71506013,-0.02136635,0.01665528,0.95427739,0.00000000
26.03786858,-3.52021456,9.71505966,-0.02136639,0.01665528,0.95427739,0.00000000
26.03786858,-3.52021456,9.71505966,-0.02136639,0.01665528,0.95427739,0.00000000
26.03786858,-3.52021462,9.71505942,-0.02136640,0.01665516,0.95427056,0.00000000
1 distance_m current_x_m current_y_m lateral_error_m heading_error_rad heading_error_deg yaw_command
2 0.00000000 0.00000000 0.00000000 0.00000000 0.00000777 0.00044540 0.00001562
3 0.00000000 0.00000000 0.00000000 0.00000000 0.00000777 0.00044540 0.00001562
4 0.00000000 0.00040876 0.00189316 -0.00189316 0.00001089 0.00062371 -0.00372659
5 0.00000000 0.00040876 0.00189316 -0.00189316 0.00001089 0.00062371 -0.00372659
6 0.00000000 0.00040876 0.00189316 -0.00189316 0.00001089 0.00062371 -0.00372659
7 0.00000000 0.00434336 0.00273577 -0.00273582 0.00001199 0.00068720 -0.00539272
8 0.00000000 0.00434336 0.00273577 -0.00273582 0.00001199 0.00068720 -0.00539272
9 0.00000000 0.01825690 0.00331407 -0.00331429 0.00001238 0.00070915 -0.00653699
10 0.00000000 0.01825690 0.00331407 -0.00331429 0.00001238 0.00070915 -0.00653699
11 0.00000000 0.01825690 0.00331407 -0.00331429 0.00001238 0.00070915 -0.00653699
12 0.00000000 0.04220500 0.00389884 -0.00389936 0.00001250 0.00071617 -0.00769459
13 0.00000000 0.04220500 0.00389884 -0.00389936 0.00001250 0.00071617 -0.00769459
14 0.00000000 0.07620478 0.00460014 -0.00460109 0.00001252 0.00071734 -0.00908313
15 0.00000000 0.07620478 0.00460014 -0.00460109 0.00001252 0.00071734 -0.00908313
16 0.00000000 0.07620478 0.00460014 -0.00460109 0.00001252 0.00071734 -0.00908313
17 0.19371847 0.12026528 0.00546277 -0.00546180 0.00001252 0.00071743 -0.01079102
18 0.19371847 0.12026528 0.00546277 -0.00546180 0.00001252 0.00071743 -0.01079102
19 0.19371847 0.12026528 0.00546277 -0.00546180 0.00001252 0.00071743 -0.01079102
20 0.19371847 0.17439076 0.00650497 -0.00650467 0.00001255 0.00071882 -0.01285453
21 0.19371847 0.17439076 0.00650497 -0.00650467 0.00001255 0.00071882 -0.01285453
22 0.19371847 0.23858285 0.00773414 -0.00773465 0.00001261 0.00072263 -0.01528815
23 0.19371847 0.23858285 0.00773414 -0.00773465 0.00001261 0.00072263 -0.01528815
24 0.19371847 0.23858285 0.00773414 -0.00773465 0.00001261 0.00072263 -0.01528815
25 0.38743695 0.31231190 0.00900941 -0.00899935 0.00013250 0.00759168 -0.01757225
26 0.38743695 0.31231190 0.00900941 -0.00899935 0.00013250 0.00759168 -0.01757225
27 0.38743695 0.38826441 0.00986294 -0.00986336 0.00064879 0.03717283 -0.01822504
28 0.38743695 0.38826441 0.00986294 -0.00986336 0.00064879 0.03717283 -0.01822504
29 0.38743695 0.38826441 0.00986294 -0.00986336 0.00064879 0.03717283 -0.01822504
30 0.38743695 0.46418652 0.01061130 -0.01070499 0.00122214 0.07002355 -0.01855497
31 0.38743695 0.46418652 0.01061130 -0.01070499 0.00122214 0.07002355 -0.01855497
32 0.58115542 0.54009715 0.01126934 -0.01119380 0.00183552 0.10516770 -0.01862555
33 0.58115542 0.54009715 0.01126934 -0.01119380 0.00183552 0.10516770 -0.01862555
34 0.58115542 0.54009715 0.01126934 -0.01119380 0.00183552 0.10516770 -0.01862555
35 0.58115542 0.61599213 0.01186707 -0.01195259 0.00246072 0.14098913 -0.01855305
36 0.58115542 0.61599213 0.01186707 -0.01195259 0.00246072 0.14098913 -0.01855305
37 0.58115542 0.61599213 0.01186707 -0.01195259 0.00246072 0.14098913 -0.01855305
38 0.77487390 0.69186015 0.01241582 -0.01215927 0.00308717 0.17688157 -0.01838100
39 0.77487390 0.69186015 0.01241582 -0.01215927 0.00308717 0.17688157 -0.01838100
40 0.77487390 0.76772778 0.01292068 -0.01289384 0.00371263 0.21271818 -0.01812410
41 0.77487390 0.76772778 0.01292068 -0.01289384 0.00371263 0.21271818 -0.01812410
42 0.77487390 0.76772778 0.01292068 -0.01289384 0.00371263 0.21271818 -0.01812410
43 0.77487390 0.84356382 0.01339605 -0.01369372 0.00433858 0.24858223 -0.01780785
44 0.77487390 0.84356382 0.01339605 -0.01369372 0.00433858 0.24858223 -0.01780785
45 0.77487390 0.84356382 0.01339605 -0.01369372 0.00433858 0.24858223 -0.01780785
46 0.96859237 0.91940390 0.01383464 -0.01359048 0.00495494 0.28389732 -0.01743792
47 0.96859237 0.91940390 0.01383464 -0.01359048 0.00495494 0.28389732 -0.01743792
48 0.96859237 0.99524364 0.01423726 -0.01438499 0.00556171 0.31866261 -0.01701615
49 0.96859237 0.99524364 0.01423726 -0.01438499 0.00556171 0.31866261 -0.01701615
50 0.96859237 0.99524364 0.01423726 -0.01438499 0.00556171 0.31866261 -0.01701615
51 1.16231085 1.07108650 0.01460523 -0.01404284 0.00615841 0.35285088 -0.01654590
52 1.16231085 1.07108650 0.01460523 -0.01404284 0.00615841 0.35285088 -0.01654590
53 1.16231085 1.14692912 0.01494714 -0.01484283 0.00673834 0.38607863 -0.01605783
54 1.16231085 1.14692912 0.01494714 -0.01484283 0.00673834 0.38607863 -0.01605783
55 1.16231085 1.14692912 0.01494714 -0.01484283 0.00673834 0.38607863 -0.01605783
56 1.16231085 1.22277348 0.01526737 -0.01570793 0.00729865 0.41818177 -0.01556627
57 1.16231085 1.22277348 0.01526737 -0.01570793 0.00729865 0.41818177 -0.01556627
58 1.16231085 1.22277348 0.01526737 -0.01570793 0.00729865 0.41818177 -0.01556627
59 1.35602932 1.29862078 0.01556002 -0.01510874 0.00784598 0.44954160 -0.01504606
60 1.35602932 1.29862078 0.01556002 -0.01510874 0.00784598 0.44954160 -0.01504606
61 1.35602932 1.37446715 0.01583085 -0.01598435 0.00837606 0.47991305 -0.01451741
62 1.35602932 1.37446715 0.01583085 -0.01598435 0.00837606 0.47991305 -0.01451741
63 1.35602932 1.37446715 0.01583085 -0.01598435 0.00837606 0.47991305 -0.01451741
64 1.35602932 1.45031631 0.01607619 -0.01691367 0.00889318 0.50954177 -0.01396432
65 1.35602932 1.45031631 0.01607619 -0.01691367 0.00889318 0.50954177 -0.01396432
66 1.54974779 1.52616499 0.01630701 -0.01608445 0.00938873 0.53793487 -0.01342571
67 1.54974779 1.52616499 0.01630701 -0.01608445 0.00938873 0.53793487 -0.01342571
68 1.54974779 1.52616499 0.01630701 -0.01608445 0.00938873 0.53793487 -0.01342571
69 1.54974779 1.60201474 0.01652214 -0.01703651 0.00986511 0.56522900 -0.01289467
70 1.54974779 1.60201474 0.01652214 -0.01703651 0.00986511 0.56522900 -0.01289467
71 1.54974779 1.60201474 0.01652214 -0.01703651 0.00986511 0.56522900 -0.01289467
72 1.74346627 1.67786556 0.01672303 -0.01604452 0.01032229 0.59142379 -0.01237388
73 1.74346627 1.67786556 0.01672303 -0.01604452 0.01032229 0.59142379 -0.01237388
74 1.74346627 1.75369789 0.01694750 -0.01705601 0.01074851 0.61584401 -0.01196208
75 1.74346627 1.75369789 0.01694750 -0.01705601 0.01074851 0.61584401 -0.01196208
76 1.74346627 1.75369789 0.01694750 -0.01705601 0.01074851 0.61584401 -0.01196208
77 1.74346627 1.82950125 0.01715149 -0.01810945 0.01115284 0.63901078 -0.01155369
78 1.74346627 1.82950125 0.01715149 -0.01810945 0.01115284 0.63901078 -0.01155369
79 1.93718474 1.90529999 0.01733959 -0.01697006 0.01153680 0.66101007 -0.01115469
80 1.93718474 1.90529999 0.01733959 -0.01697006 0.01153680 0.66101007 -0.01115469
81 1.93718474 1.90529999 0.01733959 -0.01697006 0.01153680 0.66101007 -0.01115469
82 1.93718474 1.98112841 0.01750664 -0.01802898 0.01192760 0.68340103 -0.01070035
83 1.93718474 1.98112841 0.01750664 -0.01802898 0.01192760 0.68340103 -0.01070035
84 1.93718474 1.98112841 0.01750664 -0.01802898 0.01192760 0.68340103 -0.01070035
85 2.13090322 2.05696762 0.01765309 -0.01674073 0.01231418 0.70555032 -0.01021362
86 2.13090322 2.05696762 0.01765309 -0.01674073 0.01231418 0.70555032 -0.01021362
87 2.13090322 2.13280679 0.01778827 -0.01781040 0.01268499 0.72679659 -0.00973633
88 2.13090322 2.13280679 0.01778827 -0.01781040 0.01268499 0.72679659 -0.00973633
89 2.13090322 2.13280679 0.01778827 -0.01781040 0.01268499 0.72679659 -0.00973633
90 2.13090322 2.20864673 0.01790892 -0.01892085 0.01304384 0.74735684 -0.00925433
91 2.13090322 2.20864673 0.01790892 -0.01892085 0.01304384 0.74735684 -0.00925433
92 2.32462169 2.28448763 0.01802488 -0.01748552 0.01338303 0.76679129 -0.00880240
93 2.32462169 2.28448763 0.01802488 -0.01748552 0.01338303 0.76679129 -0.00880240
94 2.32462169 2.28448763 0.01802488 -0.01748552 0.01338303 0.76679129 -0.00880240
95 2.32462169 2.36033167 0.01813966 -0.01862654 0.01370065 0.78498932 -0.00839159
96 2.32462169 2.36033167 0.01813966 -0.01862654 0.01370065 0.78498932 -0.00839159
97 2.32462169 2.36033167 0.01813966 -0.01862654 0.01370065 0.78498932 -0.00839159
98 2.51834017 2.43617681 0.01824425 -0.01709100 0.01400618 0.80249477 -0.00798478
99 2.51834017 2.43617681 0.01824425 -0.01709100 0.01400618 0.80249477 -0.00798478
100 2.51834017 2.51202082 0.01834167 -0.01824875 0.01429770 0.81919784 -0.00759203
101 2.51834017 2.51202082 0.01834167 -0.01824875 0.01429770 0.81919784 -0.00759203
102 2.51834017 2.51202082 0.01834167 -0.01824875 0.01429770 0.81919784 -0.00759203
103 2.51834017 2.58786574 0.01843518 -0.01944570 0.01457327 0.83498710 -0.00722357
104 2.51834017 2.58786574 0.01843518 -0.01944570 0.01457327 0.83498710 -0.00722357
105 2.51834017 2.58786574 0.01843518 -0.01944570 0.01457327 0.83498710 -0.00722357
106 2.71205864 2.66371151 0.01852167 -0.01780159 0.01483670 0.85008011 -0.00686552
107 2.71205864 2.66371151 0.01852167 -0.01780159 0.01483670 0.85008011 -0.00686552
108 2.71205864 2.73956017 0.01860427 -0.01901627 0.01508603 0.86436588 -0.00652817
109 2.71205864 2.73956017 0.01860427 -0.01901627 0.01508603 0.86436588 -0.00652817
110 2.71205864 2.73956017 0.01860427 -0.01901627 0.01508603 0.86436588 -0.00652817
111 2.90577711 2.81540955 0.01867855 -0.01729062 0.01532603 0.87811687 -0.00619299
112 2.90577711 2.81540955 0.01867855 -0.01729062 0.01532603 0.87811687 -0.00619299
113 2.90577711 2.89125953 0.01875190 -0.01852307 0.01555095 0.89100362 -0.00588638
114 2.90577711 2.89125953 0.01875190 -0.01852307 0.01555095 0.89100362 -0.00588638
115 2.90577711 2.89125953 0.01875190 -0.01852307 0.01555095 0.89100362 -0.00588638
116 2.90577711 2.96711906 0.01880658 -0.01977026 0.01576196 0.90309380 -0.00557073
117 2.90577711 2.96711906 0.01880658 -0.01977026 0.01576196 0.90309380 -0.00557073
118 2.90577711 2.96711906 0.01880658 -0.01977026 0.01576196 0.90309380 -0.00557073
119 3.09949559 3.04299058 0.01883154 -0.01792648 0.01596035 0.91446062 -0.00522150
120 3.09949559 3.04299058 0.01883154 -0.01792648 0.01596035 0.91446062 -0.00522150
121 3.09949559 3.11886119 0.01885593 -0.01916529 0.01614727 0.92517015 -0.00489428
122 3.09949559 3.11886119 0.01885593 -0.01916529 0.01614727 0.92517015 -0.00489428
123 3.09949559 3.11886119 0.01885593 -0.01916529 0.01614727 0.92517015 -0.00489428
124 3.09949559 3.19479213 0.01887923 -0.02043175 0.01632761 0.93550337 -0.00457812
125 3.09949559 3.19479213 0.01887923 -0.02043175 0.01632761 0.93550337 -0.00457812
126 3.29321406 3.27072781 0.01890172 -0.01852740 0.01649211 0.94492839 -0.00429207
127 3.29321406 3.27072781 0.01890172 -0.01852740 0.01649211 0.94492839 -0.00429207
128 3.29321406 3.27072781 0.01890172 -0.01852740 0.01649211 0.94492839 -0.00429207
129 3.29321406 3.34665941 0.01892186 -0.01980803 0.01664799 0.95385961 -0.00401880
130 3.29321406 3.34665941 0.01892186 -0.01980803 0.01664799 0.95385961 -0.00401880
131 3.48693254 3.42258773 0.01894242 -0.01785826 0.01679335 0.96218796 -0.00376738
132 3.48693254 3.42258773 0.01894242 -0.01785826 0.01679335 0.96218796 -0.00376738
133 3.48693254 3.42258773 0.01894242 -0.01785826 0.01679335 0.96218796 -0.00376738
134 3.48693254 3.49851417 0.01896281 -0.01915518 0.01692915 0.96996872 -0.00353494
135 3.48693254 3.49851417 0.01896281 -0.01915518 0.01692915 0.96996872 -0.00353494
136 3.48693254 3.57443672 0.01898397 -0.02047258 0.01705537 0.97720082 -0.00332326
137 3.48693254 3.57443672 0.01898397 -0.02047258 0.01705537 0.97720082 -0.00332326
138 3.48693254 3.57443672 0.01898397 -0.02047258 0.01705537 0.97720082 -0.00332326
139 3.68065101 3.65035651 0.01900644 -0.01848242 0.01717205 0.98388598 -0.00313325
140 3.68065101 3.65035651 0.01900644 -0.01848242 0.01717205 0.98388598 -0.00313325
141 3.68065101 3.65035651 0.01900644 -0.01848242 0.01717205 0.98388598 -0.00313325
142 3.68065101 3.72627357 0.01902668 -0.01981127 0.01728298 0.99024168 -0.00295048
143 3.68065101 3.72627357 0.01902668 -0.01981127 0.01728298 0.99024168 -0.00295048
144 3.87436949 3.80219003 0.01904509 -0.01778612 0.01738818 0.99626911 -0.00277548
145 3.87436949 3.80219003 0.01904509 -0.01778612 0.01738818 0.99626911 -0.00277548
146 3.87436949 3.80219003 0.01904509 -0.01778612 0.01738818 0.99626911 -0.00277548
147 3.87436949 3.87810221 0.01906040 -0.01912168 0.01748907 1.00205012 -0.00260310
148 3.87436949 3.87810221 0.01906040 -0.01912168 0.01748907 1.00205012 -0.00260310
149 3.87436949 3.95401324 0.01907630 -0.02047256 0.01758280 1.00742035 -0.00244629
150 3.87436949 3.95401324 0.01907630 -0.02047256 0.01758280 1.00742035 -0.00244629
151 3.87436949 3.95401324 0.01907630 -0.02047256 0.01758280 1.00742035 -0.00244629
152 4.06808796 4.02991996 0.01909325 -0.01841476 0.01766936 1.01238003 -0.00230585
153 4.06808796 4.02991996 0.01909325 -0.01841476 0.01766936 1.01238003 -0.00230585
154 4.06808796 4.02991996 0.01909325 -0.01841476 0.01766936 1.01238003 -0.00230585
155 4.06808796 4.10582557 0.01910860 -0.01977433 0.01775162 1.01709307 -0.00217100
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909 26.03786858 -3.52021482 9.71508470 -0.02136706 0.01665528 0.95427739 0.00000000
910 26.03786858 -3.52021456 9.71506980 -0.02136655 0.01665528 0.95427739 0.00000000
911 26.03786858 -3.52021456 9.71506980 -0.02136655 0.01665528 0.95427739 0.00000000
912 26.03786858 -3.52021456 9.71506980 -0.02136655 0.01665528 0.95427739 0.00000000
913 26.03786858 -3.52021449 9.71506371 -0.02136638 0.01665528 0.95427739 0.00000000
914 26.03786858 -3.52021449 9.71506371 -0.02136638 0.01665528 0.95427739 0.00000000
915 26.03786858 -3.52021449 9.71506119 -0.02136634 0.01665528 0.95427739 0.00000000
916 26.03786858 -3.52021449 9.71506119 -0.02136634 0.01665528 0.95427739 0.00000000
917 26.03786858 -3.52021449 9.71506119 -0.02136634 0.01665528 0.95427739 0.00000000
918 26.03786858 -3.52021452 9.71506013 -0.02136635 0.01665528 0.95427739 0.00000000
919 26.03786858 -3.52021452 9.71506013 -0.02136635 0.01665528 0.95427739 0.00000000
920 26.03786858 -3.52021452 9.71506013 -0.02136635 0.01665528 0.95427739 0.00000000
921 26.03786858 -3.52021456 9.71505966 -0.02136639 0.01665528 0.95427739 0.00000000
922 26.03786858 -3.52021456 9.71505966 -0.02136639 0.01665528 0.95427739 0.00000000
923 26.03786858 -3.52021462 9.71505942 -0.02136640 0.01665516 0.95427056 0.00000000

232
path_generate_json.py Normal file
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@ -0,0 +1,232 @@
import json
import math
import matplotlib.pyplot as plt
# ============================================================
# 参数配置
# ============================================================
# 起点:采用你提供的第一个路径点
START_LAT = 30.80062100422322
START_LON = 121.21541599992909
TURN_RADIUS = 1.0
MISSION_SPEED = 0.64
ARC_SPACING = 0.3
# stanley para
K_YAW = 2.0
K_LAT = 4.0
# lqr para
Q_e = 100
Q_h = 5
R_u = 25
# 地球半径(m)
EARTH_RADIUS = 6378137.0
#
first_line_length = 6.0
first_arc_rad = math.pi # 180° 半圆
second_line_length = 8.0
second_arc_rad = math.pi / 2 # 90° 四分之一圆
third_line_length = 5.0
# ============================================================
# 工具函数
# ============================================================
def normalize_yaw(yaw):
"""将 yaw 归一化到 [-pi, pi]。"""
return math.atan2(math.sin(yaw), math.cos(yaw))
def local_to_latlon(x, y):
"""
局部 ENU 坐标转经纬度。
x: East,向东为正,单位 m
y: North,向北为正,单位 m
"""
lat0 = math.radians(START_LAT)
lat = START_LAT + math.degrees(y / EARTH_RADIUS)
lon = START_LON + math.degrees(
x / (EARTH_RADIUS * math.cos(lat0))
)
return lat, lon
def add_point(points, x, y, yaw):
"""添加一个 [latitude, longitude, yaw] 路径点。"""
lat, lon = local_to_latlon(x, y)
points.append([
round(lat, 12),
round(lon, 12),
round(normalize_yaw(yaw), 9)
])
def add_arc(points, cx, cy, radius,
start_angle, sweep_angle, spacing):
"""
添加圆弧采样点,不重复添加圆弧起点。
yaw 为圆弧的切线方向,单位 rad。
"""
arc_length = abs(radius * sweep_angle)
num_segments = max(1, math.ceil(arc_length / spacing))
for i in range(1, num_segments + 1):
ratio = i / num_segments
angle = start_angle + sweep_angle * ratio
x = cx + radius * math.cos(angle)
y = cy + radius * math.sin(angle)
# CCW 左转时,切线方向 = 圆心角 + pi/2
yaw = angle + (
math.pi / 2 if sweep_angle > 0
else -math.pi / 2
)
add_point(points, x, y, yaw)
coordinates = []
# 1. 第一段直线:长度 6 m
# 起点 (0, 0),终点 (6, 0)
add_point(coordinates, 0, 0, 0.0)
add_point(coordinates, first_line_length, 0, 0.0)
# 第一段圆弧起始索引
first_turn_index = len(coordinates) - 1
# 2. 第一段半圆:R=1,逆时针 180°
# 圆心 (6, 1),从 -90° 转到 90°
add_arc(
coordinates,
cx=first_line_length,
cy=TURN_RADIUS,
radius=TURN_RADIUS,
start_angle=-math.pi / 2,
sweep_angle= first_arc_rad,
spacing=ARC_SPACING
)
# 3. 第二段直线:长度 8 m
# 半圆终点 (6, 2),直线终点 (-2, 2)
second_line_end_x = first_line_length - second_line_length
second_line_end_y = 2 * TURN_RADIUS
add_point(
coordinates,
second_line_end_x,
second_line_end_y,
math.pi
)
# 第二段圆弧起始索引
second_turn_index = len(coordinates) - 1
# 4. 第二段四分之一圆:顺时针 90°
# 与第一段圆弧方向相反
second_arc_cx = second_line_end_x
second_arc_cy = second_line_end_y + TURN_RADIUS
add_arc(
coordinates,
cx=second_arc_cx,
cy=second_arc_cy,
radius=TURN_RADIUS,
start_angle=-math.pi / 2,
sweep_angle=-second_arc_rad,
spacing=ARC_SPACING
)
# 5. 最后一段直线:长度 5 m
third_line_start_x = second_arc_cx
third_line_start_y = second_arc_cy
add_point(
coordinates,
third_line_start_x - TURN_RADIUS,
third_line_start_y + third_line_length,
math.pi / 2
)
# ============================================================
# 构造符合现有项目的 GeoJSON
# ============================================================
path_json = {
"type": "FeatureCollection",
"properties": {
"turnRadius": TURN_RADIUS,
"misionSpeed": MISSION_SPEED,
"k_yaw": K_YAW,
"k_lat": K_LAT,
"lqr_Q_e": Q_e,
"lqr_Q_h": Q_h,
"lqr_R_u": R_u
},
"features": [
{
"type": "Feature",
"properties": {
"turnInds": [
first_turn_index,
second_turn_index
],
"mission": [],
},
"geometry": {
"type": "LineString",
"coordinates": coordinates
}
}
]
}
# ============================================================
# 保存文件
# ============================================================
output_file = "test_path.json"
with open(output_file, "w", encoding="utf-8") as f:
json.dump(
path_json,
f,
ensure_ascii=False,
indent=2
)
print(f"JSON 文件已生成: {output_file}")
print(f"路径点数量: {len(coordinates)}")
print(f"turnInds: {path_json['features'][0]['properties']['turnInds']}")
print(f"起点: {coordinates[0]}")
print(f"终点: {coordinates[-1]}")
fig, ax = plt.subplots(figsize=(10, 6))
ax.plot([p[0] for p in coordinates], [p[1] for p in coordinates], "-o", markersize=3, label="Path")
ax.set_xlabel("X (m)")
ax.set_ylabel("Y (m)")
ax.axis("equal")
ax.grid(True)
ax.legend()
plt.tight_layout()
plt.show()

6011
path_visualizer.log Normal file

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@ -14,6 +14,7 @@ find_package(auto_drive_proj_utils REQUIRED)
find_package(auto_drive_proj_msg REQUIRED)
find_package(Eigen3 REQUIRED)
find_package(fmt REQUIRED)
find_package(visualization_msgs REQUIRED)
include_directories(
include
@ -66,6 +67,7 @@ ament_target_dependencies(mission_center_node
builtin_interfaces
auto_drive_proj_utils
auto_drive_proj_msg
visualization_msgs
)
ament_target_dependencies(auto_mission_handler_node

View File

@ -2,7 +2,7 @@ auto_mission_handler_node:
# parameters
ros__parameters:
# redis
redis_host: "192.168.1.110" # "127.0.0.1"
redis_host: "127.0.0.1" # "127.0.0.1"
redis_port: 6379
redis_db: 0
@ -20,13 +20,13 @@ path_follow_handler_node:
# parameters
ros__parameters:
# redis
redis_host: "192.168.1.110" # "127.0.0.1"
redis_host: "127.0.0.1" # "127.0.0.1"
redis_port: 6379
redis_db: 0
device_id: "2072868304711761920"
controller_type: "pure_pursuit" # "pure_pursuit", "lqr", "pid"
controller_type: "stanley" # "pure_pursuit", "lqr", "pid","stanley"
# logger
logger_logger_name: "path_follow"
@ -40,7 +40,7 @@ mission_center_node:
# parameters
ros__parameters:
# redis
redis_host: "192.168.1.110" # "127.0.0.1"
redis_host: "127.0.0.1" # "127.0.0.1"
redis_port: 6379
redis_db: 0

View File

@ -22,17 +22,18 @@ public:
B.resize(2, 1); // 控制输入矩阵
Q.resize(2, 2); // 状态误差的加权矩阵
R.resize(1, 1); // 控制输入的加权矩阵
B << -1, 1; // 控制输入矩阵
//B << 0, 1; // 控制输入矩阵
// 设计LQR的代价函数矩阵
Q << 50, 0, 0, 15; // 状态误差的加权矩阵:惩罚横向误差e_x和角度误差θ
Q << 100, 0, 0, 10; // 状态误差的加权矩阵:惩罚横向误差e_x和角度误差θ
R << 10; // 控制输入的加权矩阵:惩罚角速度ω
R << 50; // 控制输入的加权矩阵:惩罚角速度ω
};
~LQR_Controller_C() {};
// LQR求解器函数
MatrixXd lqr(const MatrixXd &A, const MatrixXd &B, const MatrixXd &Q, const MatrixXd &R)
{
MatrixXd P = Q; // 初始化P为Q
MatrixXd P_prev = MatrixXd::Zero(A.rows(), A.cols());
for (int i = 0; i < 1000; ++i)
@ -46,12 +47,16 @@ public:
MatrixXd K = (R + B.transpose() * P * B).inverse() * B.transpose() * P * A;
return K;
};
float update(const float alpha, const float linear_val, const float dis)
float update(const float alpha, const float linear_val, const float dis, const float kappa)
{
// std::cout << "LQR update!" << std::endl;
logger_->trace("LQR update!");
double dt = 0.01;
MatrixXd A(2, 2);
A << 0, linear_val, 0, 0;
A << 1.0, linear_val * dt,
0, 1.0;
B << 0.5 * linear_val * dt * dt,
dt;
Eigen::VectorXd x(2);
x << dis, alpha;
@ -61,8 +66,24 @@ public:
// << K << std::endl;
std::stringstream ss;
ss << K;
logger_->trace("LQR Gain Matrix K:\n{}", ss.str());
return -(K * x)(0, 0);
logger_->info("LQR Gain Matrix K:\n{}", ss.str());
double omega_feedback = -(K * x)(0, 0);
// Curvature feedforward
double omega_feedforward = linear_val * kappa;// feedforwward unused temporarily
double omega = omega_feedback;
logger_->info(
"LQR: dis={}, alpha={}, kappa={}, "
"omega_fb={}, omega_ff={}, omega={}",
dis,
alpha,
kappa,
omega_feedback,
omega_feedforward,
omega);
return omega;
};
/*void init_lqr(const MatrixXd B, const MatrixXd Q, const MatrixXd R)
{
@ -70,6 +91,26 @@ public:
this->Q = Q;
this->R = R;
}*/
float update(const float alpha, const float linear_val, const float dis)
{
// std::cout << "LQR update!" << std::endl;
logger_->trace("LQR update!");
double dt = 0.01;
MatrixXd A(2, 2);
A << 1.0, linear_val*dt,0, 1.0;
B << 0.5 * linear_val * dt * dt,dt;
Eigen::VectorXd x(2);
x << dis, alpha;
// 求解LQR增益矩阵K
MatrixXd K = lqr(A, B, Q, R);
// std::cout << "LQR Gain Matrix K:" << std::endl
// << K << std::endl;
std::stringstream ss;
ss << K;
logger_->info("LQR Gain Matrix K:\n{}", ss.str());
return -(K * x)(0, 0);
};
void init(const float max_linear_vel, const float max_angular_vel, const float kp, const float ki,
const float kd, const float look_ahead_dis, const float kv)
{
@ -82,7 +123,7 @@ public:
B.resize(2, 1); // 控制输入矩阵
Q.resize(2, 2); // 状态误差的加权矩阵
R.resize(1, 1); // 控制输入的加权矩阵
B << -1, 1; // 控制输入矩阵
B << 0, 1; // 控制输入矩阵
// 设计LQR的代价函数矩阵
Q << 50, 0, 0, 15; // 状态误差的加权矩阵:惩罚横向误差e_x和角度误差θ

View File

@ -55,7 +55,6 @@ private:
rclcpp::Publisher<std_msgs::msg::String>::SharedPtr event_warn_pub_;
rclcpp::Publisher<std_msgs::msg::String>::SharedPtr event_error_pub_;
rclcpp::Publisher<std_msgs::msg::String>::SharedPtr properties_report_pub_;
std::vector<std::pair<int, int>> interpolation_map_;
std::shared_future<typename auto_drive_proj_msg::srv::ReqString::Response::SharedPtr> mission_future_;
@ -86,7 +85,11 @@ private:
std::string& mission_data,
std::string& follow_data);
std::vector<std::vector<double>> interpolatePoints(const std::vector<std::vector<double>>& coordinates,
double max_distance = 0.5);
double max_distance);
// simulation only
std::vector<std::vector<double>> alignPathYaw( const std::vector<std::vector<double>>& coordinates,
double vehicle_initial_yaw);
int updatePathIndices(const int original_ind, const std::vector<std::pair<int, int>>& interpolation_map);
void handleAsyncResponses();
@ -145,6 +148,9 @@ private:
int logger_max_size_;
int logger_max_files_;
int logger_set_level_;
//
double vehicle_initial_yaw_ = 0.0;
};
} // namespace auto_drive_misson_center

View File

@ -45,7 +45,7 @@ public:
private:
MainState main_state_{MainState::IDLE};
bool is_resuming_{false};
std::string controller_type_{"pure_pursuit"};
std::string controller_type_{"lqr"};
rclcpp::Subscription<geometry_msgs::msg::PoseWithCovarianceStamped>::SharedPtr pose_sub_;
rclcpp::Publisher<geometry_msgs::msg::Twist>::SharedPtr cmd_vel_pub_;

View File

@ -15,7 +15,7 @@ struct PathPoint {
double x; // 经度或UTM X坐标
double y; // 纬度或UTM Y坐标
double yaw; // 朝向角度(弧度)
double kappa; // 曲率
PathPoint(double x_val = 0.0, double y_val = 0.0, double yaw_val = 0.0)
: x(x_val), y(y_val), yaw(yaw_val) {}
};
@ -60,7 +60,36 @@ public:
}
return false;
}
void computePathCurvature()
{
if (path_points_.size() < 3) {
return;
}
for (size_t i = 1; i < path_points_.size() - 1; ++i)
{
const auto& p_prev = path_points_[i - 1];
const auto& p_curr = path_points_[i];
const auto& p_next = path_points_[i + 1];
double ds1 = std::hypot(p_curr.x - p_prev.x,p_curr.y - p_prev.y);
double ds2 = std::hypot(p_next.x - p_curr.x,p_next.y - p_curr.y);
double ds = 0.5 * (ds1 + ds2);
if (ds < 1e-6) {
path_points_[i].kappa= 0.0;
continue;
}
double dyaw = p_next.yaw - p_prev.yaw;
while (dyaw > M_PI)
dyaw -= 2.0 * M_PI;
while (dyaw < -M_PI)
dyaw += 2.0 * M_PI;
path_points_[i].kappa = dyaw / (ds1 + ds2);
logger_->info("path ind:{}, kappa:{}", i, path_points_[i].kappa);
}
// 首尾点处理
path_points_.front().kappa= path_points_[1].kappa;
path_points_.back().kappa= path_points_[path_points_.size() - 2].kappa;
}
// 运行一次控制计算
ControlOutput run(const PathPoint& current_pose) {
if (!is_initialized_) {
@ -159,6 +188,7 @@ protected:
logger_->debug("current_point_index_:{}, path_points_ size:{}, distance2:{}, distance1:{}",
current_point_index_, path_points_.size(), distance2, distance1);
// 如果current_point_index_+1的距离小于current_point_index_距离,current_point_index_++
if (distance2 < distance1) {
@ -185,7 +215,9 @@ protected:
// 保护成员变量,子类可以访问
double mission_speed_{0.0};
double turn_radius_{1.0};
double turn_radius_{0.3};
double max_allowed_speed_{1.0};
double wheel_dis_{0.5};
std::vector<PathPoint> path_points_;
int current_point_index_{0};
bool is_initialized_{false};

View File

@ -8,6 +8,7 @@
#include "lqr_control.hpp"
#include "pid_control.hpp"
#include "pure_pursuit_control.hpp"
#include "stanley_control.hpp"
#include "spdlog/logger.h"
#include "spdlog/spdlog.h"
#include "spdlog/sinks/rotating_file_sink.h"
@ -27,29 +28,35 @@ protected:
// 从PathTrackingController的参数映射到原有控制器的init参数
// 注意:原有控制器需要max_linear_vel, max_angular_vel, kp, ki, kd, look_ahead_dis, kv
// 这里需要根据需求设置合适的参数值
float max_linear_vel = mission_speed_;
float max_linear_vel = mission_speed_ ;
float max_angular_vel = mission_speed_ / turn_radius_; // 根据转弯半径估算最大角速度
float max_angular_vel_theory = max_allowed_speed_/wheel_dis_;
max_angular_vel = std::max(max_angular_vel, max_angular_vel_theory);
float kp = 1.76; // 需要根据实际情况调整
float ki = 0.0;
float kd = 0.8;
float look_ahead_dis = 0.4; // 需要根据实际情况调整
float kv = 1.0; // 需要根据实际情况调整
float look_ahead_dis = 0.6; // 需要根据实际情况调整
float kv = 0.0; // 需要根据实际情况调整
controller_->init(max_linear_vel, max_angular_vel, kp, ki, kd, look_ahead_dis, kv);
return true;
}
bool kappa_cal = false;
ControlOutput onRun(const PathPoint& current_pose) override {
// 从PathPoint计算所需参数
float alpha = 0.0; // 角度误差
float linear_val = 0.0; // 线速度
float dis = 0.0; // 横向误差
computeControlParams(current_pose, alpha, linear_val, dis);
logger_->debug("PPAdapter alpha={}, linear_val={}, dis={}", alpha, linear_val, dis);
float kappa = 0.0; // 路徑曲率
if(!kappa_cal){
computePathCurvature();
kappa_cal = true;
}
computeControlParams(current_pose, alpha, linear_val, dis, kappa);
logger_->debug("PPAdapter alpha={}, linear_val={}, dis={}, kappa:{}", alpha, linear_val, dis,kappa);
// 调用原有控制器的update方法
float angular_velocity = controller_->update(alpha, linear_val, dis);
float angular_velocity = controller_->update(alpha, linear_val, dis, kappa);
logger_->debug("PP output angular={}", angular_velocity);
// 判断是否完成
@ -65,8 +72,8 @@ protected:
private:
std::shared_ptr<LQR_Controller_C> controller_;
std::shared_ptr<spdlog::logger> logger_;
void computeControlParams(const PathPoint& current_pose, float& alpha, float& linear_val, float& dis) {
void computeControlParams(const PathPoint& current_pose, float& alpha, float& linear_val, float& dis, float& kappa) {
// 获取当前路径点的索引
int idx = current_point_index_;
if (idx >= static_cast<int>(path_points_.size()) - 1) {
@ -82,13 +89,15 @@ private:
dis = static_cast<float>(-dx * sin(path_yaw) + dy * cos(path_yaw));
// 角度误差
alpha = static_cast<float>(path_yaw - current_pose.yaw);
alpha = static_cast<float>(current_pose.yaw - path_yaw);
// 归一化到 [-pi, pi]
while (alpha > M_PI) alpha -= 2 * M_PI;
while (alpha < -M_PI) alpha += 2 * M_PI;
// 线速度
linear_val = static_cast<float>(mission_speed_);
kappa = path_points_[idx].kappa;
}
};
@ -165,29 +174,38 @@ public:
protected:
bool onInitialize() override {
float max_linear_vel = mission_speed_;
float max_linear_vel = mission_speed_ ;
float max_angular_vel = mission_speed_ / turn_radius_;
float max_angular_vel_theory = max_allowed_speed_/wheel_dis_;
max_angular_vel = std::max(max_angular_vel, max_angular_vel_theory);
float kp = 1.76;
float ki = 0.0;
float kd = 0.8;
float look_ahead_dis = 0.4; // 前视距离,需要根据实际情况调整
float kv = 0.6; // 速度系数,需要根据实际情况调整
adapter_look_ahead_dis_ = look_ahead_dis;
float kv = 1.0; // 速度系数,需要根据实际情况调整
controller_->init(max_linear_vel, max_angular_vel, kp, ki, kd, look_ahead_dis, kv);
//logger_->debug("PP para mission_speed_={}, turn_radius_={} frontdis={}", mission_speed_, turn_radius_,adapter_look_ahead_dis_ );
controller_->init(max_linear_vel, max_angular_vel, kp, ki, kd, adapter_look_ahead_dis_, kv);
return true;
}
bool kappa_cal = false;
ControlOutput onRun(const PathPoint& current_pose) override {
float alpha = 0.0;
float linear_val = 0.0;
float dis = 0.0;
float lon_dis = 0.0;
float kappa = 0.0;
if(!kappa_cal){
computePathCurvature();
kappa_cal = true;
}
// Pure Pursuit使用前视点,计算需要特殊处理
computeControlParamsWithLookAhead(current_pose, alpha, linear_val, dis);
logger_->debug("PPAdapter alpha={}, linear_val={}, dis={}", alpha, linear_val, dis);
computeControlParamsWithLookAhead(current_pose, alpha, linear_val, dis, lon_dis, kappa);
logger_->debug("PPAdapter alpha={}, linear_val={}, dis={}, lon_dis={},kappa={}", alpha, linear_val, dis,lon_dis,kappa);
float angular_velocity = controller_->update(alpha, linear_val, dis);
float angular_velocity = controller_->update(alpha, linear_val, dis, lon_dis, kappa);
//float angular_velocity = controller_->update(alpha, linear_val, dis);
logger_->debug("PP output angular={}", angular_velocity);
bool completed = isPathCompleted(current_pose);
@ -204,7 +222,7 @@ private:
std::shared_ptr<spdlog::logger> logger_;
void computeControlParamsWithLookAhead(const PathPoint& current_pose,
float& alpha, float& linear_val, float& dis) {
float& alpha, float& linear_val, float& dis, float& lon_dis, float& kappa) {
// 查找前视点
PathPoint lookahead_point;
if (!findLookAheadPoint(current_pose, lookahead_point)) {
@ -237,7 +255,19 @@ private:
double dx_path = current_pose.x - path_points_[idx].x;
double dy_path = current_pose.y - path_points_[idx].y;
double path_yaw = path_points_[idx].yaw;
dis = static_cast<float>(-dx_path * sin(path_yaw) + dy_path * cos(path_yaw));
//dis = static_cast<float>(-dx_path * sin(path_yaw) + dy_path * cos(path_yaw));
dis = static_cast<float>(-dx * sin(path_yaw) + dy * cos(path_yaw));
lon_dis = static_cast<float>(sqrt(dx * dx + dy * dy));
kappa = static_cast<float>(path_points_[idx].kappa);
logger_->debug("current_pose_x:{}, current_pose_y:{},current_pose_yaw:{}",
current_pose.x, current_pose.y,current_pose.yaw
);
logger_->debug("current_path_x:{}, current_path_y:{},current_path_yaw:{}",
path_points_[idx].x,
path_points_[idx].y,
path_points_[idx].yaw
);
}
bool findLookAheadPoint(const PathPoint& current_pose, PathPoint& lookahead_point) {
@ -267,6 +297,7 @@ private:
lookahead_point.x = path_points_[i].x + ratio * dx;
lookahead_point.y = path_points_[i].y + ratio * dy;
lookahead_point.yaw = path_points_[i].yaw;
lookahead_point.kappa = path_points_[i].kappa;
return true;
}
accumulated_dist += segment_length;
@ -278,6 +309,148 @@ private:
}
};
} // namespace auto_drive_misson_center
// Stanley控制器适配器
class StanleyControllerAdapter : public PathTrackingController {
public:
StanleyControllerAdapter(std::shared_ptr<spdlog::logger> logger = nullptr)
: logger_(logger), PathTrackingController(logger), controller_(std::make_shared<Stanley_Controller_C>(logger)) {}
protected:
bool onInitialize() override {
float max_linear_vel = mission_speed_ ;
float max_angular_vel = mission_speed_ / turn_radius_;
float max_angular_vel_theory = max_allowed_speed_/wheel_dis_;
max_angular_vel = std::max(max_angular_vel, max_angular_vel_theory);
float kp = 1.76;
float ki = 0.0;
float kd = 0.8;
float look_ahead_dis = 0.4; // 前视距离,需要根据实际情况调整
float kv = 0.6; // 速度系数,需要根据实际情况调整
adapter_look_ahead_dis_ = look_ahead_dis;
//logger_->debug("PP para mission_speed_={}, turn_radius_={} frontdis={}", mission_speed_, turn_radius_,adapter_look_ahead_dis_ );
controller_->init(max_linear_vel, max_angular_vel, kp, ki, kd, adapter_look_ahead_dis_, kv);
return true;
}
bool kappa_cal = false;
ControlOutput onRun(const PathPoint& current_pose) override {
float alpha = 0.0;
float linear_val = 0.0;
float dis = 0.0;
float lon_dis = 0.0;
float kappa = 0.0;
if(!kappa_cal){
computePathCurvature();
kappa_cal = true;
}
// Pure Pursuit使用前视点,计算需要特殊处理
computeControlParamsWithLookAhead(current_pose, alpha, linear_val, dis, lon_dis, kappa);
logger_->debug("Stanley Adapter alpha={}, linear_val={}, dis={}, lon_dis={},kappa={}", alpha, linear_val, dis,lon_dis,kappa);
float angular_velocity = controller_->update(alpha, linear_val, dis, lon_dis, kappa);
//float angular_velocity = controller_->update(alpha, linear_val, dis);
logger_->debug("Stanley output angular={}", angular_velocity);
bool completed = isPathCompleted(current_pose);
return ControlOutput(mission_speed_, angular_velocity, completed);
}
void onReset() override {
controller_->reset();
}
private:
std::shared_ptr<Stanley_Controller_C> controller_;
double adapter_look_ahead_dis_; //保存前视距离
std::shared_ptr<spdlog::logger> logger_;
void computeControlParamsWithLookAhead(const PathPoint& current_pose,
float& alpha, float& linear_val, float& dis, float& lon_dis, float& kappa) {
// 查找前视点
PathPoint lookahead_point;
if (!findLookAheadPoint(current_pose, lookahead_point)) {
// 如果找不到前视点,使用当前路径点
int idx = current_point_index_;
if (idx >= static_cast<int>(path_points_.size())) {
idx = path_points_.size() - 1;
}
lookahead_point = path_points_[idx];
}
// 计算从当前点到前视点的角度误差
double dx = lookahead_point.x - current_pose.x;
double dy = lookahead_point.y - current_pose.y;
double target_yaw = atan2(dy, dx);
// 预瞄角度误差
alpha = static_cast<float>(target_yaw - current_pose.yaw);
while (alpha > M_PI) alpha -= 2 * M_PI;
while (alpha < -M_PI) alpha += 2 * M_PI;
// 线速度
linear_val = static_cast<float>(mission_speed_);
// 横向误差(用于更新索引)
int idx = current_point_index_;
if (idx >= static_cast<int>(path_points_.size()) - 1) {
idx = path_points_.size() - 1;
}
double dx_path = current_pose.x - path_points_[idx].x;
double dy_path = current_pose.y - path_points_[idx].y;
double path_yaw = path_points_[idx].yaw;
//dis = static_cast<float>(-dx_path * sin(path_yaw) + dy_path * cos(path_yaw));
dis = static_cast<float>(-dx * sin(path_yaw) + dy * cos(path_yaw));
lon_dis = static_cast<float>(sqrt(dx * dx + dy * dy));
kappa = static_cast<float>(path_points_[idx].kappa);
logger_->debug("current_pose_x:{}, current_pose_y:{},current_pose_yaw:{}",
current_pose.x, current_pose.y,current_pose.yaw
);
logger_->debug("current_path_x:{}, current_path_y:{},current_path_yaw:{}",
path_points_[idx].x,
path_points_[idx].y,
path_points_[idx].yaw
);
}
bool findLookAheadPoint(const PathPoint& current_pose, PathPoint& lookahead_point) {
if (path_points_.empty()) {
return false;
}
// 获取前视距离(从控制器参数获取)
// 这里需要从controller_获取lookAheadDis,但由于接口限制,我们使用默认值
double look_ahead_distance = adapter_look_ahead_dis_; // 需要与初始化时一致
// 从当前点开始沿路径向前查找
int start_idx = current_point_index_;
if (start_idx >= static_cast<int>(path_points_.size())) {
start_idx = path_points_.size() - 1;
}
double accumulated_dist = 0.0;
for (int i = start_idx; i < static_cast<int>(path_points_.size()) - 1; ++i) {
double dx = path_points_[i + 1].x - path_points_[i].x;
double dy = path_points_[i + 1].y - path_points_[i].y;
double segment_length = sqrt(dx * dx + dy * dy);
if (accumulated_dist + segment_length >= look_ahead_distance) {
// 在前视距离内插值
double ratio = (look_ahead_distance - accumulated_dist) / segment_length;
lookahead_point.x = path_points_[i].x + ratio * dx;
lookahead_point.y = path_points_[i].y + ratio * dy;
lookahead_point.yaw = path_points_[i].yaw;
lookahead_point.kappa = path_points_[i].kappa;
return true;
}
accumulated_dist += segment_length;
}
// 如果路径长度不够,使用最后一个点
lookahead_point = path_points_.back();
return true;
}
}; // namespace auto_drive_misson_center
}
#endif // AUTO_DRIVE_MISSION_CENTER_PATH_TRACKING_CONTROLLER_ADAPTER_HPP_

View File

@ -27,17 +27,30 @@ public:
lookAheadDis = look_ahead_dis;
k_v = kv;
}
float update(const float alpha, const float linear_val, const float dis)
{
// std::cout<<"PurePursuit update!"<<std::endl;
logger_->trace("PurePursuit update!");
float w_val = (2 * fabs(linear_val) * sin(alpha) / (k_v * fabs(linear_val) + lookAheadDis));
float w_val = (2 * fabs(linear_val) * sin(alpha) / (lookAheadDis));
w_val = fabs(w_val) > maxAngularVel ? (maxAngularVel * (w_val > 0.0 ? 1.0 : -1.0)) : w_val;
// std::cout << "linear_val: " << linear_val << ", w_val: " << w_val << std::endl;
logger_->trace("linear_val:{}, w_val:{}", linear_val, w_val);
return w_val;
};
float update(const float alpha, const float linear_val, const float dis, const float lon_dis,const float kappa)
{
// std::cout<<"PurePursuit update!"<<std::endl;
logger_->trace("PurePursuit update!");
// logger_->info("PurePursuit process! linear_val:{}, dis:{}, lon_dis:{}, kappa:{}", linear_val, dis, lon_dis, kappa);
float w_val = (2 * fabs(linear_val) * sin(alpha) / (lon_dis)) + dis;
//float w_val = linear_val*kappa;
w_val = fabs(w_val) > maxAngularVel ? (maxAngularVel * (w_val > 0.0 ? 1.0 : -1.0)) : w_val;
// std::cout << "linear_val: " << linear_val << ", w_val: " << w_val << std::endl;
logger_->trace("linear_val:{}, w_val:{}", linear_val, w_val);
return w_val;
};
void reset()
{

View File

@ -0,0 +1,71 @@
#ifndef _STANLEY_CONTROLL_H_
#define _STANLEY_CONTROLL_H_
#include <iostream>
#include <math.h>
#include <Eigen/Dense>
#include "base_following_controller.hpp"
#include "pid_control.hpp"
using namespace Eigen;
class Stanley_Controller_C : public BaseFollowingController
{
public:
Stanley_Controller_C() = delete;
Stanley_Controller_C(std::shared_ptr<spdlog::logger> logger_recorder)
:BaseFollowingController(logger_recorder), logger_(logger_recorder){};
~Stanley_Controller_C(){};
void init(const float max_linear_vel, const float max_angular_vel, const float kp, const float ki, \
const float kd, const float look_ahead_dis, const float kv)
{
// std::cout<<"PurePursuit init!"<<std::endl;
logger_->info("PurePursuit init! max_linear_vel:{}, max_angular_vel:{}", max_linear_vel, max_angular_vel);
maxLinearVel = max_linear_vel;
maxAngularVel = max_angular_vel;
lookAheadDis = look_ahead_dis;
k_v = kv;
}
float update(const float alpha, const float linear_val, const float dis)
{
// std::cout<<"PurePursuit update!"<<std::endl;
logger_->trace("PurePursuit update!");
float w_val = 1.5 * alpha +1.0 * std::atan2(dis,linear_val);
w_val = fabs(w_val) > maxAngularVel ? (maxAngularVel * (w_val > 0.0 ? 1.0 : -1.0)) : w_val;
// std::cout << "linear_val: " << linear_val << ", w_val: " << w_val << std::endl;
logger_->trace("linear_val:{}, w_val:{}", linear_val, w_val);
return w_val;
};
float update(const float alpha, const float linear_val, const float dis, const float lon_dis,const float kappa)
{
// std::cout<<"PurePursuit update!"<<std::endl;
logger_->trace("PurePursuit update!");
// logger_->info("PurePursuit process! linear_val:{}, dis:{}, lon_dis:{}, kappa:{}", linear_val, dis, lon_dis, kappa);
float w_val = 1.5 * alpha +1.0 * std::atan2(dis,linear_val);
//float w_val = linear_val*kappa;
w_val = fabs(w_val) > maxAngularVel ? (maxAngularVel * (w_val > 0.0 ? 1.0 : -1.0)) : w_val;
// std::cout << "linear_val: " << linear_val << ", w_val: " << w_val << std::endl;
logger_->trace("linear_val:{}, w_val:{}", linear_val, w_val);
return w_val;
};
void reset()
{
//pid_.reset();
}
void test(){std::cout<<"pp test!"<<std::endl; logger_->debug("pp test!");};
private:
float maxLinearVel;
float maxAngularVel;
float lookAheadDis, k_v;
float maxSlowAngularVel, minToleranceAngle;
//PID_Controller_C pid_;
std::shared_ptr<spdlog::logger> logger_;
};
#endif

View File

@ -12,6 +12,7 @@
<depend>rclcpp</depend>
<depend>std_msgs</depend>
<depend>std_srvs</depend>
<depend>visualization_msgs</depend>
<depend>auto_drive_proj_utils</depend>
<depend>auto_drive_proj_msg</depend>
<depend>libcurl-dev</depend>

View File

@ -20,20 +20,20 @@ class MockPoseNode(Node):
)
self.get_logger().info("Service /start_pose ready, always return success=True")
# 2. rtk_pose publisher
self.pub_rtk = self.create_publisher(
PoseWithCovarianceStamped,
"/rtk_pose",
10
)
# # 2. rtk_pose publisher
# self.pub_rtk = self.create_publisher(
# PoseWithCovarianceStamped,
# "/rtk_pose",
# 10
# )
self.url = url
self.coord_list = []
self.load_geojson()
# self.url = url
# self.coord_list = []
# self.load_geojson()
self.index = 0
self.timer_period = 0.2
self.timer = self.create_timer(self.timer_period, self.publish_rtk_pose_cb)
# self.index = 0
# self.timer_period = 0.2
# self.timer = self.create_timer(self.timer_period, self.publish_rtk_pose_cb)
def load_geojson(self):
try:
@ -53,40 +53,40 @@ class MockPoseNode(Node):
response.success = True
return response
def publish_rtk_pose_cb(self):
if not self.coord_list:
return
# def publish_rtk_pose_cb(self):
# if not self.coord_list:
# return
lon, lat, alt = self.coord_list[self.index]
# lon, lat, alt = self.coord_list[self.index]
msg = PoseWithCovarianceStamped()
msg.header.stamp = self.get_clock().now().to_msg()
msg.header.frame_id = "enu"
# msg = PoseWithCovarianceStamped()
# msg.header.stamp = self.get_clock().now().to_msg()
# msg.header.frame_id = "enu"
msg.pose.pose.position.x = lon
msg.pose.pose.position.y = lat
msg.pose.pose.position.z = alt
# msg.pose.pose.position.x = lon
# msg.pose.pose.position.y = lat
# msg.pose.pose.position.z = alt
msg.pose.pose.orientation.w = 1.0
msg.pose.pose.orientation.x = 0.0
msg.pose.pose.orientation.y = 0.0
msg.pose.pose.orientation.z = 0.0
# msg.pose.pose.orientation.w = 1.0
# msg.pose.pose.orientation.x = 0.0
# msg.pose.pose.orientation.y = 0.0
# msg.pose.pose.orientation.z = 0.0
for i in range(36):
msg.pose.covariance[i] = 0.0
# for i in range(36):
# msg.pose.covariance[i] = 0.0
self.pub_rtk.publish(msg)
# self.pub_rtk.publish(msg)
self.index += 1
if self.index >= len(self.coord_list):
self.index = 0
# self.index += 1
# if self.index >= len(self.coord_list):
# self.index = 0
def main(args=None):
# 解析命令行参数,注意:分离ros args和自定义argparse参数
parser = argparse.ArgumentParser()
parser.add_argument("--url", type=str,
default="https://test.sweetai.cn:4433/load/locationCmdPath/1701148411059699712.json",
default="https://test.sweetai.cn:4433/load/locationCmdPath/1700080420079468544.json",
help="geojson path url")
parsed_args, ros_args = parser.parse_known_args(args)
@ -119,4 +119,4 @@ if __name__ == "__main__":
3. `/start_pose`服务不解析请求参数,任何入参直接返回`success=True`,完全匹配你的需求。
4. frame_id 写为`map`,你可以根据实际工程改成`enu`。
5. 发布周期`timer_period = 0.2`,可按需调快 / 调慢。
'''
'''

View File

@ -427,7 +427,7 @@ void MissionCenterNode::missionOnCallback(
response->success = true;
response->message = "Mission request accepted, processing in background";
LOG_INFO("mission on cb, set main state to WAIT MIS RSP, Mission request accepted, processing in background");
LOG_INFO("========================== /invoke/missionOn request= end");
} catch (const std::exception& e) {
response->success = false;
response->message = std::string("Exception: ") + e.what();
@ -744,6 +744,67 @@ std::string MissionCenterNode::downloadFile(const std::string& url)
return readBuffer;
}
std::vector<std::vector<double>> MissionCenterNode::alignPathYaw(
const std::vector<std::vector<double>>& coordinates,
double vehicle_initial_yaw)
{
std::vector<std::vector<double>> aligned_coordinates;
if (coordinates.empty())
return aligned_coordinates;
// Path 起点
const double origin_x = coordinates.front()[0];
const double origin_y = coordinates.front()[1];
// Path 原始起始航向
const double path_initial_yaw = coordinates.front()[2];
// 将 Path 初始航向旋转到车辆初始航向
const double yaw_offset =
vehicle_initial_yaw - path_initial_yaw;
const double cos_offset = std::cos(yaw_offset);
const double sin_offset = std::sin(yaw_offset);
aligned_coordinates.reserve(coordinates.size());
for (const auto& coord : coordinates)
{
if (coord.size() < 3)
continue;
const double dx = coord[0] - origin_x;
const double dy = coord[1] - origin_y;
const double x =
cos_offset * dx - sin_offset * dy;
const double y =
sin_offset * dx + cos_offset * dy;
double yaw = coord[2] + yaw_offset;
while (yaw > M_PI)
yaw -= 2.0 * M_PI;
while (yaw < -M_PI)
yaw += 2.0 * M_PI;
aligned_coordinates.push_back({
x,
y,
yaw
});
LOG_INFO(
"Path new: x={} y={} yaw={} deg",
x,
y,
yaw * 180.0 / M_PI);
}
return aligned_coordinates;
}
bool MissionCenterNode::processDownloadedData(const std::string& downloaded_data,
std::string& mission_data, std::string& follow_data)
{
@ -786,8 +847,10 @@ bool MissionCenterNode::processDownloadedData(const std::string& downloaded_data
}
interpolation_map_.clear();
std::vector<std::vector<double>> interpolated_coords = interpolatePoints(coordinates, 0.5);
std::vector<std::vector<double>> aligned_coords = alignPathYaw(coordinates, vehicle_initial_yaw_);
std::vector<std::vector<double>> interpolated_coords = interpolatePoints(aligned_coords, 0.2);
json processed_data = data;
json new_coordinates = json::array();
for (const auto& coord : interpolated_coords) {
@ -857,9 +920,28 @@ std::vector<std::vector<double>> MissionCenterNode::interpolatePoints(
double ratio = static_cast<double>(j) / num_segments;
double x = current[0] + dx * ratio;
double y = current[1] + dy * ratio;
result.push_back({x, y, current[2]});
inserted_points++;
}
// yaw 插值
double yaw_diff = next[2] - current[2];
// normalize yaw_diff 到 [-pi, pi]
while (yaw_diff > M_PI) {
yaw_diff -= 2.0 * M_PI;
}
while (yaw_diff < -M_PI) {
yaw_diff += 2.0 * M_PI;
}
double yaw =current[2] + yaw_diff * ratio;
// normalize yaw 到 [-pi, pi]
while (yaw > M_PI) {
yaw -= 2.0 * M_PI;
}
while (yaw < -M_PI) {
yaw += 2.0 * M_PI;
}
result.push_back({x, y, yaw});
inserted_points++;
}
interpolation_map_.push_back({i + 1, inserted_points});
}

View File

@ -9,7 +9,6 @@
#include <chrono>
#include <cmath>
#include <limits>
namespace auto_drive_misson_center {
constexpr double PathFollowHandler::POS_LOST_TIMEOUT_SECONDS;
@ -79,7 +78,7 @@ PathFollowHandler::PathFollowHandler(const std::string & node_name)
10
);
this->declare_parameter<std::string>("controller_type", "pure_pursuit");
this->declare_parameter<std::string>("controller_type", "lqr");
this->get_parameter("controller_type", controller_type_);
LOG_INFO("Controller type parameter: {}", controller_type_);
@ -417,7 +416,7 @@ void PathFollowHandler::processRunningState()
auto path_ind_msg = std_msgs::msg::Int32();
path_ind_msg.data = path_controller_->getCurrentPointIndex() + path_ind_start_;
path_ind_pub_->publish(path_ind_msg);
LOG_INFO("Published final path index 1: {}", path_ind_msg.data);
saveCurrentStateToRedis();
LOG_DEBUG("path controller, current pose point:x:{}, y:{}, yaw:{}; twist_msg pub:linear x:{}, angular z:{}, run is completed:{}, Current Point Index:{}, Path Point Count:{}",
@ -444,7 +443,7 @@ void PathFollowHandler::processRunningState()
}
final_ind_msg.data = total_points - 1;
path_ind_pub_->publish(final_ind_msg);
LOG_INFO("Published final path index 2: {}", final_ind_msg.data);
LOG_INFO("main state change(Running State:Mission completed):{} -> {}", mainStateToString(main_state_), "AUTO_OFF");
main_state_ = MainState::AUTO_OFF;
} else {
@ -693,7 +692,13 @@ bool PathFollowHandler::loadPathTrackingController(const std::string& controller
LOG_INFO("Pure Pursuit path tracking controller loaded");
reportEvent("info", "PATH_CONTROLLER_LOAD_OK", "Pure Pursuit控制器加载成功");
return true;
} else {
} else if (controller_type == "stanley") {
path_controller_ = std::make_shared<StanleyControllerAdapter>(logger);
LOG_INFO("STAN path tracking controller loaded");
reportEvent("info", "PATH_CONTROLLER_LOAD_OK", "STAN控制器加载成功");
return true;
}
else {
LOG_ERROR("load Path Tracking Controller ERROR");
}

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