Refactor main execution flow by enabling task 2.5 with arrow direction detection and adjusting task 3 back execution. Update task 2.5 parameters for distance and speed, and modify task 5's horizontal line movement distance. Set observe flag to False in track detection functions for consistency.

main.py

@@ -17,9 +17,8 @@ from utils.robot_control_cmd_lcmt import robot_control_cmd_lcmt
 from utils.robot_control_response_lcmt import robot_control_response_lcmt
 from utils.localization_lcmt import localization_lcmt
 from utils.image_raw import ImageProcessor
-# from utils.marker_client import MarkerRunner
-# from utils.marker_client import MarkerRunner
 from utils.base_msg import BaseMsg
+# from utils.marker_client import MarkerRunner
 
 from task_1.task_1 import run_task_1
 from task_2_5.task_2_5 import run_task_2_5
@@ -30,11 +29,10 @@ from task_2_5.task_2_5 import run_task_2_5, run_task_2_5_back
 from task_4.task_4 import run_task_4, run_task_4_back
 from task_test.task_left_line import run_task_test
 from task_5.task_5 import run_task_5
-# from task_test.go_to_xy_example import run_go_to_xy_example
-
 
 from base_move.turn_degree import turn_degree, turn_degree_v2
-from base_move.go_to_xy import go_to_y_v2
+
+from utils.log_helper import info
 
 pass_marker = True
 
@@ -50,14 +48,13 @@ def main():
         Ctrl.base_msg.stop()  # BUG 垃圾指令 for eat
         
         # time.sleep(100) # TEST,
-        # run_task_1(Ctrl, msg)
+        run_task_1(Ctrl, msg)
 
-        # arrow_direction = run_task_2(Ctrl, msg, xy_flag=False)
+        arrow_direction = run_task_2(Ctrl, msg, xy_flag=False)
+        # arrow_direction = 'right' # TEST
 
-        # print(f"arrow_direction: {arrow_direction}")
+        info(f"识别到箭头方向: {arrow_direction}", "info")
-        # run_task_2_5(Ctrl, msg, direction=arrow_direction)
+        run_task_2_5(Ctrl, msg, direction=arrow_direction)
-
-        arrow_direction = 'right' # TEST
 
         if arrow_direction == 'left':
             run_task_4(Ctrl, msg)
@@ -68,8 +65,7 @@ def main():
         run_task_5(Ctrl, msg, direction=arrow_direction)
 
         if arrow_direction == 'left':
-            # run_task_3_back(Ctrl, msg)
+            run_task_3_back(Ctrl, msg)
-            pass
         else:
             run_task_4_back(Ctrl, msg)
 

task_2_5/task_2_5.py

@@ -26,12 +26,12 @@ def run_task_2_5(Ctrl, msg, direction='left'):
         Ctrl,
         msg,
         angle_deg=90 if direction == 'left' else -90,
-        target_distance=0.3,
+        target_distance=0.2,
         detect_func_version=3,
         observe=observe,
     )
 
-    go_straight(Ctrl, msg, distance=0.2, speed=0.5, observe=observe)
+    go_straight(Ctrl, msg, distance=0.1, speed=0.5, observe=observe)
 
     section('任务2.5-2：第二次旋转', "移动")
 

task_3/task_3.py

@@ -495,7 +495,7 @@ def run_task_3(ctrl, msg):
     info('开始执行任务3...', "启动")
 
     turn_degree_v2(ctrl, msg, 90, absolute=True)
-    go_lateral(ctrl, msg, distance=0.2, speed=0.1, observe=True)
+    # go_lateral(ctrl, msg, distance=0.2, speed=0.1, observe=True)
 
     section('任务3-1：up', "开始")
     pass_up_down(ctrl, msg)
@@ -520,3 +520,6 @@ def run_task_3(ctrl, msg):
     time.sleep(3)
     info("完成原地站立", "站立")
     
+
+def run_task_3_back(ctrl, msg):
+    return

task_5/task_5.py

@@ -244,7 +244,7 @@ def run_task_5(ctrl, msg, direction='left', observe=False):
 
     # section('任务5-4：返回', "移动")
     go_straight(ctrl, msg, distance=-1, speed=0.5, observe=observe)
-    move_to_hori_line(ctrl, msg, target_distance=1.2, observe=observe)
+    move_to_hori_line(ctrl, msg, target_distance=1.3, observe=observe)
 
     section('任务5-5：转弯', "转弯")
     turn_degree_v2(ctrl, msg, degree=90)

utils/detect_track.py

@@ -817,6 +817,7 @@ def detect_horizontal_track_edge_v3(image, observe=False, delay=1000, save_log=T
         edge_point: 赛道前方边缘点的坐标 (x, y)
         edge_info: 边缘信息字典
     """
+    observe = False
     # 如果输入是字符串（文件路径），则加载图像
     if isinstance(image, str):
         img = cv2.imread(image)
@@ -1418,6 +1419,7 @@ def detect_left_side_track(image, observe=False, delay=1000, save_log=True):
     返回:
     tuple: (线信息字典, 最佳跟踪点)
     """
+    observe = False
     # 如果输入是字符串（文件路径），则加载图像
     if isinstance(image, str):
         img = cv2.imread(image)