diff --git a/README.md b/README.md
index 3ae7a35..e73276a 100644
--- a/README.md
+++ b/README.md
@@ -22,11 +22,9 @@
 ### 编译
 
 ```bash
-cd /workspace
 # pip install -r requirements.txt
+cd /workspace
 colcon build --symlink-install
-source /opt/ros/humble/setup.bash
-source install/setup.bash
 ```
 
 ### 注意事项
@@ -34,63 +32,55 @@ source install/setup.bash
 
 ```bash
 source /opt/ros/humble/setup.bash
-source install/setup.bash
-export TURTLEBOT3_MODEL=burger
+source /workspace/install/setup.bash
 export NAV2_MAP_PATH=/workspace/src/turtlebot3_simulations/turtlebot3_gazebo/map/office_map.yaml
 ```
 
-为了方便，也可以写入 `~/.bashrc`：
-
-```bash
-echo 'source /opt/ros/humble/setup.bash' >> ~/.bashrc
-echo 'source /workspace/install/setup.bash' >> ~/.bashrc
-echo 'export TURTLEBOT3_MODEL=burger' >> ~/.bashrc
-echo 'export NAV2_MAP_PATH=/workspace/src/turtlebot3_simulations/turtlebot3_gazebo/map/office_map.yaml' >> ~/.bashrc
-source ~/.bashrc
-```
-1.   启动仿真， Nav2导航栈，  AMCL节点，rviz
+## 2. 启动仿真(后面每一个步骤都在新终端执行)
 
 ```bash
 ./gzsim_run.sh
- 
+```
+
+## 3. 启动 AMCL节点（负责 “map_server + amcl”）
+
+```bash
 ros2 launch nav2_rrtstar_planner bringup_localization_with_initial_pose.launch.py \
     use_sim_time:=true \
-    map:=$NAV2_MAP_PATH
+    map:=$NAV2_MAP_PATH \
+    params_file:=/workspace/src/TurtleBot-RRT-Star/nav2_params.yaml
+```
 
+## 4. 启动 Nav2导航栈（负责 “RRT* 规划器 + controller + bt_navigator）
+
+```bash
 ros2 launch nav2_bringup navigation_launch.py \
     use_sim_time:=True \
-    params_file:=/workspace/src/TurtleBot-RRT-Star/nav2_params.yaml \
-    map:=$NAV2_MAP_PATH
+    params_file:=/workspace/src/TurtleBot-RRT-Star/nav2_params.yaml
+```
 
+## 5. 打开rviz
+
+```bash
 export __NV_PRIME_RENDER_OFFLOAD=1
 export __GLX_VENDOR_LIBRARY_NAME=nvidia
 ros2 run rviz2 rviz2 -d /opt/ros/humble/share/nav2_bringup/rviz/nav2_default_view.rviz --ros-args -p use_sim_time:=true
-
 ```
-2.   新开终端，执行环境生效后启动vlm_perception_pkg的语义检测节点，开始目标语义标注并构建语义记忆库，在机器人导航过程中，节点实时检测环境中的目标并生成语义标注结果，保存JSON 格式的语义记忆库到指定路径。
+
+新开终端，执行环境生效后启动vlm_perception_pkg的语义检测节点，开始目标语义标注并构建语义记忆库，在机器人导航过程中，节点实时检测环境中的目标并生成语义标注结果，保存JSON 格式的语义记忆库到指定路径。
+## 6. 启动目标检测节点
+
 ```bash
 ros2 run vlm_perception_pkg vlm_detection --ros-args -p use_sim_time:=true
-# 默认：启动时加载历史 JSON（同一个语义记忆库文件）
-# 可选：启动时重置语义记忆库，避免历史最高分长期不更新
+# 默认：启动时加载历史 JSON（同一个语义记忆库文件）可选：启动时重置语义记忆库，避免历史最高分长期不更新
 # ros2 run vlm_perception_pkg vlm_detection --ros-args -p use_sim_time:=true -p reset_records_on_startup:=true
 ```
-3.   加载生成的语义记忆库文件（in /workspace/src/vlm_nav_pkg/config），通过 CLIP 将语义标签编码为shared embedding space，当用户输入自然语言查询时，系统匹配最接近的已知目标，提取其坐标并发送至 Nav2，实现基于自然语言的语义导航。以下是验证步骤：
-4.   重新启动仿真， Nav2导航栈， AMCL节点，rviz
+
+检测完成后可查看目标标注结果（in /workspace/src/vlm_nav_pkg/config，带时间戳），加载生成的语义记忆库文件，通过 CLIP 将语义标签编码为shared embedding space，当用户输入自然语言查询时，系统匹配最接近的已知目标，提取其坐标并发送至 Nav2，实现基于自然语言的语义导航。以下是验证步骤：
+## 7. 启动目标检测节点
+
 ```bash
-
-./gzsim_run.sh
- 
-ros2 launch nav2_rrtstar_planner bringup_localization_with_initial_pose.launch.py \
-    use_sim_time:=true \
-    map:=$NAV2_MAP_PATH
-
-ros2 launch nav2_bringup navigation_launch.py \
-    use_sim_time:=True \
-    params_file:=/workspace/src/TurtleBot-RRT-Star/nav2_params.yaml  \
-    map:=$NAV2_MAP_PATH
- 
-ros2 run rviz2 rviz2 -d /opt/ros/humble/share/nav2_bringup/rviz/nav2_default_view.rviz --ros-args -p use_sim_time:=true
-
+# 关闭检测节点后
 pip3 install "numpy<2"
 ros2 run vlm_nav_pkg semantic_nav
 ```
diff --git a/assets/picture07.png b/assets/picture07.png
index ed383af..1596a90 100644
Binary files a/assets/picture07.png and b/assets/picture07.png differ
diff --git a/src/TurtleBot-RRT-Star/CMakeLists.txt b/src/TurtleBot-RRT-Star/CMakeLists.txt
index 4700401..6eb216f 100755
--- a/src/TurtleBot-RRT-Star/CMakeLists.txt
+++ b/src/TurtleBot-RRT-Star/CMakeLists.txt
@@ -79,10 +79,6 @@ install(DIRECTORY launch/
   DESTINATION share/${PROJECT_NAME}/launch
 )
 
-install(DIRECTORY scripts/
-  DESTINATION share/${PROJECT_NAME}/scripts
-)
-
 if(BUILD_TESTING)
   find_package(ament_lint_auto REQUIRED)
   ament_lint_auto_find_test_dependencies()
diff --git a/src/vlm_nav_pkg/config/example_object_detection_vlm.json b/src/vlm_nav_pkg/config/example_object_detection_vlm.json
index 2befd64..739e61d 100755
--- a/src/vlm_nav_pkg/config/example_object_detection_vlm.json
+++ b/src/vlm_nav_pkg/config/example_object_detection_vlm.json
@@ -1,7 +1,7 @@
 {
     "Refrigerator": {
-        "highest_score": 0.301513671875,
-        "last_detected": "2026-05-18T14:13:25.085735",
+        "highest_score": 0.271240234375,
+        "last_detected": "2026-05-18T10:25:26.258502",
         "position": {
             "x": -1.3181,
             "y": 0.5165,
@@ -9,39 +9,39 @@
         }
     },
     "water dispenser": {
-        "highest_score": 0.316162109375,
-        "last_detected": "2026-05-18T14:15:56.440127",
+        "highest_score": 0.31689453125,
+        "last_detected": "2026-05-18T10:26:11.990891",
         "position": {
-            "x": -1.6292,
-            "y": -1.7906,
-            "z": 0.661
+            "x": -1.4587,
+            "y": -1.8551,
+            "z": 0.6153
         }
     },
     "sofa": {
-        "highest_score": 0.307861328125,
-        "last_detected": "2026-05-18T14:17:27.585956",
+        "highest_score": 0.311767578125,
+        "last_detected": "2026-05-18T10:26:51.065460",
         "position": {
-            "x": 1.4535,
-            "y": -0.4709,
-            "z": 0.5445
+            "x": 1.6362,
+            "y": 0.2425,
+            "z": 0.4759
         }
     },
     "white toilet": {
-        "highest_score": 0.269287109375,
-        "last_detected": "2026-05-18T14:13:07.308074",
+        "highest_score": 0.2568359375,
+        "last_detected": "2026-05-18T10:25:18.120319",
         "position": {
-            "x": -4.3598,
-            "y": -1.1593,
-            "z": 0.3363
+            "x": -4.4027,
+            "y": -1.2225,
+            "z": 0.1715
         }
     },
     "office chair with wheels": {
-        "highest_score": 0.3037109375,
-        "last_detected": "2026-05-18T14:12:39.416197",
+        "highest_score": 0.302734375,
+        "last_detected": "2026-05-18T10:26:35.889506",
         "position": {
-            "x": -2.8461,
-            "y": 2.6732,
-            "z": 0.4811
+            "x": -2.9779,
+            "y": 2.3836,
+            "z": 0.4007
         }
     }
 }
\ No newline at end of file
diff --git a/src/vlm_perception_pkg/vlm_perception_pkg/vlm_detection.py b/src/vlm_perception_pkg/vlm_perception_pkg/vlm_detection.py
index f8998dc..048e67f 100755
--- a/src/vlm_perception_pkg/vlm_perception_pkg/vlm_detection.py
+++ b/src/vlm_perception_pkg/vlm_perception_pkg/vlm_detection.py
@@ -55,7 +55,12 @@ class ClipDetectionNode(Node):
 
         # CLIP + Faster R-CNN
         self.clip_model, self.clip_preprocess = clip.load("ViT-B/32", device=self.device)
-        self.frcnn_model = torchvision.models.detection.fasterrcnn_resnet50_fpn_v2(weights="DEFAULT").eval()
+        self.frcnn_model = torchvision.models.detection.fasterrcnn_resnet50_fpn_v2(
+            weights="DEFAULT"
+        ).to(self.device).eval()
+        self.get_logger().info(
+            f"Detection models initialized on device={self.device}"
+        )
 
         # Labels and score tracking
         self.labels = ["Refrigerator","water dispenser", "sofa", "white toilet",  "office chair with wheels"]
@@ -208,7 +213,7 @@ class ClipDetectionNode(Node):
             depth_img = self.latest_depth.copy()
             frame_stamp = self.latest_depth_stamp
             camera_info = self.camera_info
-            image_tensor = transforms.ToTensor()(cv_img)
+            image_tensor = transforms.ToTensor()(cv_img).to(self.device)
             frcnn_min_score = self.get_parameter(
                 'frcnn_min_score'
             ).get_parameter_value().double_value