diff --git a/Code/MowingRobot/pibot_ros/ros_ws/src/upbot_location/src/align.cpp b/Code/MowingRobot/pibot_ros/ros_ws/src/upbot_location/src/align.cpp
index 941d989..8cb47b9 100644
--- a/Code/MowingRobot/pibot_ros/ros_ws/src/upbot_location/src/align.cpp
+++ b/Code/MowingRobot/pibot_ros/ros_ws/src/upbot_location/src/align.cpp
@@ -38,8 +38,8 @@ namespace uwb_slam{
         Mat Kg(2, 2, 0);
         Mat Z(2, 2, 0);
 
-        uwbStartPos.mat[0][0]=9;
-        uwbStartPos.mat[1][0]=0;
+        uwbStartPos.mat[0][0]=9;//表示UWB设备相对于imu在x方向上有9cm的距离,后期如UWB摆放在imu正上方即删除
+        uwbStartPos.mat[1][0]=0;//表示UWB设备相对于imu在y方向上有0cm的距离
         Mat H = F;
         Mat I = F;
         bool imuStartFlag = 1;
@@ -65,7 +65,8 @@ namespace uwb_slam{
 
                 std::cout<<"roll:"<<roll<<std::endl;
                 if (imuStartFlag == 1 && imuPos.mat[0][0] != 0 && imuPos.mat[1][0] != 0) {
-                    imuStartRoll = roll-PI/2;
+                    imuStartRoll = roll-PI/2;//跟imu摆放的位置有关,减PI / 2是为了让角度变成跟车前进方向的夹角
+
                     imuStartPos = imuPos;
                     imuStartFlag = 0;
                 }
@@ -76,7 +77,7 @@ namespace uwb_slam{
                 prevPos = imuPos;
                 if (uwbDataRxTime != uwb_->uwb_data_.uwb_t_) {
                     std::cout << "uwb received" << std::endl;
-
+                    //这一步是为了把上述提到的UWB设备与imu设备不在同一坐标轴上设计的坐标轴对齐操作
                     Rotate.mat[0][0] = cos(roll);
                     Rotate.mat[0][1] = -sin(roll);
                     Rotate.mat[1][0] = sin(roll);
@@ -86,7 +87,8 @@ namespace uwb_slam{
                     uwbPos.mat[1][0] = uwb_->uwb_data_.y_;
 
                     uwbPos = uwbPos - Rotate * uwbStartPos;
-
+                    //后期如UWB摆放在imu正上方即删除
+                    //卡尔曼更新过程
                     predPos = F * syncPos + detPos;
                     Z = H * uwbPos;
                     P = F * P * (~F) + Q;
@@ -95,7 +97,7 @@ namespace uwb_slam{
                     P = (I - Kg * H) * P;
                     uwbDataRxTime = uwb_->uwb_data_.uwb_t_;
                 } else {
-                    syncPos = syncPos + detPos;
+                    syncPos = syncPos + detPos;//如果UWB没有更新信息,则使用imu对齐位置进行更新
                 }
                 imuDataRxTime = imu_odom_.imu_data_.imu_t_;
                 odomDataRxTime = odom_tmp_;
diff --git a/Code/MowingRobot/pibot_ros/ros_ws/src/upbot_location/src/uwb.cpp b/Code/MowingRobot/pibot_ros/ros_ws/src/upbot_location/src/uwb.cpp
index 2a17887..6d41146 100644
--- a/Code/MowingRobot/pibot_ros/ros_ws/src/upbot_location/src/uwb.cpp
+++ b/Code/MowingRobot/pibot_ros/ros_ws/src/upbot_location/src/uwb.cpp
@@ -45,15 +45,7 @@ namespace uwb_slam{
         
         while(1){
         this->Serread();
-           // std::cout<<"s"<<std::endl;
-           // std::cout<<this->x<<std::endl;
-        /*if(t_tmp!=imu_odom_.imu_data_.imu_t_){
-            imu_odom_queue_.push(imu_odom_);
-            t_tmp=imu_odom_.imu_data_.imu_t_;
-        }*/
         }
-
-
     }
 
 
@@ -82,13 +74,19 @@ namespace uwb_slam{
         memcpy(&this->d[1], &tmpdata[5], sizeof(uint16_t));
         memcpy(&this->d[2], &tmpdata[7], sizeof(uint16_t));
         // std::cout << "d:" << d[0] << " " << d[1] << " " << d[2] << std::endl;
+        // 如果距离过大说明数据无效
         if(abs(d[0]) > 2000 || abs(d[1]) > 2000 || abs(d[2]) > 2000) {
             return;
         }
+        // 修正车子和标签的高度差
+        // d[i]是三维距离,
         for(int i=0; i<3; i++)
         {
             this->d[i] = sqrt(this->d[i] * this->d[i] - (AnchorPos[i][2] - CARHEIGHT) * (AnchorPos[i][2] - CARHEIGHT));
         }
+        // 多项式拟合,用于提高精度,注释之后不影响
+        // 在不同的距离采集数据,然后拟合出一条曲线
+        // 这个地方的参数纯粹和设备特性有关,和标签位置无关 
         d[0] = ((((4.9083e-07 * d[0]) - 4.6166e-04) * d[0]) + 1.0789) * d[0] + 5.4539;
         d[1] = ((((-4.1679e-07 * d[1]) + 5.0999e-04) * d[1]) + 0.7930) * d[1] + 29.8296;
         d[2] = ((((2.3514e-07 * d[2]) - 1.8277e-04) * d[2]) + 0.9935) * d[2] + 9.8852;
@@ -98,6 +96,7 @@ namespace uwb_slam{
                         -(this->AnchorPos[0][0]*this->AnchorPos[0][0]-this->AnchorPos[i+1][0]*this->AnchorPos[i+1][0])\
                         -(this->AnchorPos[0][1]*this->AnchorPos[0][1]-this->AnchorPos[i+1][1]*this->AnchorPos[i+1][1]);
         }
+        // 构造线性最小二乘求解位置
         Mat AT=~A;
         uwbPos=(AT*A)%AT*b;
         this->uwb_data_.x_ = uwbPos.mat[0][0];
diff --git a/Docs/UWB解算原理.md b/Docs/UWB解算原理.md
new file mode 100644
index 0000000..ae176dd
--- /dev/null
+++ b/Docs/UWB解算原理.md
@@ -0,0 +1,108 @@
+# UWB 定位原理
+
+#### 1.三维坐标转二维坐标
+
+​		在割草机器人项目中,割草机器人目前只考虑二维平面的定位。但是UWB测量的距离是三维距离,所以我们根据机器人的高度`carH`和`UWB`标签的高度`UwbH`计算出水平距离`dxy`。
+
+```cpp
+// dxy^2 = di^2 - (UwbH = carH)^2 
+for(int i=0; i<3; i++){
+	this->d[i] = sqrt(this->d[i] * this->d[i] - (AnchorPos[i][2] - CARHEIGHT) * 
+                     (AnchorPos[i][2] - CARHEIGHT));
+}
+```
+
+<img src="./Image/UWB高度修正.png" alt="" style="zoom:50%;" />
+
+#### 2.多项式拟合
+
+​		UWB的定位是存在波动的,所以会根据UWB计算距离的规律对计算的距离进行多项式拟合,可以起到滤波提高精度作用。下面的计算实际是收集不同实测距离下,UWB的实际输出距离,利用3次多项式拟合得到的结果。
+
+​		下面的计算跟标签的位置以及高度无关,主要跟UWB的硬件设备的特性有关。
+
+```cpp
+d[0] = ((((4.9083e-07 * d[0]) - 4.6166e-04) * d[0]) + 1.0789) * d[0] + 5.4539;
+d[1] = ((((-4.1679e-07 * d[1]) + 5.0999e-04) * d[1]) + 0.7930) * d[1] + 29.8296;
+d[2] = ((((2.3514e-07 * d[2]) - 1.8277e-04) * d[2]) + 0.9935) * d[2] + 9.8852;
+```
+
+#### 3.位置求解
+
+​		UWB位置求解采用如下图示:
+
+<img src="./Image/UWB位置示意图.png" alt="" style="zoom:50%;" />
+
+​		UWB的定位可以用下面公式描述, 其中$(x,y)$是割草机器人上面的UWB的位置,另外三个坐标点是3个UWB标签的位置,可以有如下的公式。
+$$
+d_1^2 = (x_1 - x)^2 + (y_1 - y)^2   \space\space\space\space\space (1)\\
+d_2^2 = (x_2 - x)^2 + (y_2 - y)^2   \space\space\space\space\space(2)\\
+d_3^2 = (x_3 - x)^2 + (y_3 - y)^2   \space\space\space\space\space(3)\\
+$$
+​		$(2)-(1)$以及$(3)-(2)$消去二次项,可得:
+$$
+d_1^2 - d_2^2 = \left[ -2(x_1 - x_2)x + x_1^2 - x_2^2 \right] + \left[ -2(y_1 - y_2)y + y_1^2 - y_2^2 \right] \\
+
+d_1^2 - d_3^2 = \left[ -2(x_1 - x_3)x + x_1^2 - x_3^2 \right] + \left[ -2(y_1 - y_3)y + y_1^2 - y_3^2 \right]
+$$
+​		整理为矩阵形式:
+$$
+-2 \begin{bmatrix}
+x_1 - x_2 & y_1 - y_2 \\
+x_1 - x_3 & y_1 - y_3
+\end{bmatrix}
+\begin{bmatrix}
+x \\
+y
+\end{bmatrix}
+=
+\begin{bmatrix}
+(d_1^2 - d_2^2) - (x_1^2 - x_3^2) - (y_1^2 - y_3^2) \\
+(d_1^2 - d_3^2) - (x_1^2 - x_3^2) - (y_1^2 - y_3^2)
+\end{bmatrix}
+$$
+​		整理可得:
+$$
+\begin{align*}
+A &= -2\cdot \begin{bmatrix}
+x_1 - x_2 & y_1 - y_2 \\
+x_1 - x_3 & y_1 - y_3
+\end{bmatrix}\\
+b &= \begin{bmatrix}
+(d_1^2 - d_2^2) - (x_1^2 - x_2^2) - (y_1^2 - y_2^2) \\
+(d_1^2 - d_3^2) - (x_1^2 - x_3^2) - (y_1^2 - y_3^2)
+\end{bmatrix}\\
+X &= \begin{bmatrix}
+x\\
+y
+\end{bmatrix}
+\end{align*}
+$$
+​		矩阵A对应的代码:
+
+```cpp
+for(int i=0; i<2; i++){
+    A.mat[i][0] = -2*(this->AnchorPos[0][0]-this->AnchorPos[i+1][0]);
+    A.mat[i][1] = -2*(this->AnchorPos[0][1]-this->AnchorPos[i+1][1]);
+}
+```
+
+​		矩阵b对应的代码:
+
+```cpp
+for(int i=0; i<2; i++)
+{
+	b.mat[i][0] = (this->d[0]*this->d[0]-this->d[i+1]*this->d[i+1])\
+     - (this->AnchorPos[0][0]*this->AnchorPos[0][0]-this->AnchorPos[i+1][0]*this->AnchorPos[i+1][0]) 
+     - (this->AnchorPos[0][1]*this->AnchorPos[0][1]-this->AnchorPos[i+1][1]*this->AnchorPos[i+1][1]);
+}
+```
+
+​		那么,上述矩阵可以通过$X=(A^T\cdot A)^{-1}A^T*b$ 求解UWB的位置。
+
+```cpp
+Mat AT=~A;
+uwbPos=(AT*A)%AT*b;
+this->uwb_data_.x_ = uwbPos.mat[0][0];
+this->uwb_data_.y_ = uwbPos.mat[1][0];
+```
+
diff --git a/Docs/image/UWB位置示意图.png b/Docs/image/UWB位置示意图.png
new file mode 100644
index 0000000..19a3be3
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diff --git a/Docs/image/UWB高度修正.png b/Docs/image/UWB高度修正.png
new file mode 100644
index 0000000..5c8659b
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diff --git a/Docs/image/uwb安装说明.png b/Docs/image/uwb安装说明.png
new file mode 100644
index 0000000..699bb7e
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diff --git a/Docs/割草机融合定位:.md b/Docs/割草机融合定位:.md
new file mode 100644
index 0000000..6cd8fc1
--- /dev/null
+++ b/Docs/割草机融合定位:.md
@@ -0,0 +1,9 @@
+割草机融合定位:
+地图坐标系与小车坐标系重合,小车起始点为坐标系原点,使用三个标签分别是A,B,C。A放置在充电桩处。车上放置基站M。通过基站到三个标签的距离获得小车的位置信息。如图所示:
+
+![img](file:///C:\Users\ray\AppData\Local\Temp\ksohtml2328\wps2.png)
+
+注:为了方便,人为设定UWB坐标系与地图坐标系和小车坐标系均重合。
+小车半径:a
+标签高度:h
+BC标签距离d
\ No newline at end of file