fix spell

Signed-off-by: kosuke55 <[email protected]>

control_data_collecting_tool/README.md

@@ -9,9 +9,9 @@ This package provides tools for automatically collecting data using pure pursuit
 - This package aims to collect a dataset consisting of control inputs (i.e. `control_cmd`) and observation variables (i.e. `kinematic_state`, `steering_status`, etc).
 - The collected dataset can be used as training dataset for learning-based controllers, including [smart_mpc](https://github.com/autowarefoundation/autoware.universe/tree/f30c0350861d020ad26a45806ab1334895122fab/control/smart_mpc_trajectory_follower).
 - The data collecting approach is as follows:
-  - Seting a figure-eight target trajectory within the specified rectangular area.
+  - Setting a figure-eight target trajectory within the specified rectangular area.
   - Following the trajectory using a pure pursuit control law.
-  - Adding noises to the trajectory and the control command for data diversity, improveing the prediction accuracy of learning model.
+  - Adding noises to the trajectory and the control command for data diversity, improving the prediction accuracy of learning model.
 
 ## How to use
 
@@ -101,7 +101,7 @@ ROS 2 params in `/data_collecting_pure_pursuit_trajectory_follower` node:
 | :--------------------------------------- | :------- | :------------------------------------------------------------- | :------------ |
 | `pure_pursuit_type`                      | `string` | Pure pursuit type (`naive` or `linearized` steer control law ) | `linearized`  |
 | `wheel_base`                             | `double` | Wheel base [m]                                                 | 2.79          |
-| `acc_kp`                                 | `double` | Accel command propotional gain                                 | 0.5           |
+| `acc_kp`                                 | `double` | Accel command proportional gain                                | 0.5           |
 | `lookahead_time`                         | `double` | Pure pursuit lookahead time [s]                                | 1.5           |
 | `min_lookahead`                          | `double` | Pure pursuit minimum lookahead length [m]                      | 3.0           |
 | `linearized_pure_pursuit_steer_kp_param` | `double` | Linearized pure pursuit steering P gain parameter              | 2.0           |

control_data_collecting_tool/scripts/data_collecting_pure_pursuit_trajectory_follower.py

@@ -40,7 +40,7 @@ def getYaw(orientation_xyzw):
     return R.from_quat(orientation_xyzw.reshape(-1, 4)).as_euler("xyz")[:, 2]
 
 
-class DataCollectingPurePursuitTrajetoryFollower(Node):
+class DataCollectingPurePursuitTrajectoryFollower(Node):
     def __init__(self):
         super().__init__("data_collecting_pure_pursuit_trajectory_follower")
 
@@ -61,7 +61,7 @@ def __init__(self):
         self.declare_parameter(
             "acc_kp",
             0.5,
-            ParameterDescriptor(description="Pure pursuit accel command propotional gain"),
+            ParameterDescriptor(description="Pure pursuit accel command proportional gain"),
         )
 
         self.declare_parameter(
@@ -266,8 +266,8 @@ def pure_pursuit_control(
             np.arctan2(pos_xy_ref_target[1] - pos_xy_obs[1], pos_xy_ref_target[0] - pos_xy_obs[0])
             - pos_yaw_obs[0]
         )
-        angz = 2.0 * longitudinal_vel_ref_nearest * np.sin(alpha) / wheel_base
-        steer = np.arctan(angz * wheel_base / longitudinal_vel_ref_nearest)
+        ang_z = 2.0 * longitudinal_vel_ref_nearest * np.sin(alpha) / wheel_base
+        steer = np.arctan(ang_z * wheel_base / longitudinal_vel_ref_nearest)
 
         return np.array([pure_pursuit_acc_cmd, steer])
 
@@ -284,7 +284,7 @@ def linearized_pure_pursuit_control(
         wheel_base = self.get_parameter("wheel_base").get_parameter_value().double_value
         acc_kp = self.get_parameter("acc_kp").get_parameter_value().double_value
 
-        # Currently, the following params are not declareed as ROS 2 params.
+        # Currently, the following params are not declared as ROS 2 params.
         lookahead_coef = self.get_parameter("lookahead_time").get_parameter_value().double_value
         lookahead_intercept = self.get_parameter("min_lookahead").get_parameter_value().double_value
 
@@ -618,7 +618,7 @@ def control(self):
 def main(args=None):
     rclpy.init(args=args)
 
-    data_collecting_pure_pursuit_trajectory_follower = DataCollectingPurePursuitTrajetoryFollower()
+    data_collecting_pure_pursuit_trajectory_follower = DataCollectingPurePursuitTrajectoryFollower()
 
     rclpy.spin(data_collecting_pure_pursuit_trajectory_follower)
 

control_data_collecting_tool/scripts/data_collecting_trajectory_publisher.py

@@ -58,7 +58,7 @@ def get_trajectory_points(
     D = [(b - a) / 2, -a / 2]
 
     # _O = [0.0, 0.0]  # origin
-    R = a / 2  # radious of the circle
+    R = a / 2  # radius of the circle
     OL = [-(b - a) / 2, 0]  # center of the left circle
     OR = [(b - a) / 2, 0]  # center of the right circle
     OB = np.sqrt((b - a) ** 2 + a**2) / 2  # half length of the linear trajectory
@@ -271,10 +271,10 @@ def updateNominalTargetTrajectory(self):
 
         vec_from_center_to_point0_data = data_collecting_area[0, :2] - rectangle_center_position
         vec_from_center_to_point1_data = data_collecting_area[1, :2] - rectangle_center_position
-        unitvec_from_center_to_point0_data = vec_from_center_to_point0_data / (
+        unit_vec_from_center_to_point0_data = vec_from_center_to_point0_data / (
             np.sqrt((vec_from_center_to_point0_data**2).sum()) + 1e-10
         )
-        unitvec_from_center_to_point1_data = vec_from_center_to_point1_data / (
+        unit_vec_from_center_to_point1_data = vec_from_center_to_point1_data / (
             np.sqrt((vec_from_center_to_point1_data**2).sum()) + 1e-10
         )
 
@@ -283,15 +283,19 @@ def updateNominalTargetTrajectory(self):
         if la > lb:
             long_side_length = la
             short_side_length = lb
-            vec_long_side = -unitvec_from_center_to_point0_data + unitvec_from_center_to_point1_data
+            vec_long_side = (
+                -unit_vec_from_center_to_point0_data + unit_vec_from_center_to_point1_data
+            )
         else:
             long_side_length = lb
             short_side_length = la
-            vec_long_side = unitvec_from_center_to_point0_data + unitvec_from_center_to_point1_data
-        unitvec_long_side = vec_long_side / np.sqrt((vec_long_side**2).sum())
-        if unitvec_long_side[1] < 0:
-            unitvec_long_side *= -1
-        yaw_offset = np.arccos(unitvec_long_side[0])
+            vec_long_side = (
+                unit_vec_from_center_to_point0_data + unit_vec_from_center_to_point1_data
+            )
+        unit_vec_long_side = vec_long_side / np.sqrt((vec_long_side**2).sum())
+        if unit_vec_long_side[1] < 0:
+            unit_vec_long_side *= -1
+        yaw_offset = np.arccos(unit_vec_long_side[0])
         if yaw_offset > pi / 2:
             yaw_offset -= pi