To fix AtsushiSakai#298

PathTracking/pure_pursuit/pure_pursuit.py

@@ -1,6 +1,6 @@
 """
 
-Path tracking simulation with pure pursuit steering control and PID speed control.
+Path tracking simulation with pure pursuit steering and PID speed control.
 
 author: Atsushi Sakai (@Atsushi_twi)
         Guillaume Jacquenot (@Gjacquenot)
@@ -10,13 +10,12 @@
 import math
 import matplotlib.pyplot as plt
 
-
+# Parameters
 k = 0.1  # look forward gain
-Lfc = 2.0  # look-ahead distance
+Lfc = 2.0  # [m] look-ahead distance
 Kp = 1.0  # speed proportional gain
-dt = 0.1  # [s]
-L = 2.9  # [m] wheel base of vehicle
-
+dt = 0.1  # [s] time tick
+WB = 2.9  # [m] wheel base of vehicle
 
 show_animation = True
 
@@ -28,20 +27,18 @@ def __init__(self, x=0.0, y=0.0, yaw=0.0, v=0.0):
         self.y = y
         self.yaw = yaw
         self.v = v
-        self.rear_x = self.x - ((L / 2) * math.cos(self.yaw))
-        self.rear_y = self.y - ((L / 2) * math.sin(self.yaw))
+        self.rear_x = self.x - ((WB / 2) * math.cos(self.yaw))
+        self.rear_y = self.y - ((WB / 2) * math.sin(self.yaw))
 
     def update(self, a, delta):
-
         self.x += self.v * math.cos(self.yaw) * dt
         self.y += self.v * math.sin(self.yaw) * dt
-        self.yaw += self.v / L * math.tan(delta) * dt
+        self.yaw += self.v / WB * math.tan(delta) * dt
         self.v += a * dt
-        self.rear_x = self.x - ((L / 2) * math.cos(self.yaw))
-        self.rear_y = self.y - ((L / 2) * math.sin(self.yaw))
+        self.rear_x = self.x - ((WB / 2) * math.cos(self.yaw))
+        self.rear_y = self.y - ((WB / 2) * math.sin(self.yaw))
 
     def calc_distance(self, point_x, point_y):
-
         dx = self.rear_x - point_x
         dy = self.rear_y - point_y
         return math.hypot(dx, dy)
@@ -56,27 +53,30 @@ def __init__(self):
         self.v = []
         self.t = []
 
-    def append(self, t , state):
+    def append(self, t, state):
         self.x.append(state.x)
         self.y.append(state.y)
         self.yaw.append(state.yaw)
         self.v.append(state.v)
         self.t.append(t)
 
 
-def PIDControl(target, current):
+def proportional_control(target, current):
     a = Kp * (target - current)
 
     return a
 
 
-class Trajectory:
+class TargetCourse:
+
     def __init__(self, cx, cy):
         self.cx = cx
         self.cy = cy
         self.old_nearest_point_index = None
 
     def search_target_index(self, state):
+
+        # To speed up nearest point search, doing it at only first time.
         if self.old_nearest_point_index is None:
             # search nearest point index
             dx = [state.rear_x - icx for icx in self.cx]
@@ -86,47 +86,45 @@ def search_target_index(self, state):
             self.old_nearest_point_index = ind
         else:
             ind = self.old_nearest_point_index
-            distance_this_index = state.calc_distance(self.cx[ind], self.cy[ind])
+            distance_this_index = state.calc_distance(self.cx[ind],
+                                                      self.cy[ind])
             while True:
-                ind = ind + 1 if (ind + 1) < len(self.cx) else ind
-                distance_next_index = state.calc_distance(self.cx[ind], self.cy[ind])
+                distance_next_index = state.calc_distance(self.cx[ind + 1],
+                                                          self.cy[ind + 1])
                 if distance_this_index < distance_next_index:
                     break
+                ind = ind + 1 if (ind + 1) < len(self.cx) else ind
                 distance_this_index = distance_next_index
             self.old_nearest_point_index = ind
 
-        L = 0.0
-
-        Lf = k * state.v + Lfc
+        Lf = k * state.v + Lfc  # update look ahead distance
 
         # search look ahead target point index
-        while Lf > L and (ind + 1) < len(self.cx):
-            L = state.calc_distance(self.cx[ind], self.cy[ind])
+        while Lf > state.calc_distance(self.cx[ind], self.cy[ind]):
+            if (ind + 1) >= len(self.cx):
+                break  # not exceed goal
             ind += 1
 
-        return ind
+        return ind, Lf
 
 
-def pure_pursuit_control(state, trajectory, pind):
-
-    ind = trajectory.search_target_index(state)
+def pure_pursuit_steer_control(state, trajectory, pind):
+    ind, Lf = trajectory.search_target_index(state)
 
     if pind >= ind:
         ind = pind
 
     if ind < len(trajectory.cx):
         tx = trajectory.cx[ind]
         ty = trajectory.cy[ind]
-    else:
+    else:  # toward goal
         tx = trajectory.cx[-1]
         ty = trajectory.cy[-1]
         ind = len(trajectory.cx) - 1
 
     alpha = math.atan2(ty - state.rear_y, tx - state.rear_x) - state.yaw
 
-    Lf = k * state.v + Lfc
-
-    delta = math.atan2(2.0 * L * math.sin(alpha) / Lf, 1.0)
+    delta = math.atan2(2.0 * WB * math.sin(alpha) / Lf, 1.0)
 
     return delta, ind
 
@@ -147,7 +145,7 @@ def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r", ec="k"):
 
 def main():
     #  target course
-    cx = np.arange(0, 50, 0.1)
+    cx = np.arange(0, 50, 0.5)
     cy = [math.sin(ix / 5.0) * ix / 2.0 for ix in cx]
 
     target_speed = 10.0 / 3.6  # [m/s]
@@ -161,22 +159,27 @@ def main():
     time = 0.0
     states = States()
     states.append(time, state)
-    trajectory = Trajectory(cx, cy)
-    target_ind = trajectory.search_target_index(state)
+    target_course = TargetCourse(cx, cy)
+    target_ind, _ = target_course.search_target_index(state)
 
     while T >= time and lastIndex > target_ind:
-        ai = PIDControl(target_speed, state.v)
-        di, target_ind = pure_pursuit_control(state, trajectory, target_ind)
-        state.update(ai, di)
+
+        # Calc control input
+        ai = proportional_control(target_speed, state.v)
+        di, target_ind = pure_pursuit_steer_control(
+            state, target_course, target_ind)
+
+        state.update(ai, di)  # Control vehicle
 
         time += dt
         states.append(time, state)
 
         if show_animation:  # pragma: no cover
             plt.cla()
             # for stopping simulation with the esc key.
-            plt.gcf().canvas.mpl_connect('key_release_event',
-                    lambda event: [exit(0) if event.key == 'escape' else None])
+            plt.gcf().canvas.mpl_connect(
+                'key_release_event',
+                lambda event: [exit(0) if event.key == 'escape' else None])
             plot_arrow(state.x, state.y, state.yaw)
             plt.plot(cx, cy, "-r", label="course")
             plt.plot(states.x, states.y, "-b", label="trajectory")