mirror of
https://github.com/AtsushiSakai/PythonRobotics.git
synced 2026-01-13 14:48:02 -05:00
improve code
This commit is contained in:
AtsushiSakai
@@ -12,11 +12,15 @@
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import random
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import math
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import copy
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import pandas as pd
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import numpy as np
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import reeds_shepp_path_planning
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import pure_pursuit
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import unicycle_model
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import pandas as pd
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target_speed = 10.0 / 3.6
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accel = 0.1
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curvature = 10.0
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class RRT():
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@@ -82,7 +86,7 @@ class RRT():
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print("feasible path is found")
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break
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return x, y, yaw, v, t, a, d
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return flag, x, y, yaw, v, t, a, d
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def calc_tracking_path(self, path):
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path = np.matrix(path[::-1])
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@@ -106,14 +110,8 @@ class RRT():
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def check_tracking_path_is_feasible(self, path):
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print("check_tracking_path_is_feasible")
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path = np.matrix(path[::-1])
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init_speed = 0.0
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max_speed = 10.0 / 3.6
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path = self.calc_tracking_path(path)
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# speed_profile = self.calc_speed_profile(path, max_speed)
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# plt.plot(path[:, 0], path[:, 1], '-xg')
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# save csv
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df = pd.DataFrame()
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df["x"] = np.array(path[:, 0]).flatten()
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@@ -121,52 +119,34 @@ class RRT():
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df["yaw"] = np.array(path[:, 2]).flatten()
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df.to_csv("rrt_course.csv", index=None)
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state = unicycle_model.State(
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x=self.start.x, y=self.start.y, yaw=self.start.yaw, v=init_speed)
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cx = np.array(path[:, 0])
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cy = np.array(path[:, 1])
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cyaw = np.array(path[:, 2])
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target_ind = pure_pursuit.calc_nearest_index(
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state, path[:, 0], path[:, 1])
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goal = [cx[-1], cy[-1], cyaw[-1]]
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lastIndex = len(path[:, 0]) - 2
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cx, cy, cyaw = pure_pursuit.extend_path(cx, cy, cyaw)
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x = [state.x]
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y = [state.y]
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yaw = [state.yaw]
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v = [state.v]
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t = [0.0]
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a = [0.0]
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d = [0.0]
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time = 0.0
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speed_profile = pure_pursuit.calc_speed_profile(
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cx, cy, cyaw, target_speed, accel)
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while lastIndex > target_ind:
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# print(lastIndex, target_ind)
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ai = pure_pursuit.PIDControl(max_speed, state.v)
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di, target_ind = pure_pursuit.pure_pursuit_control(
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state, path[:, 0], path[:, 1], target_ind)
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state = unicycle_model.update(state, ai, di)
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t, x, y, yaw, v, a, d, find_goal = pure_pursuit.closed_loop_prediction(
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cx, cy, cyaw, speed_profile, goal)
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time = time + unicycle_model.dt
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if abs(yaw[-1] - goal[2]) >= math.pi / 2.0:
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print(yaw[-1], goal[2])
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find_goal = False
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if not find_goal:
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print("This path is bad")
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x.append(state.x)
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y.append(state.y)
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yaw.append(state.yaw)
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v.append(state.v)
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t.append(time)
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a.append(ai)
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d.append(di)
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plt.clf
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plt.plot(x, y, '-r')
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plt.plot(path[:, 0], path[:, 1], '-g')
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plt.grid(True)
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plt.axis("equal")
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plt.show()
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if self.CollisionCheckWithXY(path[:, 0], path[:, 1], self.obstacleList):
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# print("OK")
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return True, x, y, yaw, v, t, a, d
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else:
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# print("NG")
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return False, x, y, yaw, v, t, a, d
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# plt.plot(x, y, '-r')
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# plt.plot(path[:, 0], path[:, 1], '-g')
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# plt.show()
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# return True
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return find_goal, x, y, yaw, v, t, a, d
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def choose_parent(self, newNode, nearinds):
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if len(nearinds) == 0:
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@@ -202,7 +182,6 @@ class RRT():
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def steer(self, rnd, nind):
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# print(rnd)
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curvature = 5.0
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nearestNode = self.nodeList[nind]
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@@ -313,6 +292,7 @@ class RRT():
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for node in self.nodeList:
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if node.parent is not None:
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plt.plot(node.path_x, node.path_y, "-g")
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pass
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# plt.plot([node.x, self.nodeList[node.parent].x], [
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# node.y, self.nodeList[node.parent].y], "-g")
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@@ -401,7 +381,11 @@ if __name__ == '__main__':
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goal = [10.0, 10.0, math.radians(0.0)]
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rrt = RRT(start, goal, randArea=[-2.0, 15.0], obstacleList=obstacleList)
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x, y, yaw, v, t, a, d = rrt.Planning(animation=False)
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flag, x, y, yaw, v, t, a, d = rrt.Planning(animation=False)
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if not flag:
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print("cannot find feasible path")
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exit()
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# flg, ax = plt.subplots(1)
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# Draw final path
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@@ -429,7 +413,7 @@ if __name__ == '__main__':
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flg, ax = plt.subplots(1)
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plt.plot(t, a, '-r')
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plt.xlabel("time[s]")
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plt.ylabel("input[m/s]")
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plt.ylabel("accel[m/ss]")
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plt.grid(True)
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flg, ax = plt.subplots(1)
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@@ -12,8 +12,14 @@ import math
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import matplotlib.pyplot as plt
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import unicycle_model
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Kp = 1.0 # speed propotional gain
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Kp = 2.0 # speed propotional gain
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Lf = 1.0 # look-ahead distance
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T = 1000.0 # max simulation time
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goal_dis = 0.5
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stop_speed = 0.1
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# animation = True
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animation = False
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def PIDControl(target, current):
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@@ -24,20 +30,24 @@ def PIDControl(target, current):
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def pure_pursuit_control(state, cx, cy, pind):
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if state.v >= 0:
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ind = calc_nearest_index(state, cx[pind:], cy[pind:])
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ind = calc_target_index(state, cx, cy)
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if pind >= ind:
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ind = pind
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# print(pind, ind)
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if ind < len(cx):
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tx = cx[ind]
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ty = cy[ind]
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else:
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ind = calc_nearest_index(state, cx[:pind + 1], cy[:pind + 1])
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if state.v >= 0:
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ind = ind + pind
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tx = cx[ind]
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ty = cy[ind]
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tx = cx[-1]
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ty = cy[-1]
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ind = len(cx) - 1
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alpha = math.atan2(ty - state.y, tx - state.x) - state.yaw
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if state.v < 0: # back
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if state.v <= 0.0: # back
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# alpha = math.pi - alpha
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if alpha > 0:
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alpha = math.pi - alpha
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else:
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@@ -45,33 +55,195 @@ def pure_pursuit_control(state, cx, cy, pind):
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delta = math.atan2(2.0 * unicycle_model.L * math.sin(alpha) / Lf, 1.0)
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if state.v < 0: # back
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delta = delta * -1.0
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return delta, ind
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def calc_nearest_index(state, cx, cy):
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def calc_target_index(state, cx, cy):
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dx = [state.x - icx for icx in cx]
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dy = [state.y - icy for icy in cy]
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d = [abs(math.sqrt(idx ** 2 + idy ** 2) -
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Lf) for (idx, idy) in zip(dx, dy)]
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d = [abs(math.sqrt(idx ** 2 + idy ** 2)) for (idx, idy) in zip(dx, dy)]
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ind = d.index(min(d))
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L = 0.0
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while Lf > L and (ind + 1) < len(cx):
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dx = cx[ind + 1] - cx[ind]
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dy = cx[ind + 1] - cx[ind]
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L += math.sqrt(dx ** 2 + dy ** 2)
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ind += 1
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return ind
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def closed_loop_prediction(cx, cy, cyaw, speed_profile, goal):
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state = unicycle_model.State(x=-0.0, y=-0.0, yaw=0.0, v=0.0)
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# lastIndex = len(cx) - 1
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time = 0.0
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x = [state.x]
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y = [state.y]
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yaw = [state.yaw]
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v = [state.v]
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t = [0.0]
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a = [0.0]
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d = [0.0]
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target_ind = calc_target_index(state, cx, cy)
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find_goal = False
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while T >= time:
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di, target_ind = pure_pursuit_control(state, cx, cy, target_ind)
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ai = PIDControl(speed_profile[target_ind], state.v)
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state = unicycle_model.update(state, ai, di)
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if abs(state.v) <= stop_speed:
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target_ind += 1
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time = time + unicycle_model.dt
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# check goal
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dx = state.x - goal[0]
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dy = state.y - goal[1]
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if math.sqrt(dx ** 2 + dy ** 2) <= goal_dis:
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find_goal = True
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break
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x.append(state.x)
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y.append(state.y)
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yaw.append(state.yaw)
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v.append(state.v)
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t.append(time)
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a.append(ai)
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d.append(di)
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if target_ind % 20 == 0 and animation:
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plt.cla()
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plt.plot(cx, cy, "-r", label="course")
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plt.plot(x, y, "ob", label="trajectory")
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plt.plot(cx[target_ind], cy[target_ind], "xg", label="target")
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plt.axis("equal")
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plt.grid(True)
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plt.title("speed:" + str(round(state.v, 2)) +
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"tind:" + str(target_ind))
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plt.pause(0.0001)
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return t, x, y, yaw, v, a, d, find_goal
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def set_stop_point(target_speed, cx, cy, cyaw):
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speed_profile = [target_speed] * len(cx)
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forward = True
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d = []
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# Set stop point
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for i in range(len(cx) - 1):
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dx = cx[i + 1] - cx[i]
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dy = cy[i + 1] - cy[i]
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d.append(math.sqrt(dx ** 2.0 + dy ** 2.0))
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iyaw = cyaw[i]
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move_direction = math.atan2(dy, dx)
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is_back = abs(move_direction - iyaw) >= math.pi / 2.0
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if dx == 0.0 and dy == 0.0:
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continue
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if is_back:
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speed_profile[i] = - target_speed
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else:
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speed_profile[i] = target_speed
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if is_back and forward:
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speed_profile[i] = 0.0
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forward = False
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# plt.plot(cx[i], cy[i], "xb")
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# print(iyaw, move_direction, dx, dy)
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elif not is_back and not forward:
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speed_profile[i] = 0.0
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forward = True
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# plt.plot(cx[i], cy[i], "xb")
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# print(iyaw, move_direction, dx, dy)
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speed_profile[0] = 0.0
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speed_profile[-1] = 0.0
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d.append(d[-1])
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return speed_profile, d
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def calc_speed_profile(cx, cy, cyaw, target_speed, a):
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speed_profile, d = set_stop_point(target_speed, cx, cy, cyaw)
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nsp = len(speed_profile)
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if animation:
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plt.plot(speed_profile, "xb")
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# forward integration
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for i in range(nsp - 1):
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if speed_profile[i + 1] >= 0: # forward
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tspeed = speed_profile[i] + a * d[i]
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if tspeed <= speed_profile[i + 1]:
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speed_profile[i + 1] = tspeed
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else:
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tspeed = speed_profile[i] - a * d[i]
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if tspeed >= speed_profile[i + 1]:
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speed_profile[i + 1] = tspeed
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if animation:
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plt.plot(speed_profile, "ok")
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# back integration
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for i in range(nsp - 1):
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if speed_profile[- i - 1] >= 0: # forward
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tspeed = speed_profile[-i] + a * d[-i]
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if tspeed <= speed_profile[-i - 1]:
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speed_profile[-i - 1] = tspeed
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else:
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tspeed = speed_profile[-i] - a * d[-i]
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if tspeed >= speed_profile[-i - 1]:
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speed_profile[-i - 1] = tspeed
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if animation:
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plt.plot(speed_profile, "-r")
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plt.show()
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return speed_profile
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def extend_path(cx, cy, cyaw):
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dl = 0.1
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dl_list = [dl] * (int(Lf / dl) + 10)
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move_direction = math.atan2(cy[-1] - cy[-2], cx[-1] - cx[-2])
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is_back = abs(move_direction - cyaw[-1]) >= math.pi / 2.0
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for idl in dl_list:
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if is_back:
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idl *= -1
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cx = np.append(cx, cx[-1] + idl * math.cos(cyaw[-1]))
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cy = np.append(cy, cy[-1] + idl * math.sin(cyaw[-1]))
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cyaw = np.append(cyaw, cyaw[-1])
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return cx, cy, cyaw
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def main():
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# target course
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# target course
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import numpy as np
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cx = np.arange(0, 50, 0.1)
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cy = [math.sin(ix / 5.0) * ix / 2.0 for ix in cx]
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target_speed = 30.0 / 3.6
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target_speed = 5.0 / 3.6
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T = 15.0 # max simulation time
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state = unicycle_model.State(x=-1.0, y=-5.0, yaw=0.0, v=0.0)
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state = unicycle_model.State(x=-0.0, y=-3.0, yaw=0.0, v=0.0)
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# state = unicycle_model.State(x=-1.0, y=-5.0, yaw=0.0, v=-30.0 / 3.6)
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# state = unicycle_model.State(x=10.0, y=5.0, yaw=0.0, v=-30.0 / 3.6)
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# state = unicycle_model.State(
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@@ -86,7 +258,7 @@ def main():
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yaw = [state.yaw]
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v = [state.v]
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t = [0.0]
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target_ind = calc_nearest_index(state, cx, cy)
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target_ind = calc_target_index(state, cx, cy)
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while T >= time and lastIndex > target_ind:
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ai = PIDControl(target_speed, state.v)
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@@ -127,6 +299,44 @@ def main():
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plt.show()
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def main2():
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import pandas as pd
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data = pd.read_csv("rrt_course.csv")
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cx = np.array(data["x"])
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cy = np.array(data["y"])
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cyaw = np.array(data["yaw"])
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target_speed = 10.0 / 3.6
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a = 2.0
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goal = [cx[-1], cy[-1]]
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cx, cy, cyaw = extend_path(cx, cy, cyaw)
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speed_profile = calc_speed_profile(cx, cy, cyaw, target_speed, a)
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t, x, y, yaw, v, a, d, flag = closed_loop_prediction(
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cx, cy, cyaw, speed_profile, goal)
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flg, ax = plt.subplots(1)
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plt.plot(cx, cy, ".r", label="course")
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plt.plot(x, y, "-b", label="trajectory")
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plt.plot(goal[0], goal[1], "xg", label="goal")
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plt.legend()
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plt.xlabel("x[m]")
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plt.ylabel("y[m]")
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plt.axis("equal")
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plt.grid(True)
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flg, ax = plt.subplots(1)
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plt.plot(t, [iv * 3.6 for iv in v], "-r")
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plt.xlabel("Time[s]")
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plt.ylabel("Speed[km/h]")
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plt.grid(True)
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plt.show()
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if __name__ == '__main__':
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print("Pure pursuit path tracking simulation start")
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main()
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# main()
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main2()
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