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cb08c39a93
* add normal vector calculation routine. * add normal vector calculation routine. * add normal vector estimation * fix unittests in not matplotlib frontend * fix lint ci * add ransac based normal distribution estimation * add normal_vector_estimation_main.rst * normal_vector_estimation_main.rst を更新 * update normal_vector_estimation_main.rst * update normal_vector_estimation_main.rst * normal_vector_estimation_main.rst * normal_vector_estimation_main.rst を更新 * add normal_vector_estimation_main.rst * add normal_vector_estimation_main.rst * add normal_vector_estimation_main.rst * add normal_vector_estimation_main.rst * add normal_vector_estimation_main.rst * add normal_vector_estimation_main.rst * add normal_vector_estimation_main.rst * add normal_vector_estimation_main.rst * add normal_vector_estimation_main.rst
309 lines
9.9 KiB
Python
309 lines
9.9 KiB
Python
"""
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Mobile robot motion planning sample with Dynamic Window Approach
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author: Atsushi Sakai (@Atsushi_twi), Göktuğ Karakaşlı
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"""
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import math
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from enum import Enum
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import matplotlib.pyplot as plt
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import numpy as np
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show_animation = True
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def dwa_control(x, config, goal, ob):
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"""
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Dynamic Window Approach control
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"""
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dw = calc_dynamic_window(x, config)
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u, trajectory = calc_control_and_trajectory(x, dw, config, goal, ob)
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return u, trajectory
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class RobotType(Enum):
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circle = 0
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rectangle = 1
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class Config:
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"""
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simulation parameter class
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"""
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def __init__(self):
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# robot parameter
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self.max_speed = 1.0 # [m/s]
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self.min_speed = -0.5 # [m/s]
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self.max_yaw_rate = 40.0 * math.pi / 180.0 # [rad/s]
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self.max_accel = 0.2 # [m/ss]
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self.max_delta_yaw_rate = 40.0 * math.pi / 180.0 # [rad/ss]
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self.v_resolution = 0.01 # [m/s]
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self.yaw_rate_resolution = 0.1 * math.pi / 180.0 # [rad/s]
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self.dt = 0.1 # [s] Time tick for motion prediction
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self.predict_time = 3.0 # [s]
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self.to_goal_cost_gain = 0.15
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self.speed_cost_gain = 1.0
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self.obstacle_cost_gain = 1.0
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self.robot_stuck_flag_cons = 0.001 # constant to prevent robot stucked
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self.robot_type = RobotType.circle
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# if robot_type == RobotType.circle
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# Also used to check if goal is reached in both types
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self.robot_radius = 1.0 # [m] for collision check
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# if robot_type == RobotType.rectangle
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self.robot_width = 0.5 # [m] for collision check
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self.robot_length = 1.2 # [m] for collision check
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# obstacles [x(m) y(m), ....]
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self.ob = np.array([[-1, -1],
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[0, 2],
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[4.0, 2.0],
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[5.0, 4.0],
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[5.0, 5.0],
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[5.0, 6.0],
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[5.0, 9.0],
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[8.0, 9.0],
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[7.0, 9.0],
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[8.0, 10.0],
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[9.0, 11.0],
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[12.0, 13.0],
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[12.0, 12.0],
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[15.0, 15.0],
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[13.0, 13.0]
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])
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@property
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def robot_type(self):
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return self._robot_type
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@robot_type.setter
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def robot_type(self, value):
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if not isinstance(value, RobotType):
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raise TypeError("robot_type must be an instance of RobotType")
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self._robot_type = value
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config = Config()
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def motion(x, u, dt):
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"""
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motion model
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"""
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x[2] += u[1] * dt
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x[0] += u[0] * math.cos(x[2]) * dt
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x[1] += u[0] * math.sin(x[2]) * dt
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x[3] = u[0]
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x[4] = u[1]
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return x
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def calc_dynamic_window(x, config):
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"""
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calculation dynamic window based on current state x
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"""
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# Dynamic window from robot specification
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Vs = [config.min_speed, config.max_speed,
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-config.max_yaw_rate, config.max_yaw_rate]
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# Dynamic window from motion model
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Vd = [x[3] - config.max_accel * config.dt,
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x[3] + config.max_accel * config.dt,
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x[4] - config.max_delta_yaw_rate * config.dt,
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x[4] + config.max_delta_yaw_rate * config.dt]
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# [v_min, v_max, yaw_rate_min, yaw_rate_max]
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dw = [max(Vs[0], Vd[0]), min(Vs[1], Vd[1]),
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max(Vs[2], Vd[2]), min(Vs[3], Vd[3])]
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return dw
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def predict_trajectory(x_init, v, y, config):
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"""
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predict trajectory with an input
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"""
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x = np.array(x_init)
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trajectory = np.array(x)
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time = 0
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while time <= config.predict_time:
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x = motion(x, [v, y], config.dt)
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trajectory = np.vstack((trajectory, x))
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time += config.dt
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return trajectory
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def calc_control_and_trajectory(x, dw, config, goal, ob):
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"""
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calculation final input with dynamic window
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"""
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x_init = x[:]
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min_cost = float("inf")
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best_u = [0.0, 0.0]
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best_trajectory = np.array([x])
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# evaluate all trajectory with sampled input in dynamic window
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for v in np.arange(dw[0], dw[1], config.v_resolution):
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for y in np.arange(dw[2], dw[3], config.yaw_rate_resolution):
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trajectory = predict_trajectory(x_init, v, y, config)
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# calc cost
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to_goal_cost = config.to_goal_cost_gain * calc_to_goal_cost(trajectory, goal)
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speed_cost = config.speed_cost_gain * (config.max_speed - trajectory[-1, 3])
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ob_cost = config.obstacle_cost_gain * calc_obstacle_cost(trajectory, ob, config)
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final_cost = to_goal_cost + speed_cost + ob_cost
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# search minimum trajectory
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if min_cost >= final_cost:
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min_cost = final_cost
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best_u = [v, y]
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best_trajectory = trajectory
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if abs(best_u[0]) < config.robot_stuck_flag_cons \
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and abs(x[3]) < config.robot_stuck_flag_cons:
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# to ensure the robot do not get stuck in
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# best v=0 m/s (in front of an obstacle) and
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# best omega=0 rad/s (heading to the goal with
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# angle difference of 0)
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best_u[1] = -config.max_delta_yaw_rate
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return best_u, best_trajectory
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def calc_obstacle_cost(trajectory, ob, config):
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"""
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calc obstacle cost inf: collision
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"""
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ox = ob[:, 0]
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oy = ob[:, 1]
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dx = trajectory[:, 0] - ox[:, None]
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dy = trajectory[:, 1] - oy[:, None]
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r = np.hypot(dx, dy)
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if config.robot_type == RobotType.rectangle:
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yaw = trajectory[:, 2]
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rot = np.array([[np.cos(yaw), -np.sin(yaw)], [np.sin(yaw), np.cos(yaw)]])
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rot = np.transpose(rot, [2, 0, 1])
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local_ob = ob[:, None] - trajectory[:, 0:2]
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local_ob = local_ob.reshape(-1, local_ob.shape[-1])
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local_ob = np.array([local_ob @ x for x in rot])
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local_ob = local_ob.reshape(-1, local_ob.shape[-1])
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upper_check = local_ob[:, 0] <= config.robot_length / 2
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right_check = local_ob[:, 1] <= config.robot_width / 2
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bottom_check = local_ob[:, 0] >= -config.robot_length / 2
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left_check = local_ob[:, 1] >= -config.robot_width / 2
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if (np.logical_and(np.logical_and(upper_check, right_check),
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np.logical_and(bottom_check, left_check))).any():
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return float("Inf")
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elif config.robot_type == RobotType.circle:
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if np.array(r <= config.robot_radius).any():
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return float("Inf")
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min_r = np.min(r)
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return 1.0 / min_r # OK
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def calc_to_goal_cost(trajectory, goal):
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"""
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calc to goal cost with angle difference
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"""
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dx = goal[0] - trajectory[-1, 0]
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dy = goal[1] - trajectory[-1, 1]
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error_angle = math.atan2(dy, dx)
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cost_angle = error_angle - trajectory[-1, 2]
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cost = abs(math.atan2(math.sin(cost_angle), math.cos(cost_angle)))
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return cost
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def plot_arrow(x, y, yaw, length=0.5, width=0.1): # pragma: no cover
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plt.arrow(x, y, length * math.cos(yaw), length * math.sin(yaw),
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head_length=width, head_width=width)
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plt.plot(x, y)
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def plot_robot(x, y, yaw, config): # pragma: no cover
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if config.robot_type == RobotType.rectangle:
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outline = np.array([[-config.robot_length / 2, config.robot_length / 2,
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(config.robot_length / 2), -config.robot_length / 2,
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-config.robot_length / 2],
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[config.robot_width / 2, config.robot_width / 2,
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- config.robot_width / 2, -config.robot_width / 2,
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config.robot_width / 2]])
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Rot1 = np.array([[math.cos(yaw), math.sin(yaw)],
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[-math.sin(yaw), math.cos(yaw)]])
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outline = (outline.T.dot(Rot1)).T
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outline[0, :] += x
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outline[1, :] += y
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plt.plot(np.array(outline[0, :]).flatten(),
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np.array(outline[1, :]).flatten(), "-k")
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elif config.robot_type == RobotType.circle:
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circle = plt.Circle((x, y), config.robot_radius, color="b")
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plt.gcf().gca().add_artist(circle)
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out_x, out_y = (np.array([x, y]) +
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np.array([np.cos(yaw), np.sin(yaw)]) * config.robot_radius)
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plt.plot([x, out_x], [y, out_y], "-k")
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def main(gx=10.0, gy=10.0, robot_type=RobotType.circle):
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print(__file__ + " start!!")
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# initial state [x(m), y(m), yaw(rad), v(m/s), omega(rad/s)]
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x = np.array([0.0, 0.0, math.pi / 8.0, 0.0, 0.0])
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# goal position [x(m), y(m)]
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goal = np.array([gx, gy])
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# input [forward speed, yaw_rate]
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config.robot_type = robot_type
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trajectory = np.array(x)
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ob = config.ob
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while True:
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u, predicted_trajectory = dwa_control(x, config, goal, ob)
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x = motion(x, u, config.dt) # simulate robot
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trajectory = np.vstack((trajectory, x)) # store state history
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if show_animation:
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plt.cla()
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# for stopping simulation with the esc key.
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plt.gcf().canvas.mpl_connect(
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'key_release_event',
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lambda event: [exit(0) if event.key == 'escape' else None])
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plt.plot(predicted_trajectory[:, 0], predicted_trajectory[:, 1], "-g")
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plt.plot(x[0], x[1], "xr")
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plt.plot(goal[0], goal[1], "xb")
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plt.plot(ob[:, 0], ob[:, 1], "ok")
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plot_robot(x[0], x[1], x[2], config)
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plot_arrow(x[0], x[1], x[2])
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plt.axis("equal")
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plt.grid(True)
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plt.pause(0.0001)
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# check reaching goal
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dist_to_goal = math.hypot(x[0] - goal[0], x[1] - goal[1])
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if dist_to_goal <= config.robot_radius:
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print("Goal!!")
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break
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print("Done")
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if show_animation:
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plt.plot(trajectory[:, 0], trajectory[:, 1], "-r")
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plt.pause(0.0001)
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plt.show()
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if __name__ == '__main__':
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main(robot_type=RobotType.rectangle)
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# main(robot_type=RobotType.circle)
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