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import matplotlib.pyplot as plt
import matplotlib.cm as cm
# Simulator class:
# Responsible for managing the simulation of the robots in the environment
def __init__(self, robots, dynamics_models, env, policy, settings):
"""
robots: list of Robot objects
dynamics_models: list of DynamicsModel objects
circle_obstacles: list of tuples (x,y,radius)
rectangle_obstacles: list of tuples (x,y,width,height)
policy: The policy that gives us the controls for each robot at a given time
state: the current state of the world. This is a list of tuples, where each tuple is the state of a robot
time: the current time
"""
self.robots = robots
self.circ_obstacles = env.circle_obs
self.rect_obstacles = env.rect_obs
self.policy = policy
self.state = [robot.current_position for robot in robots]
self.num_robots = len(robots)
self.dynamics_models = dynamics_models
self.time = 0
self.scaling_factor = settings['simulator']['scaling_factor']
# Helper variables to keep track of the sim
self.sim_time = 0
self.x_history = [ [] for _ in range(self.num_robots) ]
self.y_history = [ [] for _ in range(self.num_robots) ]
self.h_history = [ [] for _ in range(self.num_robots) ]
self.optimized_trajectories_hist = [ [] for _ in range(self.num_robots) ]
self.optimized_trajectory = None
def all_robots_at_goal(self):
for i in range(self.num_robots):
if (np.sqrt((self.state[i][0] - self.robots[i].goal[0]) ** 2 + (self.state[i][1] - self.robots[i].goal[1]) ** 2) > .5):
return False
return True
def advance(self, screen, dt):
"""
Advance the simulation by dt seconds
"""
# Get the controls from the policy
x_mpc, controls = self.policy.advance(screen, self.state)
# # Update the state of each robot
# for i in range(self.num_robots):
# new_state = self.dynamics_models[i].next_state(self.state[i], controls[i], dt)
# self.robots[i].current_position = new_state
# self.state[i] = new_state
rachelmoan
committed
# Update the time
self.time += dt
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return x_mpc, controls
def run(self, show_plots=False):
"""
Run the path tracker algorithm.
Parameters:
- show_plots (bool): Flag indicating whether to show plots during the simulation. Default is False.
Returns:
- numpy.ndarray: Array containing the history of x, y, and h coordinates.
"""
# Add the initial state to the histories
self.state = np.array(self.state)
for i in range(self.num_robots):
self.x_history[i].append(self.state[i, 0])
self.y_history[i].append(self.state[i, 1])
self.h_history[i].append(self.state[i, 2])
# if show_plots: self.plot_sim()
self.plot_current_world_state()
while 1:
# check if all robots have reached their goal
if self.all_robots_at_goal():
print("Success! Goal Reached")
return np.asarray([self.x_history, self.y_history, self.h_history])
# plot the current state of the robots
self.plot_current_world_state()
# get the next control for all robots
x_mpc, controls = self.advance(self.state, self.policy.DT)
next_states = []
for i in range(self.num_robots):
next_states.append(self.policy.dynamics.next_state(self.state[i], controls[i], self.policy.DT))
self.state = next_states
self.state = np.array(self.state)
for i in range(self.num_robots):
self.x_history[i].append(self.state[i, 0])
self.y_history[i].append(self.state[i, 1])
self.h_history[i].append(self.state[i, 2])
# use the optimizer output to preview the predicted state trajectory
# self.optimized_trajectory = self.ego_to_global(x_mpc.value)
# if show_plots: self.optimized_trajectory = self.ego_to_global_roomba(x_mpc)
# if show_plots: self.plot_sim()
def plot_current_world_state(self):
"""
Plot the current state of the world.
"""
colors = cm.rainbow(np.linspace(0, 1, self.num_robots))
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circle1 = plt.Circle((obs[0], obs[1]), obs[2], color='k', fill=True)
plt.gca().add_artist(circle1)
# Plot the current state of each robot using the most recent values from
# x_history, y_history, and h_history
for i in range(self.num_robots):
self.plot_roomba(self.x_history[i][-1], self.y_history[i][-1], self.h_history[i][-1], colors[i], False, self.policy.radius)
x, y, theta = self.policy.paths[i][:, -1]
plt.plot(x, y, 'o', color=colors[i])
circle1 = plt.Circle((x, y), self.policy.radius, color=colors[i], fill=False)
plt.gca().add_artist(circle1)
# plot the ref path of each robot
for i in range(self.num_robots):
plt.plot(self.policy.paths[i][0, :], self.policy.paths[i][1, :], '--', color=colors[i])
x_range = self.env.boundary[0]
y_range = self.env.boundary[1]
plt.xlim(x_range[0], x_range[1])
plt.ylim(y_range[0], y_range[1])
# force equal aspect ratio
plt.gca().set_aspect('equal', adjustable='box')
plt.tight_layout()
plt.show()
# plt.draw()
# plt.pause(0.1)
# plt.clf()
def plot_roomba(self, x, y, yaw, color, fill, radius):
"""
Args:
x ():
y ():
yaw ():
"""
ax = plt.gca()
if fill: alpha = .3
else: alpha = 1
circle = plt.Circle((x, y), radius, color=color, fill=fill, alpha=alpha)
ax.add_patch(circle)
# Plot direction marker
dx = 1 * np.cos(yaw)
dy = 1 * np.sin(yaw)
ax.arrow(x, y, dx, dy, head_width=0.1, head_length=0.1, fc='r', ec='r')