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script.py
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147 lines (124 loc) · 5.98 KB
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from lib import rapinterface as ri, dynamic_window_approach as dw
import matplotlib.pyplot as plt
import numpy as np
import math
sensor_pos = [(0.10638160980,-0.1381410850,0.06811071560),(0.15552160980,-0.1202410850,0.06811871560),(0.19054160980,-0.0785010850,0.06811471560),(0.20916160980,-0.0272730850,0.06810771560),(0.20916160980,0.0272739150,0.06810371560),(0.19058160980,0.0831339150,0.06810771560),(0.15540160980,0.1250489150,0.06811171560),(0.10638160980,0.1381989150,0.06811071560),(-0.11031839020,0.1381989150,0.06817271560),(-0.15956839020,0.1202689150,0.06816971560),(-0.19458839020,0.0784989150,0.06816771560),(-0.21320839020,0.0272729150,0.06816671560),(-0.21320839020,-0.0272760850,0.06816671560),(-0.19457839020,-0.0785010850,0.06816771560),(-0.15956839020,-0.1202410850,0.06816971560),(-0.11031839020,-0.1381510850,0.06817271560)]
sensor_ori = [270,310, 330, 350, 10, 30, 50, 90, 90, 130, 150, 170, 190, 210, 230, 270]
param = {}
config = dw.Config()
def get_ob(x):
_c, _h, status, distances, _s = ri.getDistanceCollection()
ob = []
if len(status) == 0: # nenhum objeto encontrado
return np.array([[0,0]])
for i in range(16):
if status[2*i]!=0: # verifica se aquele sensor identificou um valor válido
distance = (distances[6*i + 0]**2 + # distância euclidiana
distances[6*i + 1]**2 + #do sensor ao ponto identificado
distances[6*i + 2]**2)**(1/2)
orientation = sensor_ori[i] + x[2] # obter o angulo real do sensor
position = [x[0] + sensor_pos[i][0] + distance*math.cos(orientation),
x[1] + sensor_pos[i][1] + distance*math.sin(orientation)]
# estimar a posição real do objeto
# somar posição real do robo,
# posição relativa do sensor com o robo
# distancia dividida em coordenadas cartesianas
ob.append(position)#adicionar obstaculo identificado no vetor de obstaculos
return np.array(ob)
#return ob
def rap_motion(u):
wr = (u[0] + u[1]*config.c)/config.r
wl = (u[0] - u[1]*config.c)/config.r
ri.setvelocity(param['motorRight'], wr)
ri.setvelocity(param['motorLeft'], wl)
#ri.setvelocity(param['motorRight'], 0)
#ri.setvelocity(param['motorLeft'], 0)
ri.sleep(config.dt)
position = ri.getposition(param['robot'],param['base'])
orientation = ri.getorientation(param['robot'],param['base'])
x = [0]*5
x[0],x[1],x[2] = position[0],position[1], orientation[2]
x[3], x[4] = u
return x
def init():
param['target'] = ri.getobject("Target")
param['base'] = ri.getobject("base")
param['motorLeft'] = ri.getobject("Pioneer_p3dx_leftMotor")
param['motorRight'] = ri.getobject("Pioneer_p3dx_rightMotor")
param['robot'] = ri.getobject("Pioneer_p3dx")
param['sonic'] = ri.getcollection("sonic")
for i in range(16):
sensor_ori[i] = sensor_ori[i]*math.pi/180
position = ri.getpositionb(param['robot'],param['base'])
orientation = ri.getorientationb(param['robot'],param['base'])
goal = ri.getpositionb(param['target'],param['base'])
robot_type = dw.RobotType.circle
# initial state [x(m), y(m), yaw(rad), v(m/s), omega(rad/s)]
x = np.array([position[0], position[1], orientation[2], 0.0, 0.0])
# goal position [x(m), y(m)]
goal = np.array([goal[0], goal[1]])
# input [forward speed, yaw_rate]
config.robot_type = robot_type
config.goal = goal
param['trajectory'] = np.array(x)
param['x'] = x
config.max_speed = 0.75 # [m/s]
config.min_speed = -0.30 # [m/s]
config.max_yaw_rate = 100.0 * math.pi / 180.0 # [rad/s]
config.max_accel = 0.3 # [m/ss]
config.max_delta_yaw_rate = 100.0 * math.pi / 180.0 # [rad/ss]
config.v_resolution = 0.01 # [m/s]
config.yaw_rate_resolution = 0.1 * math.pi / 180.0 # [rad/s]
config.dt = 0.1 # [s] Time tick for motion prediction
config.predict_time = 0.5 # [s]
config.to_goal_cost_gain = 0.15
config.speed_cost_gain = 1.0
config.obstacle_cost_gain = 1.0
config.robot_stuck_flag_cons = 0.001 # constant to prevent robot stucked
# config.robot_type = dw.RobotType.circle
# # Also used to check if goal is reached in both types
config.robot_radius = 0.30 # [m] for collision check
config.robot_type = dw.RobotType.rectangle
# Also used to check if goal is reached in both types
config.robot_width = 0.38 # [m] for collision check
config.robot_length = 0.52 # [m] for collision check
config.c = 0.19 # [m] distancia do centro às rodas
config.r = 0.185# [m] raio da roda
def loop():
x = param['x']
ob = get_ob(x)
u, predicted_trajectory = dw.dwa_control(x, config, config.goal, ob)
#x = dw.motion(x,u,config.dt)
x = rap_motion(u) # simulate robot
if(x[0] == 0):
return
param['x'] = x
param['trajectory'] = np.vstack((param['trajectory'], x)) # store state history
if dw.show_animation:
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.plot(predicted_trajectory[:, 0], predicted_trajectory[:, 1], "-g")
plt.plot(x[0], x[1], "xr")
plt.plot(config.goal[0], config.goal[1], "xb")
plt.plot(ob[:, 0], ob[:, 1], "ok")
dw.plot_robot(x[0], x[1], x[2], config)
dw.plot_arrow(x[0], x[1], x[2])
plt.axis("equal")
plt.grid(True)
plt.pause(0.01)
# check reaching goal
dist_to_goal = math.hypot(x[0] - config.goal[0], x[1] - config.goal[1])
if dist_to_goal <= config.robot_radius:
print("Goal!!")
rap_motion([0,0])
return True
def finnaly():
print("Done")
if dw.show_animation:
plt.plot(param['trajectory'][:, 0], param['trajectory'][:, 1], "-r")
plt.pause(0.001)
plt.show()
ri.start(init,loop,finnaly,30)