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72
rally2.py
72
rally2.py
@ -1,4 +1,5 @@
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import time
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from turtle import right
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import numpy as np
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import cv2
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@ -6,19 +7,19 @@ from selflocalization import particle, camera
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import robot
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LANDMARKS = [7,11,10,6]
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LANDMARKS = [7, 11, 10, 6]
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LANDMARK_POSITIONS = {
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7: [0,0],
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11: [300,0],
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10: [0,400],
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6: [300,400]
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7: [0, 0],
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11: [300, 0],
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10: [0, 400],
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6: [300, 400]
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}
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POWER = 70
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TURN_T = 7.9 # 1 degree
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DRIVE_T = 22 # 1 centimeter
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TURN_T = 7.9 # 1 degree
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DRIVE_T = 22 # 1 centimeter
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RIGHT_WHEEL_OFFSET = 4
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@ -27,16 +28,17 @@ CLOCKWISE_OFFSET = 0.96
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FOCAL_LENGTH = 1691
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CAMERA_MATRIX = np.array(
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[[FOCAL_LENGTH,0,512],[0,FOCAL_LENGTH,360],[0,0,1]],
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[[FOCAL_LENGTH, 0, 512], [0, FOCAL_LENGTH, 360], [0, 0, 1]],
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dtype=np.float32
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)
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DIST_COEF = np.array([0,0,0,0,0], dtype=np.float32)
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DIST_COEF = np.array([0, 0, 0, 0, 0], dtype=np.float32)
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SIGMA = 3
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SIGMA_THETA = 0.3
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NUM_PARTICLES = 1000
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def look_around(noah, particles, cam, est_pose):
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for _ in range(24):
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# Fetch next frame
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@ -49,7 +51,8 @@ def look_around(noah, particles, cam, est_pose):
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# List detected objects
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for i in range(len(objectIDs)):
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print("Object ID = ", objectIDs[i], ", Distance = ", dists[i], ", angle = ", angles[i])
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print(
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"Object ID = ", objectIDs[i], ", Distance = ", dists[i], ", angle = ", angles[i])
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if objectIDs[i] in LANDMARKS:
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landmark_values.append((objectIDs[i], dists[i], angles[i]))
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@ -69,7 +72,8 @@ def look_around(noah, particles, cam, est_pose):
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p.setWeight(1.0/NUM_PARTICLES)
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particle.add_uncertainty(particles, SIGMA, SIGMA_THETA)
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est_pose = particle.estimate_pose(particles) # The estimate of the robots current pose
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# The estimate of the robots current pose
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est_pose = particle.estimate_pose(particles)
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calc_weight(est_pose, landmark_values)
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noah.go_diff(POWER, POWER, 0, 1)
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time.sleep((15 * TURN_T)/1000)
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@ -77,9 +81,11 @@ def look_around(noah, particles, cam, est_pose):
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for p in particles:
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p.setTheta(p.theta + np.deg2rad(15))
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est_pose = particle.estimate_pose(particles) # The estimate of the robots current pose
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# The estimate of the robots current pose
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est_pose = particle.estimate_pose(particles)
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return particles, est_pose
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def turn_towards_landmark(noah, particles, est_pose, landmark):
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current_position = np.array([est_pose.x, est_pose.y])
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current_theta = est_pose.theta
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@ -99,10 +105,11 @@ def turn_towards_landmark(noah, particles, est_pose, landmark):
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time.sleep((abs(360 - turn_angle) * TURN_T * CLOCKWISE_OFFSET)/1000)
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noah.stop()
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est_pose = particle.estimate_pose(particles) # The estimate of the robots current pose
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# The estimate of the robots current pose
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est_pose = particle.estimate_pose(particles)
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return particles, est_pose
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def time_to_landmark(est_pose, landmark):
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"""Kør indenfor 1 meter"""
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current_position = np.array([est_pose.x, est_pose.y])
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@ -112,19 +119,29 @@ def time_to_landmark(est_pose, landmark):
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drive_distance = np.sqrt(relative_pos[0]**2 + relative_pos[1]**2) - 100
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return (DRIVE_T * drive_distance)/1000
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def drive_until_stopped(noah):
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noah.go_diff(POWER, POWER+RIGHT_WHEEL_OFFSET, 1, 1)
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start = time.time()
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while True:
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# TODO blind vinkel
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forward_dist = noah.read_front_sensor()
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if forward_dist < 1000:
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noah.stop()
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break
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left_dist = noah.read_left_sensor()
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if left_dist < 400:
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noah.stop()
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break
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right_dist = noah.read_right_sensor()
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if right_dist < 400:
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noah.stop()
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break
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time.sleep(0.01)
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end = time.time()
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return end - start
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def drunk_drive(noah):
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start = time.time()
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end = start + 2
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@ -146,6 +163,7 @@ def drunk_drive(noah):
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time.sleep(0.01)
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def inch_closer(noah):
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noah.go_diff(POWER, POWER+RIGHT_WHEEL_OFFSET, 1, 1)
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while True:
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@ -154,21 +172,26 @@ def inch_closer(noah):
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noah.stop()
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break
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def initialize_particles(num_particles):
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particles = []
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for _ in range(num_particles):
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# Random starting points.
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p = particle.Particle(600.0*np.random.ranf() - 100.0, 600.0*np.random.ranf() - 250.0, np.mod(2.0*np.pi*np.random.ranf(), 2.0*np.pi), 1.0/num_particles)
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p = particle.Particle(600.0*np.random.ranf() - 100.0, 600.0*np.random.ranf(
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) - 250.0, np.mod(2.0*np.pi*np.random.ranf(), 2.0*np.pi), 1.0/num_particles)
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particles.append(p)
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return particles
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def dist(particle, landmark):
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return np.sqrt((landmark[0]-particle.x)**2+(landmark[1]-particle.y)**2)
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def calc_angle(particle, landmark, dist):
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e_theta = np.array([np.cos(particle.theta), np.sin(particle.theta)]).T
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e_landmark = (np.array([landmark[0]-particle.x, landmark[1]-particle.y]).T)/dist
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e_landmark = (
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np.array([landmark[0]-particle.x, landmark[1]-particle.y]).T)/dist
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e_hat_theta = np.array([-np.sin(particle.theta), np.cos(particle.theta)]).T
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return np.sign(np.dot(e_landmark, e_hat_theta)) * np.arccos(np.dot(e_landmark, e_theta))
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@ -178,6 +201,7 @@ def normal(x, mean, std):
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(np.exp(-(((x - mean)**2)/(2 * std**2))))/(np.sqrt(2 * np.pi) * std)
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)
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def calc_weight(particle, landmark_values):
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if landmark_values == []:
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return
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@ -186,12 +210,14 @@ def calc_weight(particle, landmark_values):
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dist_to_landmark = dist(particle, LANDMARK_POSITIONS[values[0]])
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dist_weight = normal(values[1], dist_to_landmark, SIGMA)
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angle_to_landmark = calc_angle(particle, LANDMARK_POSITIONS[values[0]], dist_to_landmark)
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angle_to_landmark = calc_angle(
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particle, LANDMARK_POSITIONS[values[0]], dist_to_landmark)
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angle_weight = normal(values[2], angle_to_landmark, SIGMA_THETA)
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weights.append(dist_weight * angle_weight)
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particle.setWeight(np.product(weights))
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def main():
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landmark_order = LANDMARKS + [
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LANDMARKS[0]
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@ -199,15 +225,17 @@ def main():
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particles = initialize_particles(NUM_PARTICLES)
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est_pose = particle.estimate_pose(particles) # The estimate of the robots current pose
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# The estimate of the robots current pose
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est_pose = particle.estimate_pose(particles)
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noah = robot.Robot() # Noah er vores robots navn
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cam = camera.Camera(0, 'arlo', useCaptureThread = True)
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noah = robot.Robot() # Noah er vores robots navn
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cam = camera.Camera(0, 'arlo', useCaptureThread=True)
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for landmark in landmark_order:
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while True:
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particles, est_pose = look_around(noah, particles, cam, est_pose)
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particles, est_pose = turn_towards_landmark(noah, particles, est_pose, landmark)
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particles, est_pose = turn_towards_landmark(
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noah, particles, est_pose, landmark)
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drive_time = time_to_landmark(est_pose, landmark)
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if not abs(drive_until_stopped(noah) - drive_time) < 0.5:
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drunk_drive(noah)
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