:sparkles

drive_between_arucos.py

@@ -13,7 +13,7 @@ DRIVE_T = 22 # 1 centimeter
 
 RIGHT_WHEEL_OFFSET = 4
 
-CLOCKWISE_OFFSET = 0.96
+CLOCKWISE_OFFSET = 0.92
 
 FOCAL_LENGTH = 1691
 
@@ -51,33 +51,32 @@ def find_arucos(arlo):
                     position = cv2.aruco.estimatePoseSingleMarkers(
                         np.array([aruco]), 14.5, CAMERA_MATRIX, DIST_COEF
                     )[1][0][0]
-                    position = np.array([position[0], position[1]])
+                    position = np.array([position[2], position[0]])
                     position[0] += 22.5
                     aruco_dict[id_] = position
 
             if len(aruco_dict) >= 2:
                 break
         arlo.go_diff(POWER, POWER, 1, 0)
-        sleep((20 * TURN_T)/1000)
+        sleep((20 * TURN_T * CLOCKWISE_OFFSET)/1000)
         arlo.stop()
         for key, value in aruco_dict.items():
-            print(rot, value)
             aruco_dict[key] = np.dot(rot, value)
 
-    return np.array(aruco_dict.values())[:2]
+    return np.array(list(aruco_dict.values())[:2])
 
 def main():
     arlo = Robot()
     aruco_positions = find_arucos(arlo)
-
     print(aruco_positions)
 
     position = [
         np.average(aruco_positions[:,0]),
-        np.average(aruco_positions[:,2])
+        np.average(aruco_positions[:,1])
     ]
+    print(position)
 
-    angle = np.rad2deg(np.arctan(position[0]/position[1]))
+    angle = np.rad2deg(np.arctan(position[1]/position[0]))
     drive_distance = np.sqrt(position[0]**2 + position[1]**2)
     if angle < 0:
         arlo.go_diff(POWER, POWER, 0, 1)

drive_to_aruco.py

@@ -52,6 +52,7 @@ def main():
 
     angle = np.rad2deg(np.arctan(position[0]/position[2]))
     drive_distance = np.sqrt(position[0]**2 + position[2]**2)
+    print(drive_distance)
     if angle < 0:
         arlo.go_diff(POWER, POWER, 0, 1)
         sleep((abs(angle) * TURN_T)/1000)
@@ -61,9 +62,9 @@ def main():
         sleep((abs(angle) * TURN_T * CLOCKWISE_OFFSET)/1000)
         arlo.stop()
 
-    arlo.go_diff(POWER, POWER + RIGHT_WHEEL_OFFSET, 1, 1)
-    sleep((drive_distance * DRIVE_T)/1000)
-    arlo.stop()
+    # arlo.go_diff(POWER, POWER + RIGHT_WHEEL_OFFSET, 1, 1)
+    # sleep((drive_distance * DRIVE_T)/1000)
+    # arlo.stop()
 
 
 if __name__ == "__main__":

occupancy_grids.py

@@ -0,0 +1,20 @@
+from matplotlib.backend_bases import NonGuiException
+import matplotlib.pyplot as plt
+import numpy as np
+from numpy.random import uniform 
+
+
+def Binary_Baye_Filter (I, zt, x):
+    pass
+
+def invserse_sensor_model(m, x, z):
+    pass
+
+
+def update(particles, weights, z, R, landmarks):
+    for i, landmark in enumerate(landmarks):
+        distance = np.linalg.norm(particles[:, 0:2] - landmark, axis=1)
+        weights *= np.scipy.stats.norm(distance, R).pdf(z[i])
+
+    weights += 1.e-300      # avoid round-off to zero
+    weights /= sum(weights) # normalize

rally.py

@@ -0,0 +1,154 @@
+import time
+
+import numpy as np
+import cv2
+
+import robot
+
+LANDMARKS = [1,2,3,4] # SET ARUCO NUMBERS
+
+POWER = 70
+
+TURN_T = 8.3 # 1 degree
+DRIVE_T = 22 # 1 centimeter
+
+RIGHT_WHEEL_OFFSET = 5
+
+CLOCKWISE_OFFSET = 0.8
+
+FOCAL_LENGTH = 1691
+
+CAMERA_MATRIX = np.array(
+    [[FOCAL_LENGTH,0,512],[0,FOCAL_LENGTH,360],[0,0,1]],
+    dtype=np.float32
+)
+DIST_COEF = np.array([0,0,0,0,0], dtype=np.float32)
+
+def find_aruco(image):
+    aruco_dict = cv2.aruco.Dictionary_get(cv2.aruco.DICT_6X6_250)
+    aruco_params = cv2.aruco.DetectorParameters_create()
+    corners, ids, _ = cv2.aruco.detectMarkers(
+        image,
+        aruco_dict,
+        parameters=aruco_params
+    )
+
+    if corners is None:
+        return []
+
+    return {ids[i][0]: box[0] for i, box in enumerate(corners)}
+
+
+def drunk_drive(drive_time, arlo, careful_range = 600):
+    start = time.time()
+
+    end = start + drive_time
+    turning = None
+    while time.time() < end:
+        forward_dist = arlo.read_front_ping_sensor()
+        right_dist = arlo.read_right_ping_sensor()
+        left_dist = arlo.read_left_ping_sensor()
+        if forward_dist > careful_range and all(x > 250 for x in [right_dist, left_dist]):
+            turning = None
+            arlo.go_diff(POWER, POWER + RIGHT_WHEEL_OFFSET, 1, 1)
+        else:
+            if turning == "R" or (forward_dist > careful_range and right_dist > 250 and turning is None):
+                arlo.go_diff(POWER, POWER, 1, 0)
+                turning = "R"
+            else:
+                arlo.go_diff(POWER, POWER + RIGHT_WHEEL_OFFSET, 0, 1)
+                turning = "L"
+
+        time.sleep(0.01)
+
+def careful_forward(drive_time, arlo, careful_range = 600):
+    start = time.time()
+    if drive_time > 0.8:
+        drive_time = 0.8
+        hit_something = True
+    else:
+        hit_something = False
+
+    end = start + drive_time
+    turning = None
+    while time.time() < end:
+        forward_dist = arlo.read_front_ping_sensor()
+        right_dist = arlo.read_right_ping_sensor()
+        left_dist = arlo.read_left_ping_sensor()
+        if forward_dist > careful_range and all(x > 250 for x in [right_dist, left_dist]):
+            turning = None
+            arlo.go_diff(POWER, POWER + RIGHT_WHEEL_OFFSET, 1, 1)
+        else:
+            hit_something = True
+            if turning == "R" or (forward_dist > careful_range and right_dist > 250 and turning is None):
+                arlo.go_diff(POWER, POWER, 1, 0)
+                turning = "R"
+            else:
+                arlo.go_diff(POWER, POWER + RIGHT_WHEEL_OFFSET, 0, 1)
+                turning = "L"
+
+        time.sleep(0.01)
+
+    return hit_something
+
+def main():
+    landmark_order = LANDMARKS + [
+        LANDMARKS[0]
+    ]
+
+    noah = robot.Robot()
+
+    while landmark_order != []:
+        landmark = landmark_order.pop(0)
+        print(f"looking for landmark {landmark}")
+
+        while True:
+            while True:
+                for _ in range(24):
+                    arucos = find_aruco(noah.take_photo())
+                    if landmark in arucos:
+                        break
+                    noah.go_diff(POWER, POWER, 0, 1)
+                    time.sleep((20 * TURN_T)/1000)
+                    noah.stop()
+
+                if landmark in arucos:
+                    break
+
+                drunk_drive(3, noah)
+
+
+            position = cv2.aruco.estimatePoseSingleMarkers(
+                np.array([arucos[landmark]]), 14.5, CAMERA_MATRIX, DIST_COEF
+            )[1][0][0]
+
+            angle = np.rad2deg(np.arctan(position[0]/position[2])) - 5
+            drive_distance = np.sqrt(position[0]**2 + position[2]**2) - 100
+
+            if angle < 0:
+                noah.go_diff(POWER, POWER, 0, 1)
+                time.sleep((abs(angle) * TURN_T)/1000)
+                noah.stop()
+            else:
+                noah.go_diff(POWER, POWER, 1, 0)
+                time.sleep((abs(angle) * TURN_T * CLOCKWISE_OFFSET)/1000)
+                noah.stop()
+
+            if not careful_forward((drive_distance * DRIVE_T)/1000, noah, 400):
+                arucos = find_aruco(noah.take_photo())
+                if landmark in arucos:
+                    position = cv2.aruco.estimatePoseSingleMarkers(
+                        np.array([arucos[landmark]]), 14.5, CAMERA_MATRIX, DIST_COEF
+                    )[1][0][0]
+                    drive_distance = np.sqrt(position[0]**2 + position[2]**2) - 25
+                    noah.go_diff(POWER, POWER, 1, 1)
+                    time.sleep((drive_distance * DRIVE_T)/1000)
+                    noah.stop()
+
+                    break
+
+
+if __name__ == "__main__":
+    start = time.time()
+    main()
+    print(f"Drove for {time.time() - start} seconds")

rally2.py

@@ -0,0 +1,314 @@
+import time
+from turtle import right
+import numpy as np
+import cv2
+
+from selflocalization import particle, camera
+
+import robot
+
+LANDMARKS = [6,8,9,7]
+
+LANDMARK_POSITIONS = {
+    6: [0, 0],
+    8: [300, 0],
+    9: [0, 400],
+    7: [300, 400]
+}
+
+POWER = 70
+
+TURN_T = 9.6  # 1 degree
+DRIVE_T = 22  # 1 centimeter
+
+RIGHT_WHEEL_OFFSET = 4
+
+CLOCKWISE_OFFSET = 0.96
+
+FOCAL_LENGTH = 1691
+
+CAMERA_MATRIX = np.array(
+    [[FOCAL_LENGTH, 0, 512], [0, FOCAL_LENGTH, 360], [0, 0, 1]],
+    dtype=np.float32
+)
+DIST_COEF = np.array([0, 0, 0, 0, 0], dtype=np.float32)
+
+SIGMA = 3
+SIGMA_THETA = 0.3
+
+NUM_PARTICLES = 1000
+
+
+def look_around(noah, particles, cam, est_pose):
+    for _ in range(24):
+        time.sleep(0.2)
+        # Fetch next frame
+        for _ in range(10):
+            colour = cam.get_next_frame()
+            landmark_values = []
+
+            # Detect objects
+            objectIDs, dists, angles = cam.detect_aruco_objects(colour)
+            if not isinstance(objectIDs, type(None)):
+
+                # List detected objects
+                for i in range(len(objectIDs)):
+                    print(
+                        "Object ID = ", objectIDs[i], ", Distance = ", dists[i], ", angle = ", angles[i])
+                    if objectIDs[i] in LANDMARKS:
+                        landmark_values.append((objectIDs[i], dists[i] + 22.5, angles[i]))
+
+                # Compute particle weights
+                for p in particles:
+                    calc_weight(p, landmark_values)
+
+                # Resampling
+                particles = particle.resample(particles)
+
+                # Draw detected objects
+                cam.draw_aruco_objects(colour)
+
+            else:
+                # No observation - reset weights to uniform distribution
+                for p in particles:
+                    p.setWeight(1.0/NUM_PARTICLES)
+
+            particle.add_uncertainty(particles, SIGMA, SIGMA_THETA)
+            # The estimate of the robots current pose
+            est_pose = particle.estimate_pose(particles)
+            calc_weight(est_pose, landmark_values)
+        noah.go_diff(POWER, POWER, 0, 1)
+        time.sleep((15 * TURN_T)/1000)
+        noah.stop()
+        for p in particles:
+            p.setTheta(p.theta - np.deg2rad(15))
+
+    # The estimate of the robots current pose
+    est_pose = particle.estimate_pose(particles)
+    return particles, est_pose
+
+
+def turn_towards_landmark(noah, particles, est_pose, landmark):
+    current_position = np.array([est_pose.x, est_pose.y])
+    current_theta = est_pose.theta
+    landmark_position = np.array(LANDMARK_POSITIONS[landmark])
+
+    relative_pos = landmark_position - current_position
+    angle = np.rad2deg(np.arctan(relative_pos[1]/relative_pos[0]))
+    turn_angle = np.mod(angle - (np.rad2deg(current_theta) - 180), 360)
+    for p in particles:
+        p.setTheta(p.theta - np.deg2rad(turn_angle))
+    if turn_angle < 180:
+        noah.go_diff(POWER, POWER, 0, 1)
+        time.sleep((abs(turn_angle) * TURN_T)/1000)
+        noah.stop()
+    else:
+        noah.go_diff(POWER, POWER, 1, 0)
+        time.sleep((abs(360 - turn_angle) * TURN_T * CLOCKWISE_OFFSET)/1000)
+        noah.stop()
+
+    particle.add_uncertainty(particles, SIGMA, SIGMA_THETA)
+    # The estimate of the robots current pose
+    est_pose = particle.estimate_pose(particles)
+    return particles, est_pose
+
+
+def time_to_landmark(est_pose, landmark):
+    """Kør indenfor 1 meter"""
+    current_position = np.array([est_pose.x, est_pose.y])
+    landmark_position = np.array(LANDMARK_POSITIONS[landmark])
+
+    relative_pos = landmark_position - current_position
+    drive_distance = np.sqrt(relative_pos[0]**2 + relative_pos[1]**2) - 100
+    return (DRIVE_T * drive_distance)/1000
+
+
+def drive_until_stopped(noah, particles):
+    noah.go_diff(POWER, POWER+RIGHT_WHEEL_OFFSET, 1, 1)
+    start = time.time()
+    while True:
+        forward_dist = noah.read_front_ping_sensor()
+        if forward_dist < 1000:
+            noah.stop()
+            break
+        left_dist = noah.read_left_ping_sensor()
+        if left_dist < 400:
+            noah.stop()
+            break
+        right_dist = noah.read_right_ping_sensor()
+        if right_dist < 400:
+            noah.stop()
+            break
+        if time.time() - start > 5:
+            noah.stop()
+            break
+        time.sleep(0.01)
+
+    end = time.time()
+    time_driven = end - start
+    distance_driven = (time_driven*1000)/DRIVE_T
+    for p in particles:
+        x = np.cos(p.theta) * distance_driven
+        y = np.sin(p.theta) * distance_driven
+        p.x = p.x + x
+        p.y = p.y + y
+
+    particle.add_uncertainty(particles, SIGMA, SIGMA_THETA)
+    est_pose = particle.estimate_pose(particles)
+
+    return time_driven, est_pose, particles
+
+
+def drunk_drive(noah):
+    start = time.time()
+    end = start + 2
+    turning = None
+    while time.time() < end:
+        forward_dist = noah.read_front_ping_sensor()
+        right_dist = noah.read_right_ping_sensor()
+        left_dist = noah.read_left_ping_sensor()
+        if forward_dist > 600 and all(x > 400 for x in [right_dist, left_dist]):
+            turning = None
+            noah.go_diff(POWER, POWER + RIGHT_WHEEL_OFFSET, 1, 1)
+        else:
+            if turning == "R" or (forward_dist > 600 and right_dist > 400 and turning is None):
+                noah.go_diff(POWER, POWER, 1, 0)
+                turning = "R"
+            else:
+                noah.go_diff(POWER, POWER + RIGHT_WHEEL_OFFSET, 0, 1)
+                turning = "L"
+
+        time.sleep(0.01)
+
+
+def inch_closer(noah, particles):
+    start = time.time()
+    noah.go_diff(POWER, POWER+RIGHT_WHEEL_OFFSET, 1, 1)
+    while True:
+        forward_dist = noah.read_front_ping_sensor()
+        if forward_dist < 300:
+            noah.stop()
+            break
+
+    end = time.time()
+    time_driven = end - start
+    distance_driven = (time_driven*1000)/DRIVE_T
+    for p in particles:
+        x = np.cos(p.theta) * distance_driven
+        y = np.sin(p.theta) * distance_driven
+        p.x = p.x + x
+        p.y = p.y + y
+
+    particle.add_uncertainty(particles, SIGMA, SIGMA_THETA)
+    est_pose = particle.estimate_pose(particles)
+    return est_pose, particles
+
+
+def initialize_particles(num_particles):
+    particles = []
+    for _ in range(num_particles):
+        # Random starting points.
+        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)
+        particles.append(p)
+
+    return particles
+
+
+def dist(particle, landmark):
+    return np.sqrt((landmark[0]-particle.x)**2+(landmark[1]-particle.y)**2)
+
+
+def calc_angle(particle, landmark, dist):
+    e_theta = np.array([np.cos(particle.theta), np.sin(particle.theta)]).T
+    e_landmark = (
+        np.array([landmark[0]-particle.x, landmark[1]-particle.y]).T)/dist
+    e_hat_theta = np.array([-np.sin(particle.theta), np.cos(particle.theta)]).T
+    return np.sign(np.dot(e_landmark, e_hat_theta)) * np.arccos(np.dot(e_landmark, e_theta))
+
+
+def normal(x, mean, std):
+    return (
+        (np.exp(-(((x - mean)**2)/(2 * std**2))))/(np.sqrt(2 * np.pi) * std)
+    )
+
+
+def calc_weight(particle, landmark_values):
+    if landmark_values == []:
+        return
+    weights = []
+    for values in landmark_values:
+        dist_to_landmark = dist(particle, LANDMARK_POSITIONS[values[0]])
+        dist_weight = normal(values[1], dist_to_landmark, SIGMA)
+
+        angle_to_landmark = calc_angle(
+            particle, LANDMARK_POSITIONS[values[0]], dist_to_landmark)
+        angle_weight = normal(values[2], angle_to_landmark, SIGMA_THETA)
+        weights.append(dist_weight * angle_weight)
+
+    particle.setWeight(np.product(weights))
+
+def turn_90_degrees(noah, est_pose, particles):
+    x_values = [i[0] for i in LANDMARK_POSITIONS.values()]
+    y_values = [i[1] for i in LANDMARK_POSITIONS.values()]
+    middle = np.array([max(x_values)/2, max(y_values)/2])
+
+    current_position = np.array([est_pose.x, est_pose.y])
+    relative_pos = middle - current_position
+    angle = np.arctan(relative_pos[1]/relative_pos[0])
+    relative_angle = np.mod(angle - est_pose.theta, 2.0*np.pi)
+
+    clockwise = relative_angle > 180
+
+    if clockwise:
+        noah.go_diff(POWER, POWER, 1, 0)
+        time.sleep((90 * TURN_T * CLOCKWISE_OFFSET)/1000)
+    else:
+        noah.go_diff(POWER, POWER, 0, 1)
+        time.sleep((90 * TURN_T)/1000)
+
+    noah.stop()
+
+    for p in particles:
+        if clockwise:
+            p.setTheta(p.theta - np.deg2rad(90))
+        else:
+            p.setTheta(p.theta + np.deg2rad(90))
+    particle.add_uncertainty(particles, SIGMA, SIGMA_THETA)
+    _, est_pose, particles = drive_until_stopped(noah, particles)
+    return est_pose, particles
+
+
+def main(noah):
+    landmark_order = LANDMARKS + [
+        LANDMARKS[0]
+    ]
+    cam = camera.Camera(0, 'arlo', useCaptureThread=True)
+    particles = initialize_particles(NUM_PARTICLES)
+
+    # The estimate of the robots current pose
+    est_pose = particle.estimate_pose(particles)
+
+    for landmark in landmark_order:
+        print(f"Going to landmark {landmark}")
+        while True:
+            particles, est_pose = look_around(noah, particles, cam, est_pose)
+            print(est_pose)
+            particles, est_pose = turn_towards_landmark(noah, particles, est_pose, landmark)
+            drive_time = time_to_landmark(est_pose, landmark)
+            time_driven, est_pose, particles = drive_until_stopped(noah, particles)
+            if not abs(time_driven - drive_time) < 0.5:
+                # drunk_drive(noah)
+                est_pose, particles = turn_90_degrees(noah, est_pose, particles)
+                continue
+
+            est_pose, particles = inch_closer(noah, particles)
+            break
+
+
+if __name__ == "__main__":
+    noah = robot.Robot()  # Noah er vores robots navn
+    try:
+        main(noah)
+    except KeyboardInterrupt:
+        noah.stop()

robot/__init__.py

@@ -1,2 +1 @@
 from .robot import Robot
-from .arlo import Arlo

selflocalization/camera.py

@@ -3,7 +3,7 @@ import numpy as np
 import time
 import sys
 import threading
-import selflocalization.framebuffer as framebuffer
+from selflocalization import framebuffer
 from pkg_resources import parse_version
 
 
@@ -18,6 +18,8 @@ except ImportError:
 
 def isRunningOnArlo():
     """Return True if we are running on Arlo, otherwise False."""
+    # TODO: Problematic that gstreamerCameraFound is first set after
+    #  instantiation of a Camera object
     return piCameraFound or gstreamerCameraFound
 
 # Black magic in order to handle differences in namespace names in OpenCV 2.4 and 3.0
@@ -98,31 +100,33 @@ class CaptureThread(threading.Thread):
 
 
 class Camera(object):
-    """This class is responsible for doing the image processing. It detects known landmarks and 
+    """This class is responsible for doing the image processing. It detects known landmarks and
     measures distances and orientations to these."""
     
-    def __init__(self, camidx, robottype='arlo', useCaptureThread = False):
+    def __init__(self, camidx, robottype='arlo', useCaptureThread=False):
         """Constructor:
              camidx - index of camera
              robottype - specify which robot you are using in order to use the correct camera calibration. 
                          Supported types: arlo, frindo, scribbler, macbookpro"""
 
+        print("robottype =", robottype)
         self.useCaptureThread = useCaptureThread
 
         # Set camera calibration info
         if robottype == 'arlo':
-            self.imageSize = (1280, 720)
+            focal_length = 1050
+            self.imageSize = (1024, 720)
             #self.intrinsic_matrix = np.asarray([ 7.1305391967046853e+02, 0., 3.1172820723774367e+02, 0.,
             #       7.0564929862291285e+02, 2.5634470978315028e+02, 0., 0., 1. ], dtype = np.float64)
             #self.intrinsic_matrix = np.asarray([ 6.0727040957659040e+02, 0., 3.0757300398967601e+02, 0.,
             #       6.0768864690145904e+02, 2.8935674612358201e+02, 0., 0., 1. ], dtype = np.float64)
-            self.intrinsic_matrix = np.asarray([500, 0., self.imageSize[0] / 2.0, 0.,
-                   500, self.imageSize[1] / 2.0, 0., 0., 1.], dtype = np.float64)
+            self.intrinsic_matrix = np.asarray([focal_length, 0., self.imageSize[0] / 2.0, 0.,
+                   focal_length, self.imageSize[1] / 2.0, 0., 0., 1.], dtype = np.float64)
             self.intrinsic_matrix.shape = (3, 3)
             #self.distortion_coeffs = np.asarray([ 1.1911006165076067e-01, -1.0003366233413549e+00,
             #       1.9287903277399834e-02, -2.3728201444308114e-03, -2.8137265581326476e-01 ], dtype = np.float64)
             self.distortion_coeffs = np.asarray([0., 0., 2.0546093607192093e-02, -3.5538453075048249e-03, 0.], dtype = np.float64)
-        if robottype == 'frindo':
+        elif robottype == 'frindo':
             self.imageSize = (640, 480)
             #self.intrinsic_matrix = np.asarray([ 7.1305391967046853e+02, 0., 3.1172820723774367e+02, 0.,
             #       7.0564929862291285e+02, 2.5634470978315028e+02, 0., 0., 1. ], dtype = np.float64)
@@ -457,9 +461,9 @@ class Camera(object):
 if (__name__=='__main__'):
     print("Opening and initializing camera")
     
-    cam = Camera(0, 'macbookpro', useCaptureThread = True)
+    #cam = Camera(0, 'macbookpro', useCaptureThread=True)
     #cam = Camera(0, 'macbookpro', useCaptureThread = False)
-    #cam = Camera(0, 'arlo', useCaptureThread = True)
+    cam = Camera(0, robottype='arlo', useCaptureThread=True)
     
     # Open a window
     WIN_RF1 = "Camera view"
@@ -488,12 +492,12 @@ if (__name__=='__main__'):
         #gray = cv2.convertScaleAbs(loggray)
         
         # Detect objects
-        objectType, distance, angle, colourProb = cam.get_object(colour)
-        if objectType != 'none':
-            print("Object type = ", objectType, ", distance = ", distance, ", angle = ", angle, ", colourProb = ", colourProb)
+        #objectType, distance, angle, colourProb = cam.get_object(colour)
+        #if objectType != 'none':
+        #    print("Object type = ", objectType, ", distance = ", distance, ", angle = ", angle, ", colourProb = ", colourProb)
 
         # Draw detected pattern
-        cam.draw_object(colour)
+        #cam.draw_object(colour)
 
         IDs, dists, angles = cam.detect_aruco_objects(colour)
         if not isinstance(IDs, type(None)):

selflocalization/particle.py

@@ -1,6 +1,6 @@
+import random
 import numpy as np
-import selflocalization.random_numbers as rn
-
+from selflocalization import random_numbers as rn
 
 class Particle(object):
     """Data structure for storing particle information (state and weight)"""
@@ -34,21 +34,26 @@ class Particle(object):
     def setWeight(self, val):
         self.weight = val
 
+    def copy(self):
+        return Particle(self.x, self.y, self.theta, self.weight)
+
+    def __str__(self) -> str:
+        return f"({self.x},{self.y},{self.theta})"
+
 
 def estimate_pose(particles_list):
     """Estimate the pose from particles by computing the average position and orientation over all particles. 
     This is not done using the particle weights, but just the sample distribution."""
-    x_sum = 0.0
-    y_sum = 0.0
-    cos_sum = 0.0
-    sin_sum = 0.0
-     
-    for particle in particles_list:
-        x_sum += particle.getX()
-        y_sum += particle.getY()
-        cos_sum += np.cos(particle.getTheta())
-        sin_sum += np.sin(particle.getTheta())
-        
+    x_values = [i.getX() for i in particles_list]
+    y_values = [i.getY() for i in particles_list]
+    cos_values = [np.cos(i.getTheta()) for i in particles_list]
+    sin_values = [np.sin(i.getTheta()) for i in particles_list]
+
+    x_sum = sum(x_values)
+    y_sum = sum(y_values)
+    cos_sum = sum(cos_values)
+    sin_sum = sum(sin_values)
+
     flen = len(particles_list)
     if flen != 0:
         x = x_sum / flen
@@ -58,13 +63,14 @@ def estimate_pose(particles_list):
         x = x_sum
         y = y_sum
         theta = 0.0
-        
     return Particle(x, y, theta)
      
      
 def move_particle(particle, delta_x, delta_y, delta_theta):
     """Move the particle by (delta_x, delta_y, delta_theta)"""
-    print("particle.py: move_particle not implemented. You should do this.") 
+    particle.setX(particle.getX() + delta_x)
+    particle.setY(particle.getY() + delta_y)
+    particle.setTheta(particle.getTheta() + delta_theta)
 
 
 def add_uncertainty(particles_list, sigma, sigma_theta):
@@ -83,3 +89,11 @@ def add_uncertainty_von_mises(particles_list, sigma, theta_kappa):
         particle.x += rn.randn(0.0, sigma)
         particle.y += rn.randn(0.0, sigma)
         particle.theta = np.mod(rn.rand_von_mises(particle.theta, theta_kappa), 2.0 * np.pi) - np.pi
+
+def resample(particle_list):
+    weights = [p.weight for p in particle_list]
+    if sum(weights) == 0:
+        weights = [1/len(particle_list) for _ in particle_list]
+    new_particles = random.choices(particle_list, weights, k=len(particle_list))
+    particle_list = [p.copy() for p in new_particles]
+    return particle_list

selflocalization/selflocalize.py

@@ -1,14 +1,14 @@
 import cv2
-import selflocalization.particle as particle
-import selflocalization.camera as camera
+import particle as particle
+import camera as camera
 import numpy as np
-import time
+from time import sleep
 from timeit import default_timer as timer
 import sys
 
 
 # Flags
-showGUI = True  # Whether or not to open GUI windows
+showGUI = False  # Whether or not to open GUI windows
 onRobot = True # Whether or not we are running on the Arlo robot
 
 
@@ -20,8 +20,7 @@ def isRunningOnArlo():
 
 
 if isRunningOnArlo():
-    # XXX: You need to change this path to point to where your robot.py file is located
-    sys.path.append("../../../../Arlo/python")
+    sys.path.append("..")
 
 
 try:
@@ -32,6 +31,14 @@ except ImportError:
     onRobot = False
 
 
+POWER = 70
+
+TURN_T = 7.9 # 1 degree
+DRIVE_T = 22 # 1 centimeter
+
+RIGHT_WHEEL_OFFSET = 4
+
+CLOCKWISE_OFFSET = 0.96
 
 
 # Some color constants in BGR format
@@ -46,19 +53,26 @@ CBLACK = (0, 0, 0)
 
 # Landmarks.
 # The robot knows the position of 2 landmarks. Their coordinates are in the unit centimeters [cm].
-landmarkIDs = [1, 2]
+landmarkIDs = [1, 5]
 landmarks = {
     1: (0.0, 0.0),  # Coordinates for landmark 1
-    2: (300.0, 0.0)  # Coordinates for landmark 2
+    5: (300.0, 0.0)  # Coordinates for landmark 2
 }
 landmark_colors = [CRED, CGREEN] # Colors used when drawing the landmarks
 
 
+SIGMA = 3
+SIGMA_THETA = 0.3
 
 
+def normal(x, mean, std):
+    return (
+        (np.exp(-(((x - mean)**2)/(2 * std**2))))/(np.sqrt(2 * np.pi) * std)
+    )
+
 
 def jet(x):
-    """Colour map for drawing particles. This function determines the colour of 
+    """Colour map for drawing particles. This function determines the colour of
     a particle from its weight."""
     r = (x >= 3.0/8.0 and x < 5.0/8.0) * (4.0 * x - 3.0/2.0) + (x >= 5.0/8.0 and x < 7.0/8.0) + (x >= 7.0/8.0) * (-4.0 * x + 9.0/2.0)
     g = (x >= 1.0/8.0 and x < 3.0/8.0) * (4.0 * x - 1.0/2.0) + (x >= 3.0/8.0 and x < 5.0/8.0) + (x >= 5.0/8.0 and x < 7.0/8.0) * (-4.0 * x + 7.0/2.0)
@@ -90,7 +104,7 @@ def draw_world(est_pose, particles, world):
         y = ymax - (int(particle.getY() + offsetY))
         colour = jet(particle.getWeight() / max_weight)
         cv2.circle(world, (x,y), 2, colour, 2)
-        b = (int(particle.getX() + 15.0*np.cos(particle.getTheta()))+offsetX, 
+        b = (int(particle.getX() + 15.0*np.cos(particle.getTheta()))+offsetX,
                                      ymax - (int(particle.getY() + 15.0*np.sin(particle.getTheta()))+offsetY))
         cv2.line(world, (x,y), b, colour, 2)
 
@@ -102,7 +116,7 @@ def draw_world(est_pose, particles, world):
 
     # Draw estimated robot pose
     a = (int(est_pose.getX())+offsetX, ymax-(int(est_pose.getY())+offsetY))
-    b = (int(est_pose.getX() + 15.0*np.cos(est_pose.getTheta()))+offsetX, 
+    b = (int(est_pose.getX() + 15.0*np.cos(est_pose.getTheta()))+offsetX,
                                  ymax-(int(est_pose.getY() + 15.0*np.sin(est_pose.getTheta()))+offsetY))
     cv2.circle(world, a, 5, CMAGENTA, 2)
     cv2.line(world, a, b, CMAGENTA, 2)
@@ -112,12 +126,61 @@ def draw_world(est_pose, particles, world):
 def initialize_particles(num_particles):
     particles = []
     for i in range(num_particles):
-        # Random starting points. 
+        # Random starting points.
         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)
         particles.append(p)
 
     return particles
 
+def dist(particle, landmark):
+    return np.sqrt((landmark[0]-particle.x)**2+(landmark[1]-particle.y)**2)
+
+def calc_angle(particle, landmark, dist):
+    e_theta = np.array([np.cos(particle.theta), np.sin(particle.theta)]).T
+    e_landmark = (np.array([landmark[0]-particle.x, landmark[1]-particle.y]).T)/dist
+    e_hat_theta = np.array([-np.sin(particle.theta), np.cos(particle.theta)]).T
+    return np.sign(np.dot(e_landmark, e_hat_theta)) * np.arccos(np.dot(e_landmark, e_theta))
+
+def calc_weight(particle, landmark_values):
+    if landmark_values == []:
+        return
+    weights = []
+    for values in landmark_values:
+        dist_to_landmark = dist(particle, landmarks[values[0]])
+        dist_weight = normal(values[1], dist_to_landmark, SIGMA)
+
+        angle_to_landmark = calc_angle(particle, landmarks[values[0]], dist_to_landmark)
+        angle_weight = normal(values[2], angle_to_landmark, SIGMA_THETA)
+        weights.append(dist_weight * angle_weight)
+
+    particle.setWeight(np.product(weights))
+
+def drive_towards_middle(est_pose, arlo):
+    middle = np.array([150, 0])
+
+    position = np.array([est_pose.x, est_pose.y])
+    theta = est_pose.theta
+    print(np.rad2deg(theta))
+
+    relative_pos = middle - position
+    print("relative_pos", relative_pos)
+    angle = np.rad2deg(np.arctan(relative_pos[1]/relative_pos[0]))
+    print(angle)
+    turn_angle = np.mod(angle - (np.rad2deg(theta) - 180), 360)
+    print(turn_angle)
+    drive_distance = np.sqrt(position[0]**2 + position[1]**2)
+    if turn_angle < 180:
+        arlo.go_diff(POWER, POWER, 0, 1)
+        sleep((abs(turn_angle) * TURN_T)/1000)
+        arlo.stop()
+    else:
+        arlo.go_diff(POWER, POWER, 1, 0)
+        sleep((abs(360 - turn_angle) * TURN_T * CLOCKWISE_OFFSET)/1000)
+        arlo.stop()
+
+    arlo.go_diff(POWER, POWER + RIGHT_WHEEL_OFFSET, 1, 1)
+    sleep((drive_distance * DRIVE_T)/1000)
+    arlo.stop()
 
 # Main program #
 try:
@@ -133,7 +196,7 @@ try:
 
 
     # Initialize particles
-    num_particles = 1000
+    num_particles = 10000
     particles = initialize_particles(num_particles)
 
     est_pose = particle.estimate_pose(particles) # The estimate of the robots current pose
@@ -142,7 +205,7 @@ try:
     velocity = 0.0 # cm/sec
     angular_velocity = 0.0 # radians/sec
 
-    # Initialize the robot (XXX: You do this)
+    arlo = robot.Robot()
 
     # Allocate space for world map
     world = np.zeros((500,500,3), dtype=np.uint8)
@@ -156,75 +219,71 @@ try:
     else:
         cam = camera.Camera(0, 'macbookpro', useCaptureThread = True)
 
+
     while True:
 
-        # Move the robot according to user input (only for testing)
-        action = cv2.waitKey(10)
-        if action == ord('q'): # Quit
-            break
-    
-        if not isRunningOnArlo():
-            if action == ord('w'): # Forward
-                velocity += 4.0
-            elif action == ord('x'): # Backwards
-                velocity -= 4.0
-            elif action == ord('s'): # Stop
-                velocity = 0.0
-                angular_velocity = 0.0
-            elif action == ord('a'): # Left
-                angular_velocity += 0.2
-            elif action == ord('d'): # Right
-                angular_velocity -= 0.2
-
-
-
-        
-        # Use motor controls to update particles
-        # XXX: Make the robot drive
-        # XXX: You do this
-
-
         # Fetch next frame
         colour = cam.get_next_frame()
-        
+        landmark_values = []
+
         # Detect objects
         objectIDs, dists, angles = cam.detect_aruco_objects(colour)
         if not isinstance(objectIDs, type(None)):
+
             # List detected objects
             for i in range(len(objectIDs)):
                 print("Object ID = ", objectIDs[i], ", Distance = ", dists[i], ", angle = ", angles[i])
-                # XXX: Do something for each detected object - remember, the same ID may appear several times
+                if objectIDs[i] in landmarkIDs:
+                    landmark_values.append((objectIDs[i], dists[i], angles[i]))
 
             # Compute particle weights
-            # XXX: You do this
+            for p in particles:
+                calc_weight(p, landmark_values)
 
             # Resampling
-            # XXX: You do this
+            particles = particle.resample(particles)
 
             # Draw detected objects
             cam.draw_aruco_objects(colour)
+
         else:
             # No observation - reset weights to uniform distribution
             for p in particles:
                 p.setWeight(1.0/num_particles)
 
-    
+        particle.add_uncertainty(particles, SIGMA, SIGMA_THETA)
         est_pose = particle.estimate_pose(particles) # The estimate of the robots current pose
+        calc_weight(est_pose, landmark_values)
+        est_weight = est_pose.weight * 10**5
+        print("est_weight:", est_weight)
+        if est_weight > 1:
+            draw_world(est_pose, particles, world)
+            cv2.imwrite("test.png", world)
+            break
+        # else:
+        #     arlo.go_diff(POWER, POWER, 0, 1)
+        #     sleep((15 * TURN_T)/1000)
+        #     arlo.stop()
+        #     for p in particles:
+        #         p.setTheta(p.theta + np.deg2rad(15))
 
         if showGUI:
             # Draw map
             draw_world(est_pose, particles, world)
-    
+
             # Show frame
             cv2.imshow(WIN_RF1, colour)
 
             # Show world
             cv2.imshow(WIN_World, world)
-    
-  
-finally: 
+
+    print(est_pose.x, est_pose.y, est_pose.theta)
+    drive_towards_middle(est_pose, arlo)
+
+
+finally:
     # Make sure to clean up even if an exception occurred
-    
+
     # Close all windows
     cv2.destroyAllWindows()