In [1]:
%matplotlib inline
import numpy as np
from copy import copy
import math, random
import matplotlib.pyplot as plt                   #   for plotting data
from matplotlib.patches import Ellipse      #  for drawing

actual_landmarks = [np.array([-0.5,0.0]),np.array([0.5,0.0]),np.array([0.0,0.5])]

def draw_landmarks(landmarks):
xs = [ e[0] for e in actual_landmarks ]
ys = [ e[1] for e in actual_landmarks ]

plt.scatter(xs,ys,s=300,marker="*",label="landmarks",color="orange")

draw_landmarks(actual_landmarks)

In [2]:
actual_x = np.array([0.2,0.3,math.pi*200.0/180])   #ロボットの実際の姿勢

def draw_robot(pose):
plt.quiver([pose[0]],[pose[1]],[math.cos(pose[2])],[math.sin(pose[2])],color="red",label="actual robot motion")

In [3]:
draw_robot(actual_x)
draw_landmarks(actual_landmarks)

In [4]:
def relative_landmark_pos(pose,landmark):
x,y,theta = pose
lx,ly = landmark

distance = math.sqrt((lx - x)**2 + (ly - y)**2)
direction = math.atan2(ly - y, lx - x) - theta

return distance, direction, lx, ly

In [5]:
measurements = [ relative_landmark_pos(actual_x, ln) for ln in actual_landmarks ]

In [6]:
print(measurements)

[(0.7615773105863908, -6.2273593712933693, -0.5, 0.0), (0.4242640687119285, -4.2760566673861078, 0.5, 0.0), (0.28284271247461906, -1.1344640137963142, 0.0, 0.5)]

In [7]:
def draw_observation(pose, measurement):
x,y,theta = pose
mdistance, mdirection,tmp_x,tmp_y = measurement

lx = x + mdistance * math.cos(theta + mdirection)
ly = y + mdistance * math.sin(theta + mdirection)

plt.plot([pose[0],  lx],[pose[1],  ly],color="pink")

def draw_observations(pose, measurements):
for m in measurements:
draw_observation(pose,m)

In [8]:
draw_robot(actual_x)
draw_landmarks(actual_landmarks)
draw_observations(actual_x, measurements)

In [30]:
def observation(pose, landmark):

mdirection = random.gauss(adirection, 5.0/180.0 * math.pi)

if math.cos(mdirection) < 0.0:
return None

return mdistance, mdirection, alx, aly

def observations(pose,landmarks):
return filter(lambda x: x != None, [ observation(pose, e) for e in landmarks ])

In [31]:
measurements = observations(actual_x, actual_landmarks)

In [32]:
draw_robot(actual_x)
draw_landmarks(actual_landmarks)
draw_observations(actual_x, measurements)

In [33]:
### クラスや関数のコピペ ###

class Particle:
def __init__(self,w):
self.pose = np.array([0.0,0.0,0.0])
self.weight = w

def __repr__(self):
return "pose: " + str(self.pose) + " weight: " + str(self.weight)

def f(x_old,u):
pos_x, pos_y, pos_theta = x_old
act_fw, act_rot = u

act_fw = random.gauss(act_fw,act_fw/10)
dir_error = random.gauss(0.0, math.pi / 180.0 * 3.0)
act_rot = random.gauss(act_rot,act_rot/10)

pos_x += act_fw * math.cos(pos_theta + dir_error)
pos_y += act_fw * math.sin(pos_theta + dir_error)
pos_theta += act_rot

return np.array([pos_x,pos_y,pos_theta])

### 描画関数は少し変更を ###
def draw(pose,particles):
fig = plt.figure(i,figsize=(8, 8))
sp = fig.add_subplot(111, aspect='equal')
sp.set_xlim(-1.0,1.0)
sp.set_ylim(-0.5,1.5)

xs = [e.pose[0] for e in particles]
ys = [e.pose[1] for e in particles]
vxs = [math.cos(e.pose[2])*e.weight for e in particles]  #重みで長さを変えるようにしましょう
vys = [math.sin(e.pose[2])*e.weight for e in particles]  #重みで長さを変えるようにしましょう
plt.quiver(xs,ys,vxs,vys,color="blue",label="particles")

plt.quiver([pose[0]],[pose[1]],[math.cos(pose[2])],[math.sin(pose[2])],color="red",label="actual robot motion")

In [34]:
### 行動もコピペベースで。ランドマークの観測と描画を入れる ###

actual_x = np.array([0.0,0.0,0.0])   #ロボットの実際の姿勢
particles = [Particle(1.0/100) for i in range(100)]
u = np.array([0.2,math.pi / 180.0 * 20]) #ロボットの移動

import copy

path = [actual_x]
particle_path = [copy.deepcopy(particles)]
measurementss = [observations(actual_x, actual_landmarks)]
for i in range(10):
actual_x = f(actual_x,u)
path.append(actual_x)
measurementss.append(observations(actual_x,actual_landmarks))

for p in particles:
p.pose = f(p.pose,u)
particle_path.append(copy.deepcopy(particles))

for i,p in enumerate(path):
draw(path[i],particle_path[i])
draw_landmarks(actual_landmarks)
draw_observations(path[i],measurementss[i])