Multiple Movements
Let’s see how our robot responds to moving multiple times without sensing!
<img src=’images/uncertain_motion.png’ width=50% height=50% />
First let’s include our usual resource imports and display function.
# importing resources
import matplotlib.pyplot as plt
import numpy as np
A helper function for visualizing a distribution.
def display_map(grid, bar_width=1):
if(len(grid) > 0):
x_labels = range(len(grid))
plt.bar(x_labels, height=grid, width=bar_width, color='b')
plt.xlabel('Grid Cell')
plt.ylabel('Probability')
plt.ylim(0, 1) # range of 0-1 for probability values
plt.title('Probability of the robot being at each cell in the grid')
plt.xticks(np.arange(min(x_labels), max(x_labels)+1, 1))
plt.show()
else:
print('Grid is empty')
QUIZ: Write code that moves 1000 times and then prints the resulting probability distribution.
You are given the initial variables and a complete move function (that incorporates uncertainty), below.
# given initial variables
p=[0, 1, 0, 0, 0]
# the color of each grid cell in the 1D world
world=['green', 'red', 'red', 'green', 'green']
# Z, the sensor reading ('red' or 'green')
Z = 'red'
pHit = 0.6
pMiss = 0.2
pExact = 0.8
pOvershoot = 0.1
pUndershoot = 0.1
# Complete the move function
def move(p, U):
q=[]
# iterate through all values in p
for i in range(len(p)):
# use the modulo operator to find the new location for a p value
# this finds an index that is shifted by the correct amount
index = (i-U) % len(p)
nextIndex = (index+1) % len(p)
prevIndex = (index-1) % len(p)
s = pExact * p[index]
s = s + pOvershoot * p[nextIndex]
s = s + pUndershoot * p[prevIndex]
# append the correct, modified value of p to q
q.append(s)
return q
# Here is code for moving twice
p = move(p, 1)
p = move(p, 1)
print(p)
display_map(p)
## TODO: Write code for moving 1000 times
