Udacity_AI_for_Robotics_Exercise/L1_PSet_Localization_Program.py at main · aichase/Udacity_AI_for_Robotics_Exercise · GitHub

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# The function localize takes the following arguments:
#
# colors:
#        2D list, each entry either 'R' (for red cell) or 'G' (for green cell)
#
# measurements:
#        list of measurements taken by the robot, each entry either 'R' or 'G'
#
# motions:
#        list of actions taken by the robot, each entry of the form [dy,dx],
#        where dx refers to the change in the x-direction (positive meaning
#        movement to the right) and dy refers to the change in the y-direction
#        (positive meaning movement downward)
#        NOTE: the *first* coordinate is change in y; the *second* coordinate is
#              change in x
#
# sensor_right:
#        float between 0 and 1, giving the probability that any given
#        measurement is correct; the probability that the measurement is
#        incorrect is 1-sensor_right
#
# p_move:
#        float between 0 and 1, giving the probability that any given movement
#        command takes place; the probability that the movement command fails
#        (and the robot remains still) is 1-p_move; the robot will NOT overshoot
#        its destination in this exercise
#
# The function should RETURN (not just show or print) a 2D list (of the same
# dimensions as colors) that gives the probabilities that the robot occupies
# each cell in the world.
#
# Compute the probabilities by assuming the robot initially has a uniform
# probability of being in any cell.
#
# Also assume that at each step, the robot:
# 1) first makes a movement,
# 2) then takes a measurement.
#
# Motion:
#  [0,0] - stay
#  [0,1] - right
#  [0,-1] - left
#  [1,0] - down
#  [-1,0] - up

def sense(p, colors, measurement, sensor_right):
    for r in range(len(p)):
        for c in range(len(p[0])):
            if colors[r][c] == measurement:
                p[r][c] *= sensor_right
            else:
                p[r][c] *= 1 - sensor_right
    s = 0
    for i in p:
        s += sum(i)
    for r in range(len(p)):
        for c in range(len(p[0])):
            p[r][c] /= s
    return p

def move(p, motion, p_move):
    q = [[0 for row in range(len(p[0]))] for col in range(len(p))]
    for r in range(len(p)):
        for c in range(len(p[0])):
            q[(r + motion[0]) % len(p)][(c + motion[1]) % len(p[0])] += p[r][c] * p_move
            q[r][c] += p[r][c] * (1 - p_move)
    s = 0
    #for i in p:
    #    s += sum(i)
    #for r in range(len(p)):
    #    for c in range(len(p[0])):
    #        p[r][c] /= s
    return q

def localize(colors,measurements,motions,sensor_right,p_move):
    # initializes p to a uniform distribution over a grid of the same dimensions as colors
    pinit = 1.0 / float(len(colors)) / float(len(colors[0]))
    p = [[pinit for row in range(len(colors[0]))] for col in range(len(colors))]
    # >>> Insert your code here <<<
    for i in range(len(motions)):
        p = move(p, motions[i], p_move)
        p = sense(p, colors, measurements[i], sensor_right)
    return p

def show(p):
    rows = ['[' + ','.join(map(lambda x: '{0:.5f}'.format(x),r)) + ']' for r in p]
    print '[' + ',\n '.join(rows) + ']'

#############################################################
# For the following test case, your output should be
# [[0.01105, 0.02464, 0.06799, 0.04472, 0.02465],
#  [0.00715, 0.01017, 0.08696, 0.07988, 0.00935],
#  [0.00739, 0.00894, 0.11272, 0.35350, 0.04065],
#  [0.00910, 0.00715, 0.01434, 0.04313, 0.03642]]
# (within a tolerance of +/- 0.001 for each entry)

colors = [['R','G','G','R','R'],
          ['R','R','G','R','R'],
          ['R','R','G','G','R'],
          ['R','R','R','R','R']]
measurements = ['G','G','G','G','G']
motions = [[0,0],[0,1],[1,0],[1,0],[0,1]]
p = localize(colors,measurements,motions,sensor_right = 0.7, p_move = 0.8)
show(p) # displays your answer