我正在学习Udacity的机器人人工智能课程,遇到了一些令人费解的事情。这门课程已经为我们编写了一个机器人类(我对这段代码没有学分)
有一个set
方法用于设置x,y&;机器人的方向是类中的参数。机器人在2D世界中的(30,50,pi/2)
初始化,在(-pi/2, 15)
和(-pi/2, 10)
两次移动后,感知功能应估计[32.0156, 53.1507, 47.1699, 40.3112]
到地标的距离。在底部的驱动程序代码中,我发现我的答案有所不同,这取决于我是否在.set()
方法之后将类实例设置为等于自身。我不明白为什么这很重要。我认为.set()
方法将更新该类变量的实例。如果没有等号,我得到[31.6227, 58.3095, 31.6227, 58.3095]
的感觉估计
我说的驱动程序代码是:
myrobot = robot()
myrobot.set(30, 50, pi/2)
myrobot = myrobot.move(-(pi/2), 15)
print(myrobot.sense())
myrobot = myrobot.move(-(pi/2), 10)
print(myrobot.sense())
对
myrobot = robot()
myrobot.set(30, 50, pi/2)
myrobot.move(-(pi/2), 15)
print(myrobot.sense())
myrobot.move(-(pi/2), 10)
print(myrobot.sense())
机器人等级:
# Make a robot called myrobot that starts at
# coordinates 30, 50 heading north (pi/2).
# Have your robot turn clockwise by pi/2, move
# 15 m, and sense. Then have it turn clockwise
# by pi/2 again, move 10 m, and sense again.
#
# Your program should print out the result of
# your two sense measurements.
#
# Don't modify the code below. Please enter
# your code at the bottom.
from math import *
import random
landmarks = [[20.0, 20.0], [80.0, 80.0], [20.0, 80.0], [80.0, 20.0]]
world_size = 100.0
class robot:
def __init__(self):
self.x = random.random() * world_size
self.y = random.random() * world_size
self.orientation = random.random() * 2.0 * pi
self.forward_noise = 0.0;
self.turn_noise = 0.0;
self.sense_noise = 0.0;
def set(self, new_x, new_y, new_orientation):
if new_x < 0 or new_x >= world_size:
raise (ValueError, 'X coordinate out of bound')
if new_y < 0 or new_y >= world_size:
raise (ValueError, 'Y coordinate out of bound')
if new_orientation < 0 or new_orientation >= 2 * pi:
raise (ValueError, 'Orientation must be in [0..2pi]')
self.x = float(new_x)
self.y = float(new_y)
self.orientation = float(new_orientation)
def set_noise(self, new_f_noise, new_t_noise, new_s_noise):
# makes it possible to change the noise parameters
# this is often useful in particle filters
self.forward_noise = float(new_f_noise);
self.turn_noise = float(new_t_noise);
self.sense_noise = float(new_s_noise);
def sense(self):
Z = []
for i in range(len(landmarks)):
dist = sqrt((self.x - landmarks[i][0]) ** 2 + (self.y - landmarks[i][1]) ** 2)
dist += random.gauss(0.0, self.sense_noise)
Z.append(dist)
return Z
def move(self, turn, forward):
if forward < 0:
raise (ValueError, 'Robot cant move backwards')
# turn, and add randomness to the turning command
orientation = self.orientation + float(turn) + random.gauss(0.0, self.turn_noise)
orientation %= 2 * pi
# move, and add randomness to the motion command
dist = float(forward) + random.gauss(0.0, self.forward_noise)
x = self.x + (cos(orientation) * dist)
y = self.y + (sin(orientation) * dist)
x %= world_size # cyclic truncate
y %= world_size
# set particle
res = robot()
res.set(x, y, orientation)
res.set_noise(self.forward_noise, self.turn_noise, self.sense_noise)
return res
def Gaussian(self, mu, sigma, x):
# calculates the probability of x for 1-dim Gaussian with mean mu and var. sigma
return exp(- ((mu - x) ** 2) / (sigma ** 2) / 2.0) / sqrt(2.0 * pi * (sigma ** 2))
def measurement_prob(self, measurement):
# calculates how likely a measurement should be
prob = 1.0;
for i in range(len(landmarks)):
dist = sqrt((self.x - landmarks[i][0]) ** 2 + (self.y - landmarks[i][1]) ** 2)
prob *= self.Gaussian(dist, self.sense_noise, measurement[i])
return prob
def __repr__(self):
return '[x=%.6s y=%.6s orient=%.6s]' % (str(self.x), str(self.y), str(self.orientation))
def eval(r, p):
sum = 0.0;
for i in range(len(p)): # calculate mean error
dx = (p[i].x - r.x + (world_size/2.0)) % world_size - (world_size/2.0)
dy = (p[i].y - r.y + (world_size/2.0)) % world_size - (world_size/2.0)
err = sqrt(dx * dx + dy * dy)
sum += err
return sum / float(len(p))
#### DON'T MODIFY ANYTHING ABOVE HERE! ENTER CODE BELOW ####
myrobot = robot()
myrobot.set(30, 50, pi/2)
myrobot = myrobot.move(-(pi/2), 15)
print(myrobot.sense())
myrobot = myrobot.move(-(pi/2), 10)
print(myrobot.sense())
在上面的代码中,您按照所问的问题进行了正确的操作,即将机器人设置为(30,50,pi/2),并在一次移动后进行感知,在第二次移动后再次进行感知
由于move方法返回一个新实例,该实例现在从其原始位置(即[30,50,pi/2])或移动,可以说在move方法中未设置myrobot对象的位置,但程序员正在设置新实例的位置(即res)。 因此,您声明的myrobot的位置将保持不变,因此为了避免这种情况,您需要使用move方法返回的对象重新实例化
但是,在2.)代码中,即使移动机器人两次,您也会得到相同的感知结果。 2.)代码中上述两条打印指令的结果将与将机器人设置为(30,50,pi/2)时相同,并且保持不变,因为在move方法中,正在创建新实例,并且正在更改此新实例的位置
因为您正在更改的属性不是类属性(即x、y和方向不是每个对象的公共属性)。 这就是为什么更改一个对象的属性不会更改另一个对象的相应属性的原因。因为类的每个实例都有自己的属性和方法副本
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