[Artificial|[Artificial Intelligence for Robotics] {cs373} Lesson 8: Particle Filters [ArtificialIntelligenceforRobotics]{

Field Trip

State Space & Belief Modality & Efficiency & Exact or Approximate
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Particle Filters
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Moving Robot

# 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 randomlandmarks= [[20.0, 20.0], [80.0, 80.0], [20.0, 80.0], [80.0, 20.0]] world_size = 100.0class 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 Zdef 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 resdef 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 beprob = 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 probdef __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()