broken_robot.goto(target_bot.x*size_multiplier, target_bot.y*size_multiplier-200)
broken_robot.stamp()
prediction.setheading(target_bot.heading*180/pi)
prediction.goto(position_guess[0]*size_multiplier, position_guess[1]*size_multiplier-200)
prediction.stamp()
#End of Visualization
return localized
# This is a demo for what a strategy could look like. This one isn't very good.
def naive_next_pos(measurement, OTHER = None):
"""This strategy records the first reported position of the target and
assumes that eventually the target bot will eventually return to that
position, so it always guesses that the first position will be the next."""
if not OTHER: # this is the first measurement
OTHER = measurement
xy_estimate = OTHER
return xy_estimate, OTHER
# This is how we create a target bot. Check the robot.py file to understand
# How the robot class behaves.
test_target = robot(2.1, 4.3, 0.5, 2*pi / 34.0, 1.5)
measurement_noise = 0.05 * test_target.distance
test_target.set_noise(0.0, 0.0, measurement_noise)
demo_grading(estimate_next_pos, test_target)
#Visualize it
measuredbroken_robot.setheading(target_bot.heading * 180 / pi)
measuredbroken_robot.goto(
target_measurement[0] * size_multiplier,
target_measurement[1] * size_multiplier - 100)
measuredbroken_robot.stamp()
broken_robot.setheading(target_bot.heading * 180 / pi)
broken_robot.goto(target_bot.x * size_multiplier,
target_bot.y * size_multiplier - 100)
chaser_robot.setheading(hunter_bot.heading * 180 / pi)
chaser_robot.goto(hunter_bot.x * size_multiplier,
hunter_bot.y * size_multiplier - 100)
estimation_robot.goto(estimation[0] * size_multiplier,
estimation[1] * size_multiplier - 100)
#End of visualization
ctr += 1
if ctr >= 1000:
print "It took too many steps to catch the target."
return caught
target = robot(0.0, 10.0, 0.0, 2 * pi / 30, 1.5)
# measurement_noise = 2.0*target.distance # VERY NOISY!!
measurement_noise = .05 * target.distance # VERY NOISY!!
target.set_noise(0.0, 0.0, measurement_noise)
hunter = robot(-10.0, -10.0, 0.0)
print demo_grading2(hunter, target, next_move)
broken_robot.goto(target_bot.x*size_multiplier, target_bot.y*size_multiplier-200)
broken_robot.stamp()
prediction.setheading(target_bot.heading*180/pi)
prediction.goto(position_guess[0]*size_multiplier, position_guess[1]*size_multiplier-200)
prediction.stamp()
#End of Visualization
return localized
# This is a demo for what a strategy could look like. This one isn't very good.
def naive_next_pos(measurement, OTHER = None):
"""This strategy records the first reported position of the target and
assumes that eventually the target bot will eventually return to that
position, so it always guesses that the first position will be the next."""
if not OTHER: # this is the first measurement
OTHER = measurement
xy_estimate = OTHER
return xy_estimate, OTHER
# This is how we create a target bot. Check the robot.py file to understand
# How the robot class behaves.
test_target = robot(2.1, 4.3, 0.5, 2*pi / 34.0, 1.5)
measurement_noise = 0.05 * test_target.distance
test_target.set_noise(0.0, 0.0, measurement_noise)
demo_grading(estimate_next_pos, test_target)
#Visualize it
measuredbroken_robot.setheading(target_bot.heading*180/pi)
measuredbroken_robot.goto(target_measurement[0]*size_multiplier, target_measurement[1]*size_multiplier-100)
measuredbroken_robot.stamp()
broken_robot.setheading(target_bot.heading*180/pi)
broken_robot.goto(target_bot.x*size_multiplier, target_bot.y*size_multiplier-100)
chaser_robot.setheading(hunter_bot.heading*180/pi)
chaser_robot.goto(hunter_bot.x*size_multiplier, hunter_bot.y*size_multiplier-100)
if estimation:
estimation_robot.goto(estimation[0]*size_multiplier, estimation[1]*size_multiplier-100)
#End of visualization
ctr += 1
if ctr >= 1000:
print "It took too many steps to catch the target."
return caught
target = robot(0.0, 10.0, 0.0, 2*pi / 30, 1.5)
# measurement_noise = 2.0*target.distance # VERY NOISY!!
measurement_noise = .5*target.distance # VERY NOISY!!
target.set_noise(0.0, 0.0, measurement_noise)
hunter = robot(-10.0, -10.0, 0.0)
print demo_grading2(hunter, target, next_move)
