ecki/Robotics-at-Home-with-Raspberry-Pi-Pico
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Latest changes for error and perf checking

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This commit is contained in:
Danny Staple
2023-01-28 21:32:02 +00:00
parent a4f71ef889
commit 416bb52ebf
4 changed files with 63 additions and 35 deletions
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ch-13/4.3-monte-carlo_perf/display_arena.py
+16
View File
@@ -0,0 +1,16 @@
from matplotlib import pyplot as plt
from robot import arena
print(arena.distance_grid.min(), arena.distance_grid.max())
for line in arena.boundary_lines:
plt.plot([line[0][0], line[1][0]], [line[0][1], line[1][1]], color="black")
overscan_size = arena.overscan * arena.grid_cell_size
plt.imshow(
arena.distance_grid.T,
extent = [-overscan_size, arena.width + overscan_size, -overscan_size, arena.height + overscan_size],
origin="lower",
cmap="gray"
)
plt.show()
ch-13/4.3-monte-carlo_perf/robot/arena.py
+5 -3
View File
@@ -9,6 +9,8 @@ height = 1500
cutout_width = 500
cutout_height = 500
low_probability = 10 ** -10
boundary_lines = [
[(0,0), (0, height)],
[(0, height), (width, height)],
@@ -65,7 +67,7 @@ def get_distance_likelihood(x, y):
distance = get_distance_to_segment(x, y, segment)
if min_distance is None or distance < min_distance:
min_distance = distance
return 1.0 / (1 + min_distance/250) ** 2
return 1.0 / (1 + min_distance/100) ** 2
# beam endpoint model
@@ -88,10 +90,10 @@ def make_distance_grid():
distance_grid = make_distance_grid()
def get_distance_grid_at_point(x, y):
def get_distance_likelihood_at(x, y):
"""Return the distance grid value at the given point."""
grid_x = int(x // grid_cell_size + overscan)
grid_y = int(y // grid_cell_size + overscan)
if grid_x < 0 or grid_x >= distance_grid.shape[0] or grid_y < 0 or grid_y >= distance_grid.shape[1]:
return 0
return low_probability
return distance_grid[grid_x, grid_y]
ch-13/4.3-monte-carlo_perf/robot/code.py
+40 -32
View File
@@ -79,16 +79,16 @@ class Simulation:
int(random.uniform(0, arena.width)),
int(random.uniform(0, arena.height)),
int(random.uniform(0, 360))) for _ in range(self.population_size)],
dtype=np.float,
dtype=np.int16,
)
self.distance_sensors = DistanceSensorTracker()
self.collision_avoider = CollisionAvoid(self.distance_sensors)
self.last_encoder_left = robot.left_encoder.read()
self.last_encoder_right = robot.right_encoder.read()
self.alpha_rot = 0.05
self.alpha_rot_trans = 0.01
self.alpha_trans = 0.05
self.alpha_trans_rot = 0.01
self.alpha_rot = 0.09
self.alpha_rot_trans = 0.05
self.alpha_trans = 0.12
self.alpha_trans_rot = 0.05
# profiling
self.pc_resample = PerformanceCounter()
@@ -99,26 +99,6 @@ class Simulation:
self.pc_observe_distance_sensors = PerformanceCounter()
self.pc_observation_model = PerformanceCounter()
def resample(self, weights, sample_count):
"""Return sample_count number of samples from the
poses, based on the weights array.
Uses low variance resampling"""
self.pc_resample.start()
samples = np.zeros((sample_count, 3))
interval = 1 / sample_count
shift = random.uniform(0, interval)
cumulative_weights = weights[0]
source_index = 0
for current_index in range(sample_count):
weight_index = shift + current_index * interval
while weight_index >= cumulative_weights:
source_index += 1
source_index = min(len(weights), source_index)
cumulative_weights += weights[source_index]
samples[current_index] = self.poses[source_index]
self.pc_resample.stop()
return samples
def convert_odometry_to_motion(self, left_encoder_delta, right_encoder_delta):
"""
left_encoder is the change in the left encoder
@@ -203,12 +183,14 @@ class Simulation:
right_hypotenuse = np.sqrt(opposite**2 + adjacent**2)
# modify the current weights based on the distance sensors
left_sensor = np.zeros((self.poses.shape[0], 2), dtype=np.float)
# left_sensor = np.zeros((self.poses.shape[0], 2), dtype=np.float)
right_sensor = np.zeros((self.poses.shape[0], 2), dtype=np.float)
# left sensor
poses_left_angle = np.radians(self.poses[:, 2]) + left_angle
left_sensor[:, 0] = self.poses[:, 0] + np.cos(poses_left_angle) * left_hypotenuse
left_sensor[:, 1] = self.poses[:, 1] + np.sin(poses_left_angle) * left_hypotenuse
left_sensor = np.concatenate([
self.poses[:, 0] + np.cos(poses_left_angle) * left_hypotenuse,
self.poses[:, 1] + np.sin(poses_left_angle) * left_hypotenuse
], axis=1)
# right sensor
poses_right_angle = np.radians(self.poses[:, 2]) - right_angle
@@ -217,8 +199,8 @@ class Simulation:
# Look up the distance in the arena
for index in range(self.poses.shape[0]):
sensor_weight = arena.get_distance_grid_at_point(left_sensor[index,0], left_sensor[index,1])
sensor_weight += arena.get_distance_grid_at_point(right_sensor[index,0], right_sensor[index,1])
sensor_weight = arena.get_distance_likelihood_at(left_sensor[index,0], left_sensor[index,1])
sensor_weight += arena.get_distance_likelihood_at(right_sensor[index,0], right_sensor[index,1])
weights[index] *= sensor_weight
self.pc_observe_distance_sensors.stop()
return weights
@@ -228,12 +210,38 @@ class Simulation:
weights = np.ones(self.poses.shape[0], dtype=np.float)
for index, pose in enumerate(self.poses):
if not arena.contains(pose[:1], pose[:2]):
weights[index] = 0.01
weights[index] = arena.low_probability
weights = self.observe_distance_sensors(weights)
weights = weights / np.sum(weights)
self.pc_observation_model.stop()
return weights
def resample(self, weights, sample_count):
"""Return sample_count number of samples from the
poses, based on the weights array.
Uses low variance resampling"""
self.pc_resample.start()
samples = np.zeros((sample_count, 3))
interval = np.sum(weights) / sample_count
shift = random.uniform(0, interval)
cumulative_weights = weights[0]
source_index = 0
try:
for current_index in range(sample_count):
weight_index = shift + current_index * interval
while weight_index >= cumulative_weights:
source_index += 1
source_index = min(len(weights), source_index)
cumulative_weights += weights[source_index]
samples[current_index] = self.poses[source_index]
except IndexError:
send_json({"error": "IndexError in resample.", "weights": [weights.tolist()]})
raise
if samples.shape[0] != sample_count:
send_json({"error": "Sample count mismatch in resample.", "samples": [samples.tolist()]})
raise Exception("Sample count mismatch in resample.")
self.pc_resample.stop()
return samples
def print_pc_lines(self):
if self.pc_odometry.count % 10 != 0:
return
ch-13/4.3-monte-carlo_perf/robot/performance_counter.py
+2
View File
@@ -16,6 +16,8 @@ class PerformanceCounter:
self.count += 1
def per_call(self):
if self.count == 0:
return 0
return self.total_time / self.count
def total_call_time(self):