f57add5106
Including: - replacing guassian with simpler triangle distribution - Timing calculations -resulting in using only 50 points. - Changes (which may need to be reconsidered) for optimization. These are likely to be simplified back.
339 lines
14 KiB
Python
339 lines
14 KiB
Python
import asyncio
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import json
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import random
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from ulab import numpy as np
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import arena
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import robot
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import math
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import time
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# initial sample set - uniform
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# then apply sensor model
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# then resample
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# then apply motion model
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# and repeat
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class VaryingWallAvoid:
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def __init__(self):
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self.speed = 0.6
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self.last_call = time.monotonic()
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def speed_from_distance(self, distance):
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limited_error = min(distance, 300) * self.speed
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motor_speed = limited_error / 300
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if motor_speed < 0.2:
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motor_speed = -0.3
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return motor_speed
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def update(self, left_distance, right_distance):
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# Currently being called every 1.6 seconds - that is far too long.
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print("Since last call:", time.monotonic() - self.last_call)
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left = self.speed_from_distance(left_distance)
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right = self.speed_from_distance(right_distance)
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# print("left speed:", left, "right speed:", right)
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robot.set_left(left)
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robot.set_right(right)
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self.last_call = time.monotonic()
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triangular_proportion = math.sqrt(6) / 2
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def get_triangular_sample(mean, standard_deviation):
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base = triangular_proportion * (random.uniform(-standard_deviation, standard_deviation) + random.uniform(-standard_deviation, standard_deviation))
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return mean + base
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class Simulation:
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def __init__(self):
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self.population_size = 50
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self.left_distance = 100
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self.right_distance = 100
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self.imu_mix = 0.3 * 0.5
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self.encoder_mix = 0.7
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self.rotation_standard_dev = 2 # degrees
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self.speed_standard_dev = 5 # mm
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# Poses - each an array of [x, y, heading]
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self.poses = np.array(
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[(
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int(random.uniform(0, arena.width)),
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int(random.uniform(0, arena.height)),
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int(random.uniform(0, 360))) for _ in range(self.population_size)],
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dtype=np.int16,
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)
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self.collision_avoider = VaryingWallAvoid()
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async def apply_sensor_model(self):
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# Timing is about 0.65s
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# Based on vl53l1x sensor readings, create weight for each pose.
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# vl53l1x standard dev is +/- 5 mm. Each distance is a mean reading
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# we will first determine sensor positions based on poses
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# project forward based on distances sensed, introducing noise (based on standard dev)
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# then check this projected position against occupancy grid
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# and weight accordingly
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# distance sensor positions projected forward. x, y, heading, reading
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fn_start = time.monotonic()
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print("Starting apply sensor model")
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distance_sensor_left_rays = np.zeros(
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(self.poses.shape[0], 3), dtype=np.float)
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distance_sensor_right_rays = np.zeros(
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(self.poses.shape[0], 3), dtype=np.float)
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# sensors - they are facing forward, either side of the robot. Project them out to the sides
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# based on each poses heading and turn sensors into rays,
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# left sensor
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poses_left_90 = np.radians(self.poses[:, 2] + 90)
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# print("poses_left_90_shape:",poses_left_90.shape, "distance_sensor_positions_shape:",distance_sensor_positions.shape, "poses_shape:",self.poses.shape)
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distance_sensor_left_rays[:, 0] = self.poses[:, 0] + np.cos(poses_left_90) * robot.distance_sensor_from_middle
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distance_sensor_left_rays[:, 1] = self.poses[:, 1] + np.sin(poses_left_90) * robot.distance_sensor_from_middle
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distance_sensor_left_rays[:, 2] = np.radians(self.poses[:, 2])
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# right sensor
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poses_right_90 = np.radians(self.poses[:, 2] - 90)
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distance_sensor_right_rays[:, 0] = self.poses[:, 0] + np.cos(poses_right_90) * robot.distance_sensor_from_middle
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distance_sensor_right_rays[:, 1] = self.poses[:, 1] + np.sin(poses_right_90) * robot.distance_sensor_from_middle
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distance_sensor_right_rays[:, 2] = np.radians(self.poses[:, 2])
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# for each sensor position, find the distance to the nearest obstacle
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distance_sensor_standard_dev = 5
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dl_squared = self.left_distance ** 2
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dr_squared = self.right_distance ** 2
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await asyncio.sleep(0)
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print("Time to calculate sensor positions:", time.monotonic() - fn_start)
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fn_start = time.monotonic()
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# weighted poses a numpy array of weights for each pose
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weights = np.empty(self.poses.shape[0], dtype=np.float)
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# 0.6 seconds in this loop!
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for index in range(self.poses.shape[0]):
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# remove any that are outside the arena
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if not arena.point_is_inside_arena(self.poses[index,0], self.poses[index,1]) or \
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not arena.point_is_inside_arena(distance_sensor_left_rays[index,0], distance_sensor_left_rays[index,1]) or \
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not arena.point_is_inside_arena(distance_sensor_right_rays[index,0], distance_sensor_right_rays[index,1]):
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weights[index] = 0
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continue
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# difference between this distance and the distance sensed is the error
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# add noise to this error
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# left sensor
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noise = get_triangular_sample(0, distance_sensor_standard_dev)
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left_actual = arena.get_ray_distance_squared_to_nearest_boundary_segment(distance_sensor_left_rays[index])
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left_error = abs(left_actual - dl_squared + noise)
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# right sensor
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noise = get_triangular_sample(0, distance_sensor_standard_dev)
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right_actual = arena.get_ray_distance_squared_to_nearest_boundary_segment(distance_sensor_right_rays[index])
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right_error = abs(right_actual - dr_squared + noise)
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# weight is the inverse of the error
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weights[index] = 1 / (left_error + right_error)
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print("Time to calculate pose weights", time.monotonic() - fn_start)
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await asyncio.sleep(0)
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#normalise the weights
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# print("Weights sum before normalising:", np.sum(weights))
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weights = weights / np.sum(weights)
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# print("Weights sum:", np.sum(weights))
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return weights
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def resample(self, weights):
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# Fast - 0.01 to 0.035 seconds
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# based on the weights, resample the poses
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# weights is a numpy array of weights
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# resample is a numpy array of indices into the poses array
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# fn_start = time.monotonic()
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samples = []
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# use low variance resampling
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start = random.uniform(0, 1 / self.population_size)
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cumulative_weights = weights[0]
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source_index = 0
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for current_index in range(self.population_size):
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sample_index = start + current_index / self.population_size
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while sample_index > cumulative_weights:
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source_index += 1
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cumulative_weights += weights[source_index]
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samples.append(source_index)
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# set poses to the resampled poses
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self.poses = np.array([self.poses[n] for n in samples])
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# print("resample time", time.monotonic() - fn_start)
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def convert_odometry_to_motion(self, left_encoder_delta, right_encoder_delta):
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# convert odometry to motion
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# left_encoder is the change in the left encoder
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# right_encoder is the change in the right encoder
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# returns rot1, trans, rot2
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# rot1 is the rotation of the robot in radians before the translation
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# trans is the distance the robot has moved in mm
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# rot2 is the rotation of the robot in radians
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# convert encoder counts to mm
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left_mm = left_encoder_delta * robot.ticks_to_mm
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right_mm = right_encoder_delta * robot.ticks_to_mm
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if left_mm == right_mm:
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# no rotation
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return 0, left_mm, 0
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# calculate the ICC
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radius = (robot.wheelbase_mm / 2) * (left_mm + right_mm) / (right_mm - left_mm)
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theta = (right_mm - left_mm) / robot.wheelbase_mm
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# For a small enough motion, assume that the chord length = arc length
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arc_length = theta * radius
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rot1 = np.degrees(theta/2)
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rot2 = rot1
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return rot1, arc_length, rot2
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async def motion_model(self):
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"""move forward, apply the motion model"""
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# fn_start = time.monotonic()
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# Reading sensors - 0.001 to 0.002 seconds.
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starting_heading = robot.imu.euler[0]
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encoder_left = robot.left_encoder.read()
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encoder_right = robot.right_encoder.read()
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# print("Reading sensors time", time.monotonic() - fn_start)
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await asyncio.sleep(0.01)
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# fn_start = time.monotonic()
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# record sensor changes - 0.001 to 0.002 seconds
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rot1, trans, rot2 = self.convert_odometry_to_motion(
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robot.left_encoder.read() - encoder_left,
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robot.right_encoder.read() - encoder_right)
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try:
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new_heading = robot.imu.euler[0]
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except OSError:
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new_heading = None
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if new_heading:
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heading_change = starting_heading - new_heading
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# blend with the encoder heading changes
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rot1 = rot1 * self.encoder_mix + heading_change * self.imu_mix
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rot2 = rot2 * self.encoder_mix + heading_change * self.imu_mix
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else:
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print("Failed to get heading")
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# print("Got headings time", time.monotonic() - fn_start)
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# fn_start = time.monotonic()
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# move poses 0.07 - 0.08 seconds
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rot1_model = np.array([get_triangular_sample(rot1, self.rotation_standard_dev) for _ in range(self.poses.shape[0])])
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trans_model = np.array([get_triangular_sample(trans, self.speed_standard_dev) for _ in range(self.poses.shape[0])])
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rot2_model = np.array([get_triangular_sample(rot2, self.rotation_standard_dev) for _ in range(self.poses.shape[0])])
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self.poses[:,2] += rot1_model
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rot1_radians = np.radians(self.poses[:,2])
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self.poses[:,0] += trans_model * np.sin(rot1_radians)
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self.poses[:,1] += trans_model * np.cos(rot1_radians)
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self.poses[:,2] += rot2_model
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self.poses[:,2] = np.vectorize(lambda n: float(n % 360))(self.poses[:,2])
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self.poses = np.array(self.poses, dtype=np.int16)
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# print("Move poses times", time.monotonic() - fn_start)
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async def distance_sensor_updater(self):
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robot.left_distance.distance_mode = 2
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robot.right_distance.distance_mode = 2
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robot.left_distance.timing_budget = 50
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robot.right_distance.timing_budget = 50
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robot.left_distance.start_ranging()
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robot.right_distance.start_ranging()
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while True:
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# About 0.02 seconds
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# loop_start = time.monotonic()
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if robot.left_distance.data_ready and robot.left_distance.distance:
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self.left_distance = robot.left_distance.distance * 10 # convert to mm
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robot.left_distance.clear_interrupt()
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if robot.right_distance.data_ready and robot.right_distance.distance:
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self.right_distance = robot.right_distance.distance * 10
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robot.right_distance.clear_interrupt()
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print("left_distance:", self.left_distance, "right_distance:", self.right_distance)
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# move forward - with collision avoidance 0.03 to 0.04 seconds
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self.collision_avoider.update(self.left_distance, self.right_distance)
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# print("distance_sensor_updater_used_time: ", time.monotonic() - loop_start)
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await asyncio.sleep(0.01)
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async def run(self):
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asyncio.create_task(self.distance_sensor_updater())
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try:
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while True:
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# print("Applying sensor model")
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weights = await self.apply_sensor_model()
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# print("Sensor model complete.\nResampling")
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self.resample(weights)
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# print("Resampling complete.\nMoving robot")
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await self.motion_model()
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# print("Robot move complete")
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finally:
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robot.stop()
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def send_json(data):
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robot.uart.write((json.dumps(data) + "\n").encode())
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def read_command():
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data = robot.uart.readline()
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try:
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decoded = data.decode()
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except UnicodeError:
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print("UnicodeError decoding :")
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print(data)
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return None
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try:
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request = json.loads(decoded)
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except ValueError:
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print("ValueError reading json from:")
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print(decoded)
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return None
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return request
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async def updater(simulation):
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print("starting updater")
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while True:
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loop_start = time.monotonic()
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# Imu calibration and send - 0.0625 seconds
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sys_status, gyro, accel, mag = robot.imu.calibration_status
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if sys_status < 3:
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send_json(
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{
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"imu_calibration": {
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"gyro": gyro,
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"accel": accel,
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"mag": mag,
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"sys": sys_status,
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}
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}
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)
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print("Sent imu calibration in", time.monotonic() - loop_start)
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# The big time delay is in sending the poses.
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print("Sending poses", simulation.poses.shape[0])
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for n in range(0, simulation.poses.shape[0], 10):
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loop_start = time.monotonic()
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# each pose group is 0.2 seconds.
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# print("Sending poses from ", n, "to", n+10, "of", simulation.poses.shape[0], "poses")
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send_json({
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"poses": simulation.poses[n:n+10].tolist(),
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"offset": n,
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})
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print("Sent poses in", time.monotonic() - loop_start)
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await asyncio.sleep(0.01)
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await asyncio.sleep(0.5)
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async def command_handler(simulation):
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print("Starting handler")
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update_task = None
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simulation_task = None
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# simulation_task = asyncio.create_task(simulation.run())
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while True:
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if robot.uart.in_waiting:
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print("Receiving data...")
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request = read_command()
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if not request:
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print("no request")
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continue
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if request["command"] == "arena":
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send_json(
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{
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"arena": arena.boundary_lines
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}
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)
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if not update_task:
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update_task = asyncio.create_task(updater(simulation))
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elif request["command"] == "start":
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if not simulation_task:
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simulation_task = asyncio.create_task(simulation.run())
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await asyncio.sleep(0.1)
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simulation = Simulation()
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asyncio.run(command_handler(simulation))
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