Danny Staple
308 lines
12 KiB
Python
308 lines
12 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 traceback
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from performance_counter import PerformanceCounter
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class DistanceSensorTracker:
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def __init__(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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self.left = 300
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self.right = 300
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async def main(self):
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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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if robot.left_distance.data_ready and robot.left_distance.distance:
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self.left = 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 = robot.right_distance.distance * 10
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robot.right_distance.clear_interrupt()
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await asyncio.sleep(0.01)
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class CollisionAvoid:
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def __init__(self, distance_sensors):
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self.speed = 0.5
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self.distance_sensors = distance_sensors
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async def main(self):
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try:
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while True:
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robot.set_right(self.speed)
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while self.distance_sensors.left < 300 or \
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self.distance_sensors.right < 300:
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robot.set_left(-self.speed)
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await asyncio.sleep(0.3)
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robot.set_left(self.speed)
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await asyncio.sleep(0)
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except Exception as e:
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send_json({"exception": traceback.format_exception(e, e, e.__traceback__)})
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raise
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def get_random_sample(mean, scale):
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return mean + (random.uniform(-scale, scale) + random.uniform(-scale, scale)) / 2
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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_json():
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try:
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data = robot.uart.readline()
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decoded = data.decode()
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return json.loads(decoded)
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except (UnicodeError, ValueError):
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print("Invalid data")
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return None
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def send_poses(samples):
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print(f"sending {len(samples)} poses")
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send_json({
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"poses": np.array(samples[:,:2], dtype=np.int16).tolist(),
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})
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class Simulation:
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def __init__(self):
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self.population_size = 200
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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.distance_sensors = DistanceSensorTracker()
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self.collision_avoider = CollisionAvoid(self.distance_sensors)
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self.last_encoder_left = robot.left_encoder.read()
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self.last_encoder_right = robot.right_encoder.read()
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self.alpha_rot = 0.09
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self.alpha_rot_trans = 0.05
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self.alpha_trans = 0.12
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self.alpha_trans_rot = 0.05
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# profiling
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self.pc_resample = PerformanceCounter()
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self.pc_odometry = PerformanceCounter()
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self.pc_move_poses = PerformanceCounter() # superset of odometry time
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self.pc_randomise_motion = PerformanceCounter()
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self.pc_motion_model = PerformanceCounter()
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self.pc_observe_distance_sensors = PerformanceCounter()
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self.pc_observation_model = PerformanceCounter()
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def convert_odometry_to_motion(self, left_encoder_delta, right_encoder_delta):
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"""
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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 degrees 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 degrees
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"""
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self.pc_odometry.start()
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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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return 0, left_mm, 0
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# calculate the radius of the arc
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radius = (robot.wheelbase_mm / 2) * (left_mm + right_mm) / (right_mm - left_mm)
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## angle = difference in steps / wheelbase
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d_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 = d_theta * radius
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rot1 = np.degrees(d_theta/2)
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rot2 = rot1
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self.pc_odometry.stop()
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return rot1, arc_length, rot2
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def move_poses(self, rot1, trans, rot2):
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self.pc_move_poses.start()
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self.poses[:,2] += rot1
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rot1_radians = np.radians(self.poses[:,2])
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self.poses[:,0] += trans * np.cos(rot1_radians)
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self.poses[:,1] += trans * np.sin(rot1_radians)
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self.poses[:,2] += rot2
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self.poses[:,2] = np.array([float(theta % 360) for theta in self.poses[:,2]])
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self.pc_move_poses.stop()
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def randomise_motion(self, rot1, trans, rot2):
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self.pc_randomise_motion.start()
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rot1_scale = self.alpha_rot * abs(rot1) + self.alpha_rot_trans * abs(trans)
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trans_scale = self.alpha_trans * abs(trans) + self.alpha_trans_rot * (abs(rot1) + abs(rot2))
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rot2_scale = self.alpha_rot * abs(rot2) + self.alpha_rot_trans * abs(trans)
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rot1_model = np.array([get_random_sample(rot1, rot1_scale) for _ in range(self.poses.shape[0])])
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trans_model = np.array([get_random_sample(trans, trans_scale) for _ in range(self.poses.shape[0])])
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rot2_model = np.array([get_random_sample(rot2, rot2_scale) for _ in range(self.poses.shape[0])])
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self.pc_randomise_motion.stop()
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return rot1_model, trans_model, rot2_model
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def motion_model(self):
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"""Apply the motion model"""
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self.pc_motion_model.start()
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new_encoder_left = robot.left_encoder.read()
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new_encoder_right = robot.right_encoder.read()
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rot1, trans, rot2 = self.convert_odometry_to_motion(
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new_encoder_left - self.last_encoder_left,
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new_encoder_right - self.last_encoder_right)
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self.last_encoder_left = new_encoder_left
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self.last_encoder_right = new_encoder_right
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rot1_model, trans_model, rot2_model = self.randomise_motion(rot1, trans, rot2)
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self.move_poses(rot1_model, trans_model, rot2_model)
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#
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# print(
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# json.dumps(
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# [self.poses.tolist(), rot1, trans, rot2]
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# )
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# )
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self.pc_motion_model.stop()
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def observe_distance_sensors(self, weights):
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self.pc_observe_distance_sensors.start()
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# Sensor triangle left
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adjacent = self.distance_sensors.left + robot.dist_forward_mm
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opposite = robot.dist_side_mm
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left_angle = np.atan(opposite / adjacent)
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left_hypotenuse = np.sqrt(opposite**2 + adjacent**2)
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# Sensor triangle right
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adjacent = self.distance_sensors.right + robot.dist_forward_mm
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opposite = robot.dist_side_mm
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right_angle = np.atan(opposite / adjacent)
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right_hypotenuse = np.sqrt(opposite**2 + adjacent**2)
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# modify the current weights based on the distance sensors
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left_sensor = np.zeros((self.poses.shape[0], 2), dtype=np.float)
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right_sensor = np.zeros((self.poses.shape[0], 2), dtype=np.float)
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# left sensor
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poses_left_angle = np.radians(self.poses[:, 2]) + left_angle
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left_sensor[:, 0] = self.poses[:, 0] + np.cos(poses_left_angle) * left_hypotenuse
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left_sensor[:, 1] = self.poses[:, 1] + np.sin(poses_left_angle) * left_hypotenuse
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# right sensor
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poses_right_angle = np.radians(self.poses[:, 2]) - right_angle
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right_sensor[:, 0] = self.poses[:, 0] + np.cos(poses_right_angle) * right_hypotenuse
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right_sensor[:, 1] = self.poses[:, 1] + np.sin(poses_right_angle) * right_hypotenuse
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# Look up the distance in the arena
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for index in range(self.poses.shape[0]):
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sensor_weight = arena.get_distance_likelihood_at(left_sensor[index,0], left_sensor[index,1])
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sensor_weight += arena.get_distance_likelihood_at(right_sensor[index,0], right_sensor[index,1])
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weights[index] *= sensor_weight
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send_json({"distance_observation":
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{
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"weight": weights[0],
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"pose": self.poses[0].tolist(),
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"left_sensor": left_sensor[0].tolist(),
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"right_sensor": right_sensor[0].tolist(),
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"left_distance": self.distance_sensors.left,
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"right_distance": self.distance_sensors.right
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}
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})
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self.pc_observe_distance_sensors.stop()
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return weights
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def observation_model(self):
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self.pc_observation_model.start()
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weights = np.ones(self.poses.shape[0], dtype=np.float)
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for index, pose in enumerate(self.poses):
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if not arena.contains(pose[:1], pose[:2]):
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weights[index] = arena.low_probability
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weights = self.observe_distance_sensors(weights)
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self.pc_observation_model.stop()
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return weights
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def resample(self, weights, sample_count):
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"""Return sample_count number of samples from the
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poses, based on the weights array.
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Uses low variance resampling"""
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self.pc_resample.start()
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samples = np.zeros((sample_count, 3))
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interval = np.sum(weights) / sample_count
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shift = random.uniform(0, interval)
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cumulative_weights = weights[0]
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source_index = 0
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try:
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for current_index in range(sample_count):
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weight_index = shift + current_index * interval
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while weight_index >= cumulative_weights:
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source_index += 1
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source_index = min(len(weights) - 1, source_index)
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cumulative_weights += weights[source_index]
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samples[current_index] = self.poses[source_index]
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except IndexError:
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send_json({"error": "IndexError in resample.", "weights": weights.tolist()})
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raise
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if samples.shape[0] != sample_count:
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send_json({"error": "Sample count mismatch in resample.", "samples": samples.tolist()})
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raise Exception("Sample count mismatch in resample.")
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self.pc_resample.stop()
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return samples
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def print_pc_lines(self):
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if self.pc_odometry.count % 10 != 0:
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return
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print("Resample: ", self.pc_resample.performance_line())
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print("Odometry: ", self.pc_odometry.performance_line())
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print("Move Poses: ", self.pc_move_poses.performance_line())
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print("Randomise Motion: ", self.pc_randomise_motion.performance_line())
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print("Motion Model: ", self.pc_motion_model.performance_line())
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print("Observe Distance Sensors: ", self.pc_observe_distance_sensors.performance_line())
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print("Observation Model: ", self.pc_observation_model.performance_line())
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async def main(self):
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asyncio.create_task(self.distance_sensors.main())
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collision_avoider = asyncio.create_task(self.collision_avoider.main())
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try:
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while True:
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weights = self.observation_model()
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print("Weights: ", weights)
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send_poses(self.resample(weights, 20))
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self.poses = self.resample(weights, self.population_size)
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await asyncio.sleep(0.05)
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self.motion_model()
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self.print_pc_lines()
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except Exception as e:
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send_json({"exception": traceback.format_exception(e, e, e.__traceback__)})
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raise
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finally:
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collision_avoider.cancel()
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robot.stop()
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async def command_handler(simulation):
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print("Starting handler")
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simulation_task = None
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while True:
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if robot.uart.in_waiting:
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request = read_json()
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if not request:
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continue
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print("Received: ", request)
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if request["command"] == "arena":
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send_json({
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"arena": arena.boundary_lines,
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})
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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.main())
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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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