Displaying a pose written up.

This commit is contained in:
Danny Staple 2022-12-21 14:16:37 +00:00
parent 752c298f8d
commit c14c83e286
8 changed files with 119 additions and 60 deletions

View File

@ -9,20 +9,19 @@ from robot_ble_connection import BleConnection
class RobotDisplay:
def __init__(self):
self.ble_connection = BleConnection(self.handle_data)
self.line = ""
self.buffer = ""
self.arena = {}
self.display_closed = False
self.closed = False
self.fig, self.ax = plt.subplots()
self.pose_coords = []
self.pose_uv = []
self.poses = None
def handle_close(self, _):
self.display_closed = True
self.closed = True
def handle_data(self, data):
self.line += data.decode("utf-8")
while "\n" in self.line:
line, self.line = self.line.split("\n", 1)
self.buffer += data.decode("utf-8")
while "\n" in self.buffer:
line, self.buffer = self.buffer.split("\n", 1)
print(f"Received data: {line}")
try:
message = json.loads(line)
@ -32,12 +31,7 @@ class RobotDisplay:
if "arena" in message:
self.arena = message
if "poses" in message:
# the robot poses are an array of [x, y, theta] arrays.
# matplotlib quiver plots wants an [x] ,[y] and [angle] arrays
poses = np.array(message["poses"]).T
self.pose_coords = poses[:2]
angle_rads = np.deg2rad(poses[2])
self.pose_uv = np.array([np.cos(angle_rads), np.sin(angle_rads)])
self.poses = np.array(message["poses"], dtype=np.int16)
def draw(self):
self.ax.clear()
@ -46,12 +40,8 @@ class RobotDisplay:
self.ax.plot(
[line[0][0], line[1][0]], [line[0][1], line[1][1]], color="black"
)
for line in self.arena["target_zone"]:
self.ax.plot(
[line[0][0], line[1][0]], [line[0][1], line[1][1]], color="red"
)
if len(self.pose_coords) > 0:
self.ax.quiver(self.pose_coords[0], self.pose_coords[1], self.pose_uv[0], self.pose_uv[1], color="blue")
if self.poses is not None:
self.ax.scatter(self.poses[:,0], self.poses[:,1], color="blue")
async def main(self):
plt.ion()
@ -62,7 +52,7 @@ class RobotDisplay:
await self.ble_connection.send_uart_data(request)
self.fig.canvas.mpl_connect("close_event", self.handle_close)
while not self.display_closed:
while not self.closed:
self.draw()
plt.draw()
plt.pause(0.05)

View File

@ -1,5 +1,8 @@
"""Represent the lines and target zone of the arena"""
"""Represent the lines of the arena"""
try:
from ulab import numpy as np
except ImportError:
import numpy as np
boundary_lines = [
[(0,0), (0, 1500)],
@ -10,13 +13,65 @@ boundary_lines = [
[(1000, 0), (0, 0)],
]
target_zone = [
[(1100, 900), (1100, 1100)],
[(1100, 1100), (1250, 1100)],
[(1250, 1100), (1250, 900)],
[(1250, 900), (1100, 900)],
]
width = 1500
height = 1500
grid_cell_size = 50
overscan = 10 # 10 each way
def get_distance_to_segment(x, y, segment):
"""Return the distance from the point to the segment.
Segment -> ((x1, y1), (x2, y2))
All segments are horizontal or vertical.
"""
x1, y1 = segment[0]
x2, y2 = segment[1]
# if the segment is horizontal, the point will be closest to the y value of the segment
if y1 == y2 and x >= min(x1, x2) and x <= max(x1, x2):
return abs(y - y1)
# if the segment is vertical, the point will be closest to the x value of the segment
if x1 == x2 and y >= min(y1, y2) and y <= max(y1, y2):
return abs(x - x1)
# the point will be closest to one of the end points
return np.sqrt(
min(
(x - x1) ** 2 + (y - y1) ** 2,
(x - x2) ** 2 + (y - y2) ** 2
)
)
def get_distance_likelihood(x, y):
"""Return the distance from the point to the nearest segment as a decay function."""
min_distance = None
for segment in boundary_lines:
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
grid_cell_size = 50
overscan = 10 # 10 each way
# beam endpoint model
def make_distance_grid():
"""Take the boundary lines. With and overscan of 10 cells, and grid cell size of 5cm (50mm),
make a grid of the distance to the nearest boundary line.
"""
grid = np.zeros((
width // grid_cell_size + 2 * overscan,
height // grid_cell_size + 2 * overscan
), dtype=np.float)
for x in range(grid.shape[0]):
column_x = x * grid_cell_size - (overscan * grid_cell_size)
for y in range(grid.shape[1]):
row_y = y * grid_cell_size - (overscan * grid_cell_size)
grid[x, y] = get_distance_likelihood(
column_x, row_y
)
return grid
distance_grid = make_distance_grid()

View File

@ -6,37 +6,51 @@ from ulab import numpy as np
import arena
import robot
class Simulation:
def __init__(self):
population_size = 10
self.poses = np.empty((population_size, 3), dtype=np.float)
for n in range(population_size):
self.poses[n] = random.uniform(0, arena.width), random.uniform(0, arena.height), random.uniform(0, 360)
def send_json(data):
robot.uart.write((json.dumps(data)+"\n").encode())
robot.uart.write((json.dumps(data) + "\n").encode())
def read_json():
try:
data = robot.uart.readline()
decoded = data.decode()
return json.loads(decoded)
except (UnicodeError, ValueError):
print("Invalid data")
return None
class Simulation:
def __init__(self):
self.population_size = 20
self.poses = np.array(
[(
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.int16,
)
def send_poses(samples):
send_json({
"poses": samples[:,:2].tolist(),
})
async def command_handler(simulation):
while True:
if robot.uart.in_waiting:
print("Receiving data...")
try:
data = robot.uart.readline().decode()
request = json.loads(data)
except (UnicodeError, ValueError):
print("Invalid data")
continue
# {"command": "arena"}
if request["command"] == "arena":
send_json({
"arena": arena.boundary_lines,
"target_zone": arena.target_zone,
})
else:
send_json({
"poses": simulation.poses.tolist(),
})
await asyncio.sleep(0.1)
print("Starting handler")
while True:
if robot.uart.in_waiting:
request = read_json()
if not request:
continue
print("Received: ", request)
if request["command"] == "arena":
send_json({
"arena": arena.boundary_lines,
})
send_poses(simulation.poses)
simulation= Simulation()
await asyncio.sleep(0.1)
simulation = Simulation()
asyncio.run(command_handler(simulation))