def __init__(self, cols, rows, widthMM, heightMM,
#the x,y and th of the current location can be passed
currentRow = -1, currentCol = -1, currentTh= 0,
goalRow = -1, goalCol = -1, gridUpdate = "",
image=None, grid=None, gridResize=1.0,showIter=0, title=""):
self.markerToolkit = MarkerToolkit()
self.x_localizeMM = 0
self.y_localizeMM = 0
self.thr_localize = 0
self.x_last_marker_localizeMM = 0
self.x_last_marker_robotMM = 0
self.y_last_marker_localizeMM = 0
self.y_last_marker_robotMM = 0
self.thr_last_marker_localize = 0
self.thr_last_marker_robot = 0
self.resetMarkers()
OccupancyGrid.__init__(self, cols=cols, rows=rows,
widthMM=widthMM, heightMM=heightMM,
currentRow=currentRow, currentCol=currentCol,
currentTh=currentTh,
goalRow=goalRow, goalCol=goalCol,
image=image, grid=grid, gridResize=gridResize,
showIter=showIter, title=title)
def mergeOtherGridInfo(self, otherGrid):
# find two common markers
commonKeys = []
for key in self.markers.keys():
if otherGrid.markerExists(key):
commonKeys.append(key)
if len(commonKeys) < 2:
print "I can not find two common markers, merge failed!"
return
# for now we just use the first two common markers found
thisGridAnchor1Row = self.getMarkerRow(commonKeys[0])
thisGridAnchor1Col = self.getMarkerCol(commonKeys[0])
thisGridAnchor2Row = self.getMarkerRow(commonKeys[1])
thisGridAnchor2Col = self.getMarkerCol(commonKeys[1])
otherGridAnchor1Row = otherGrid.getMarkerRow(commonKeys[0])
otherGridAnchor1Col = otherGrid.getMarkerCol(commonKeys[0])
otherGridAnchor2Row = otherGrid.getMarkerRow(commonKeys[1])
otherGridAnchor2Col = otherGrid.getMarkerCol(commonKeys[1])
return OccupancyGrid.mergeOtherGridInfo(
self,
otherGrid,
thisGridAnchor1Row=thisGridAnchor1Row,
thisGridAnchor1Col=thisGridAnchor1Col,
thisGridAnchor2Row=thisGridAnchor2Row,
thisGridAnchor2Col=thisGridAnchor2Col,
otherGridAnchor1Row=otherGridAnchor1Row,
otherGridAnchor1Col=otherGridAnchor1Col,
otherGridAnchor2Row=otherGridAnchor2Row,
otherGridAnchor2Col=otherGridAnchor2Col,
)
def drawCell(self,row,col,matrix,maxval,path,stepByStep,removeBelow=0):
# see the occupancyGrid redraw comment for more info on the coordinate
# system.
OccupancyGrid.drawCell(self,row,col,matrix,maxval,path,stepByStep,
removeBelow)
x = col
y = row
if self.hasMarker(row=row,col=col):
self.canvas.create_text((x + .5) * self.colScale,
(y + .5) * self.rowScale,
tag = 'label',
text=self.printMarker(row=row,col=col),
anchor="center",
fill='orange')
# Since our "robot" is actually a shape we need to define our own 2d pose func # current_pose = get_2d_pose(robot) target_pose = get_2d_pose(goal) current_pose = robot.get_position(vision_sensor)[:2] + robot.get_orientation()[-1:] current_pose = (-current_pose[0], current_pose[1], current_pose[2]) print(current_pose) print(get_2d_pose(robot)) pr.step() origin_x, _, origin_y, _, _, _ = vision_sensor.get_bounding_box() # Setup occ_grid occ_grid = OccupancyGrid(origin_x, origin_y) depth = vision_sensor.capture_depth() occ_grid.fromDepth(depth) ''' dij_solver = Dijkstra(occ_grid, current_pose, target_pose) moves = dij_solver.solve() for i in moves: pr.step() set_2d_pose(robot, (i[0], i[1], current_pose[2])) time.sleep(0.05) ''' ''' # RRT Solver
# Get Scene
SCENE_FILE = join(dirname(abspath(__file__)), 'scenes/scene_cpp.ttt')
# Start Simulation
pr = PyRep()
pr.launch(SCENE_FILE, headless=False)
pr.start()
robot = Shape('start_pose')
vision_sensor = VisionSensor('vision_sensor')
# Generate a random map
randomMapGenerator.generate_random_map(no_of_objects, False)
# Setup occ_grid
occ_grid = OccupancyGrid()
setupOccGrid(occ_grid, vision_sensor)
# Compute block size from shape in simulation
bounding_box = robot.get_bounding_box()
block_size_x = int(
round(bounding_box[1] - bounding_box[0], 3) / occ_grid.resolution)
block_size_y = int(
round(bounding_box[3] - bounding_box[2], 3) / occ_grid.resolution)
submapper = GridSubmapper(occ_grid)
submapper.process(block_size_x, block_size_y)
planner = SubmapPlanner(occ_grid, block_size_x, block_size_y)
cur_pos = planner.getPath(submapper.submaps, only_start=True)
# Set numpy printing options
np.set_printoptions(threshold=100 * 100,
formatter={'all': lambda x: str(x) + ','})
# Get Scene
SCENE_FILE = join(dirname(abspath(__file__)),
'scenes/scene_nonrectilinear.ttt')
# Start Simulation
pr = PyRep()
pr.launch(SCENE_FILE, headless=True)
pr.start()
# Setup occ_grid
vision_sensor = VisionSensor('vision_sensor')
occ_grid = OccupancyGrid()
setupOccGrid(occ_grid, vision_sensor)
pr.step()
# End Simulation
pr.stop()
pr.shutdown()
# Process
img_grey = np.uint8(occ_grid.grid * 255)
contours, hierarchy = cv2.findContours(img_grey, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
img_color = cv2.cvtColor(img_grey, cv2.COLOR_GRAY2BGR)
intermediate_step = img_color.copy()
def __init__(
self,
cols,
rows,
widthMM,
heightMM,
# the x,y and th of the current location can be passed
currentRow=-1,
currentCol=-1,
currentTh=0,
goalRow=-1,
goalCol=-1,
gridUpdate="",
image=None,
grid=None,
gridResize=1.0,
showIter=0,
title="",
):
self.centros = []
self.markerToolkit = MarkerToolkit()
self.x_localizeMM = 0
self.y_localizeMM = 0
self.thr_localize = 0
self.x_last_marker_localizeMM = 0
self.x_last_marker_robotMM = 0
self.y_last_marker_localizeMM = 0
self.y_last_marker_robotMM = 0
self.thr_last_marker_localize = 0
self.thr_last_marker_robot = 0
self.resetMarkers()
OccupancyGrid.__init__(
self,
cols=cols,
rows=rows,
widthMM=widthMM,
heightMM=heightMM,
currentRow=currentRow,
currentCol=currentCol,
currentTh=currentTh,
goalRow=goalRow,
goalCol=goalCol,
image=image,
grid=grid,
gridResize=gridResize,
showIter=showIter,
title=title,
)
self.distanceToWall = [[self.infinity for col in range(self.cols)] for row in range(self.rows)]
# Todos los bloques ocupados forman parte de una pared y por tanto
# se les asigna distancia 0 a las paredes.
for row in range(self.rows):
for col in range(self.cols):
if self.grid[row][col] > self.threshhold:
self.distanceToWall[row][col] = 0
# Inicia perceptrón con los pesos calculados en la parte1. Si no se desea usar, comentar
# esta sección de código.
self.pesos = [
-0.25830320437434307,
1.160526501403915,
-0.15551005292679421,
1.2578377038143795,
-0.55707372041035363,
-2.9552247878192888,
-1.6809527155003046,
-2.2014578204055359,
-13.0,
0.0,
]
self.calculadorNeuronal = PerceptronMonocapa(self.pesos, w2)