Formas de se fazer um seguidor de linha no lego mindstorms 51515
Neste post, iremos nos aprofundar em duas maneiras as quais podemos utilizar para fazer um seguidor de linha no lego mindstorms robot inventor(51515).
A primeira delas trata-se da mais convencional, utilizar as leituras do sensor de cor que vem no kit para fazer seu robô seguir a linha. Embora seja um meio bastante simples e rápido de fazer um seguidor de linha, ele apresenta algumas desvantagens, como o fato de não ser tão rápido ou eficiente, por exemplo.
Código utilizado para esse modelo:
Demonstração do modelo:
A outra forma de construir um seguidor de linha nesta plataforma é utilizando a câmera huskylens, da Dfrobot. Trata-se de uma câmera bastante fácil de se programar, mas ainda requer alguns conhecimentos a mais, em relação ao modelo anterior. Ela é bem mais precisa e funciona de forma mais suave, mas requer uma fonte externa para utilizá-la.
Para utilizá-la, instale a biblioteca pyhuskylens.py no python do app lego:
import ubinascii, uos, machine,uhashlib
from ubinascii import hexlify
b64="""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
"""
def calc_hash(b):
return hexlify(uhashlib.sha256(b).digest()).decode()
# this is the hash of the compiled mpy
hash_gen='3af861ba999a2843b8873036adb9cbca23d288a637a3cec237b68da3cddd34b0'
pyhuskylens=ubinascii.a2b_base64(b64)
hash_initial=calc_hash(pyhuskylens)
try: # remove any old versions of pyhuskylens library
uos.remove('/projects/pyhuskylens.py')
uos.remove('/projects/pyhuskylens.mpy')
except OSError:
pass
print('writing pyhuskylens.mpy to folder /projects')
with open('/projects/pyhuskylens.mpy','wb') as f:
f.write(pyhuskylens)
print('Finished writing pyhuskylens.mpy.')
print('Checking hash.')
pyhuskylens_check=open('/projects/pyhuskylens.mpy','rb').read()
hash_check=calc_hash(pyhuskylens_check)
print('Hash generated: ',hash_gen)
error=False
if hash_initial != hash_gen:
print('Failed hash of base64 input : '+hash_initial)
error=True
if hash_check != hash_gen:
print('Failed hash of .mpy on SPIKE: '+hash_check)
error=True
if not error:
print('pyhuskylens library written succesfully. Resetting....')
machine.reset()
else:
print('Failure in pyhuskylens library!')
feito isso, apague este programa e rode o código do seguidor de linha:
from mindstorms import MSHub, Motor, MotorPair, ColorSensor, DistanceSensor, App
from mindstorms.control import wait_for_seconds, wait_until, Timer
from mindstorms.operator import greater_than, greater_than_or_equal_to, less_than, less_than_or_equal_to, equal_to, not_equal_to
import math
from projects.pyhuskylens import (HuskyLens,
ALGORITHM_FACE_RECOGNITION,
ALGORITHM_FACE_RECOGNITION,
ALGORITHM_OBJECT_TRACKING,
ALGORITHM_OBJECT_RECOGNITION,
ALGORITHM_LINE_TRACKING,
ALGORITHM_COLOR_RECOGNITION,
ALGORITHM_TAG_RECOGNITION,
ALGORITHM_OBJECT_CLASSIFICATION,
ALGORITHM_QR_CODE_RECOGNITION,
ALGORITHM_BARCODE_RECOGNITION,
ARROWS, # key for get() dict
BLOCKS, # key for get() dict
FRAME, # key for get() dict
clamp_int)
### huskylens usage ###
# get_blocks(ID=None, learned=False) returns a list of block objects
# blocks have x, y , width , height, ID properties.
# get_arrows(ID=None, learned=False) returns a list of arrow objects
# arrows have x_tail, y_tail , x_head , y_head, ID and direction properties
# get(ID=None, learned=False) returns a dict with blocks, arrows & frame
# on firmware 0.5+ you also have these class methods available:
# show_text("text", position=(10,10))
# clear_text()
# Create your objects here.
ms_hub = MSHub()
hl = HuskyLens('B', baud=9600)
ma = Motor('D')
mb = Motor('A')
# Write your program here.
ms_hub.speaker.beep()
# Get direction of a learned arrow
print("Starting line tracking")
hl.set_alg(ALGORITHM_LINE_TRACKING)
while not ms_hub.left_button.is_pressed():
arrows = hl.get_arrows(learned=False)
if len(arrows) > 0:
direction = ((arrows[0].x_head+arrows[0].x_tail)/2 - 160)/-6
ma.start_at_power(clamp_int(30+direction))
mb.start_at_power(clamp_int(-30+direction))
else:
ma.start_at_power(0)
mb.start_at_power(0)
ma.start_at_power(0)
mb.start_at_power(0)
Vídeo demonstrando o modelo:
Agora que mostramos as diferentes formas de se fazer um seguidor de linha com o lego mindstorms, cabe a você escolher qual utilizar em seus projetos!
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