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ADDINEDU ROS 개발 역량 강화를 위한 AI 자율주행 로봇 과정 5기

목차

  1. EDA
  2. SQL
  3. EDA_Project
  4. Git
  5. Arduino
  6. PyQt
  7. IoT_Project
  8. OpenCV
  9. Machine Learning
  10. You Only Look Once
  11. DeepLearning_Project
  12. ESP32
  13. ROS2
  14. docker
  15. ROS2 project
  16. extra curricular openRMF

EDA

파이썬 기초와 라이브러리 numpy, pandas
시각화 matplotlib, seaborn, folium, googlemaps
크롤링 beautifulSoup, selenium

EDA oneday 프로젝트

  • 빅뱅이론 정보 수집 [URL]
  • 주유소 정보 수집 [URL]
  • 주택청약 정보 수집 [URL]
for season in tqdm(seasons):
    time.sleep(3)
    episode_list = imdb.find_elements(By.CLASS_NAME,season_class[2])    # episodes

    for episode in episode_list :
        
        title   = episode.find_element(By.CLASS_NAME,info_class[0]).text                    # head: season, episode, title
        content = episode.find_element(By.CLASS_NAME,info_class[1]).text                    # content
        date    = episode.find_element(By.CLASS_NAME,info_class[2]).text                    # aired date
        review  = episode.find_element(By.CLASS_NAME,info_class[3]).text.split(sep="\n")    # review : ratings, votes
        
        if re.search(head_pattern, title) :         # data seperate
            head = re.match(r"S(\d+).E(\d+)", title)
            head_season = head.group(1)
            head_episode = head.group(2)
            title = title.split(sep=" ", maxsplit=2)[2]
        else :                                      # exception
            head_season = 12
            head_episode = 25
        date = convert_us_date_to_korean(date)
        rating  = review[0]; vote = re.search(rate_pattern, review[2]).group(1)

        # merge
        episode_info = [head_season, head_episode, title, rating, vote, date, content]
        episode_info_list.append(episode_info)
    season_info_list.append(episode_info_list)

 

 

 

SQL

DB 구축과 SQL 기초

SQL oneday 프로젝트

  • 스타벅스와 이디야의 지점 비교 [URL]
# 인접하지 않은 두 브랜드의 지점 위치
mapping = folium.Map(location=[37.53, 127], width="100%", height="100%", zoom_start=11.8)
namelist = distance[0].tolist()
for row in starbucks[0] :
    if row not in namelist :
        starbucks_far.append(row)

for idx, row in starbucks.iterrows():
    for name in starbucks_far :
        if row[0] == name :
            folium.CircleMarker( location=[row[1], row[2]], radius=10, fill=True, color='red', fill_color='red', tooltip=row[0]).add_to(mapping)
namelist = distance[1].tolist()
for row in ediya[0] :
    if row not in namelist :
        ediya_far.append(row)

for idx, row in ediya.iterrows():
    for name in ediya_far :
        if row[0] == name :
            folium.CircleMarker( location=[row[1], row[2]], radius=10, fill=True, color='red', fill_color='red', tooltip=row[0]).add_to(mapping)

 

 

 

EDA_Project

[EDA 프로젝트]
인기 롤 프레잉 게임인 리그 오브 레전드에서 유저들의 실력 향상을 위해 추천하는 방법에 대해
개발사에서 제공하는 openAPI를 활용하여 정보를 수집하고 통계를 내어 실제로 효과가 있는지 검증한다.

match_data = []
for matchIds in tqdm(matchId):
    for _matchId in matchIds:
        req = matchData(_matchId)
        if req.status_code == 200:
            data = req.json()
            match_data.append(data)
        else:
            print(req.status_code)
            break
        time.sleep(1.3)
len(match_data)

 

 

 

Git

commit, log, version, branch

 

 

 

Arduino

I/O, Timer, callback, register

 

 

 

PyQt

PyQt를 활용한 GUI에서 데이터베이스 호출, 쿼리 작성, 시각화와 아두이노 I/O 데이터 출력

 

 

 

IoT_Project

[IoT 프로젝트]
물류센터에서의 입고부터 출고, 고객 배송까지의 과정을 시스템을 구현
시스템 관리를 위한 GUI 제작, Log 기록을 통한 관리, 동작 구현과 유기적인 시스템 제작

 

 

 

openCV

openCV, image processing, mask, overlay, projection

 

 

 

Machine Learning

환경 설정 cuda, tensor flow
라이브러리 sklearn
데이터 전처리
모델 학습 cost function, logistic regression, decision tree, kNN, ensemble, clustering, pipe line, MNIST
Principal Component Analysis, eigen factor, image segment

머신러닝 oneday 프로젝트

  • 머신러닝 활용 [URL]
# 11. 다시 원본 데이터에서 데이터를 test와 train으로 나눈 후, Standard scaler와 Decision Tree를 연달아 사용하는 pipeline을 꾸며 주세요.
x = iris_df
y = iris.target

estimators = [('scaler', StandardScaler()), ('clf', DecisionTreeClassifier(max_depth=2, random_state=50))]
pipe = Pipeline(estimators)

scores_rf = cross_validate(pipe, x, y, scoring='accuracy', cv=5)
scores_rf

딥러닝
MNIST, CNN, VGG, Transfer learning

딥러닝 oneday 프로젝트

model = models.Sequential([
    layers.Conv2D(32, (3,3), activation='relu', input_shape=(128, 128, 3)),
    layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2)),
    layers.Dropout(0.24),

    layers.Conv2D(64, (3,3), activation='relu', padding='same'),
    layers.MaxPooling2D(pool_size=(2, 2)),
    layers.Dropout(0.25),

    layers.Conv2D(64, (3,3), activation='relu', padding='same'),
    layers.MaxPooling2D(pool_size=(2, 2)),
    layers.Dropout(0.25),

    layers.Flatten(),
    layers.Dense(512, activation='relu'),
    layers.Dropout(0.25),
    layers.Dense(2, activation='softmax')
])
model.summary()

 

 

 

YOLO

Segment, Tracking, Pose Estimation

 

 

 

DeepLearning_Project

  • 경로 추적 및 예측 [URL]
    detections = detect_objects(frame_rgb)
    dets = np.array([[x1, y1, x2, y2, conf] for x1, y1, x2, y2, conf in detections])
    tracked_objects = tracker.update(dets)

    for obj in tracked_objects:
        x1, y1, x2, y2, obj_id = obj[:5]
        center_x = (x1 + x2) / 2
        center_y = (y1 + y2) / 2
        current_pos = (center_x, center_y)

        prev_pos = previous_positions.get(obj_id, None)

        if prev_pos:
            speed, direction = calculate_speed_and_direction(prev_pos, current_pos, time_interval)
            cv2.putText(frame, f'ID: {int(obj_id)} Speed: {speed:.2f} Direction: {direction:.2f}',
                        (int(x1), int(y1) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)

        #draw_arrow_and_trajectory(frame, obj_id, center_x, center_y, prev_pos)

        previous_positions[obj_id] = current_pos
  • 해산물 분류 및 가격정보 제공 솔루션 [URL]  

 

 

ESP32

TCP 통신

 

 

 

ROS2

ROS2 기초와 통신
node, topic, service, action, publisher, subscriber, pkg, openCV

ROS2 oneday 프로젝트

class OnedayPen(Node) :
    def __init__(self) :
        super().__init__('turtlesim_subscriber')
        self.subscription = self.create_subscription(Pose, '/turtle1/pose', self.pen_callback, 10)
        self.subscription
        self.cli = self.create_client(SetPen, '/turtle1/set_pen')
        while not self.cli.wait_for_service(timeout_sec=1.0):
            self.get_logger().info('Service not available, waiting again...')
        self.pen_state = None

    def pen_callback(self, msg):
        if msg.x < 5 :
            if self.pen_state != 'red':
                self.pen_state = 'red'
                print(f"X is below 5: {msg.x}")
                self.set_pen(255, 0, 0)

 

 

 

docker

docker

 

 

 

ROS2 project

class MoveToGoalNode(Node):
    def __init__(self):
        super().__init__('move_to_goal_node')
        self.cmd_vel_publisher = self.create_publisher(Twist, '/base_controller/cmd_vel_unstamped', 10)
        self.amcl_subscriber = self.create_subscription(PoseWithCovarianceStamped, '/amcl_pose', self.amcl_pose_callback, 10)
        self.goal_x = 0.4565105916649657  # 목표 위치의 x 좌표
        self.goal_y = -0.3396688475363866  # 목표 위치의 y 좌표
        self.position = {'x': 0.0, 'y': 0.0, 'theta': 0.0}
        self.pid_linear = PID(P=1.0, I=0.0, D=0.0, max_state=1.0, min_state=-1.0, dt=0.1)
        self.pid_angular = PID(P=2.0, I=0.0, D=0.0, max_state=1.0, min_state=-1.0, dt=0.1)
        self.timer = self.create_timer(0.1, self.timer_callback)  # 0.1초마다 호출

openRMF

[URL]
traffic editor를 활용한 가상 환경 제작
시뮬레이션 환경에서 다수의 로봇 동작
Gazebo와 rviz에서 동작 확인

reference open-rmf : https://github.com/open-rmf
rmf-demos : https://github.com/open-rmf/rmf_demos
fleet-adpater : https://github.com/open-rmf/fleet_adapter_template

pinklab : https://github.com/pinklabstudio
minibot : https://github.com/pinklabstudio/pinklab_minibot_robot

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