⚡ Future Innovators ⚡

class Robot: def __init__(self, name, battery=100): self.name = name self.battery = battery self.position = [0, 0] def move(self, x, y): if self.battery > 10: self.position[0] += x self.position[1] += y self.battery -= 5 return f"Moved to {self.position}" else: return "Low battery!" def scan_environment(self): # Simulate sensor data import random obstacles = random.randint(0, 5) return f"Detected {obstacles} obstacles" # Create and control robot my_robot = Robot("Explorer-7") print(my_robot.move(10, 5)) print(my_robot.scan_environment())

# Raspberry Pi Robot Control import RPi.GPIO as GPIO import time from picamera import PiCamera class AdvancedRobot: def __init__(self): GPIO.setmode(GPIO.BCM) self.camera = PiCamera() self.setup_sensors() def autonomous_navigation(self): while True: distance = self.read_ultrasonic() if distance < 20: self.avoid_obstacle() else: self.move_forward() time.sleep(0.1)

# Advanced Computer Vision with OpenCV import cv2 import numpy as np from tensorflow import keras class VisionSystem: def __init__(self): self.face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') self.model = keras.models.load_model('object_detection_model.h5') def detect_and_classify(self, frame): # Real-time object detection faces = self.face_cascade.detectMultiScale(frame, 1.3, 5) for (x, y, w, h) in faces: cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2) return frame, len(faces)

# Competition Problem: Shortest Path Algorithm def dijkstra_algorithm(graph, start_node): """ Implement Dijkstra's algorithm for shortest path Time Complexity: O((V + E) log V) Space Complexity: O(V) """ import heapq distances = {node: float('infinity') for node in graph} distances[start_node] = 0 priority_queue = [(0, start_node)] while priority_queue: current_distance, current_node = heapq.heappop(priority_queue) if current_distance > distances[current_node]: continue for neighbor, weight in graph[current_node].items(): distance = current_distance + weight if distance < distances[neighbor]: distances[neighbor] = distance heapq.heappush(priority_queue, (distance, neighbor)) return distances