# AI-Powered Drone Simulation (ESP32 + TinyML)
# Simulating AI object tracking and motor control

import time
import random

# Simulating AI object tracking with predefined data
class AIObjectTracker:
    def __init__(self):
        self.target_position = 50  # Simulated object position (0-100)

    def get_object_position(self):
        # Simulating AI detecting object movement
        self.target_position += random.randint(-5, 5)
        self.target_position = max(0, min(100, self.target_position))
        return self.target_position

# Simulated PID Controller for stabilization
class PIDController:
    def __init__(self, kp, ki, kd):
        self.kp = kp
        self.ki = ki
        self.kd = kd
        self.prev_error = 0
        self.integral = 0

    def compute(self, setpoint, measured_value):
        error = setpoint - measured_value
        self.integral += error
        derivative = error - self.prev_error
        self.prev_error = error
        return self.kp * error + self.ki * self.integral + self.kd * derivative

# Simulated Drone Motors
class DroneMotors:
    def __init__(self):
        self.speed = 50  # Base motor speed (0-100)

    def adjust_speed(self, correction):
        self.speed = max(0, min(100, self.speed + correction))
        print(f"Motor Speed Adjusted: {self.speed}%")

# Initialize AI, PID, and Motors
ai_tracker = AIObjectTracker()
pid = PIDController(1.0, 0.1, 0.05)
motors = DroneMotors()

# Simulation Loop
print("Starting AI-powered Drone Simulation...")
for _ in range(20):  # Simulating 20 frames
    object_position = ai_tracker.get_object_position()
    print(f"Detected Object Position: {object_position}")
    
    correction = pid.compute(50, object_position)  # Target is center (50)
    motors.adjust_speed(correction)
    
    time.sleep(0.5)  # Simulated time delay