Macro-Quantum Interaction Node (MQIN) Module

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MQIN Autonomous System - Object Inversion (Glass)

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This system demonstrates:

- Vision-based object detection (simulated)

- AI decision-making (target + action validation)

- Serial communication with Arduino

- Physical execution (robotic arm)

- Feedback loop (adaptive behavior)

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import serial
import time
import random

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SERIAL CONNECTION (Arduino)

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arduino = serial.Serial('COM3', 9600)
time.sleep(2)  # Allow Arduino to initialize

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VISION MODULE (SIMULATED)

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def detect_objects():
"""
Simulates detection of objects in the workspace.
In real implementation: OpenCV / ESP32 camera stream.
"""
return ["glass", "cube", "tool"]

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TARGET SELECTION

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def select_target(objects):
"""
Prioritizes the glass object for inversion task.
"""
if "glass" in objects:
return "glass"
return random.choice(objects)

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ACTION PLANNING

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def plan_action(target):
"""
Defines the action based on the selected object.
"""
if target == "glass":
return "INVERT_GLASS"
return "IDLE"

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QUANTUM-INSPIRED EVALUATION

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def evaluate_action(action):
"""
Evaluates whether executing the action is beneficial.
Represents uncertainty reduction logic.
"""
score = random.uniform(0, 1)
print(f"[AI] Action score: {score:.2f}")
return score > 0.4  # threshold

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COMMAND TRANSMISSION

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def send_command(command):
"""
Sends command to Arduino controller.
"""
print(f"[SYSTEM] Sending command: {command}")
arduino.write((command + "\n").encode())

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FEEDBACK SYSTEM

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history = []

def update_feedback(success):
"""
Stores execution results and computes performance.
"""
history.append(success)
rate = sum(history) / len(history)
print(f"[AI] Success rate: {rate:.2f}")

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MAIN AUTONOMOUS LOOP

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while True:
print("\n=== New Cycle ===")

# Step 1: Detect environment
objects = detect_objects()
print(f"[VISION] Objects detected: {objects}")

# Step 2: Select target
target = select_target(objects)
print(f"[AI] Target selected: {target}")

# Step 3: Plan action
action = plan_action(target)
print(f"[AI] Planned action: {action}")

# Step 4: Evaluate action before execution
if evaluate_action(action):

    # Step 5: Execute action
    send_command(action)

    # Step 6: Simulate execution result
    # In real system → camera verifies object orientation
    success = random.choice([0, 1])
    print(f"[RESULT] Execution success: {success}")

else:
    success = 0
    print("[AI] Action rejected (low score)")

# Step 7: Update feedback loop
update_feedback(success)

time.sleep(3)

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ARDUINO LOGIC (REFERENCE IMPLEMENTATION)

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Expected behavior on Arduino side:

- Listen for incoming serial commands

- Execute predefined motion sequences

Example:

if (cmd == "INVERT_GLASS") {

// Step 1: move arm above object

// Step 2: lower arm

// Step 3: close gripper

// Step 4: lift object

// Step 5: rotate wrist 180 degrees

// Step 6: place object upside down

}

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