Smart Vacuum Robot

Autonomous indoor cleaning robot with obstacle avoidance and computer vision using Raspberry Pi

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🟣 Overview

This project focuses on the design and implementation of a smart vacuum robot capable of autonomous indoor navigation and cleaning. The system is built on a Raspberry Pi and uses a finite state machine (FSM) with OE/OS architecture to control movement, obstacle avoidance, and vacuum operation.

Ultrasonic sensors provide real-time obstacle detection, while a camera module captures environmental snapshots during idle states. The robot demonstrates FSM-based decision-making, sensor fusion, and real-time motor control for autonomous cleaning.

🟣 Key Features

• OE/OS-based finite state machine control architecture
• Autonomous indoor navigation and cleaning
• Ultrasonic sensor-based obstacle detection (front + left)
• Real-time obstacle avoidance with recovery state
• PWM-controlled vacuum motor system
• Camera snapshot capture during idle state
• Time-based cleaning cycle (5s idle, 30s cleaning)
• Raspberry Pi GPIO-based motor control system

🔧 Hardware Components

Component	Model	Quantity
Microcontroller	Raspberry Pi 5	1
Camera Module	Pi Camera v2	1
Motor Driver	L298N H-Bridge	1
DC Motors	Gear Motors	2
Ultrasonic Sensor	HC-SR04	2
Vacuum Motor	DC Motor (PWM controlled)	1
Power Supply	7.4V Battery Pack	1

🟣 Pin Mapping

Component	GPIO Configuration
Front Ultrasonic Sensor	Trigger: GPIO4, Echo: GPIO17
Left Ultrasonic Sensor	Trigger: GPIO22, Echo: GPIO27
Left Motor	Forward: GPIO14, Backward: GPIO15
Right Motor	Forward: GPIO18, Backward: GPIO23
Vacuum Motor	PWM: GPIO24

🟣 Finite State Machine (FSM)

The system is implemented using a deterministic OE/OS finite state machine with explicit transition states for obstacle handling.

FSM Diagram

States

• OE_idle

  • Stops all motors
  • Turns vacuum OFF
  • Transitions immediately to OS_idle

• OS_idle

  • Captures environment image (idle.jpg)
  • Waits 5 seconds
  • Transitions to OE_cleaning

• OE_cleaning

  • Activates vacuum motor
  • Starts forward movement
  • Transitions to OS_cleaning

• OS_cleaning

  • Continuous forward navigation
  • Monitors ultrasonic sensors
  • Obstacle detected → OE_obstacle_avoidance
  • After 30 seconds → OE_idle

• OE_obstacle_avoidance

  • Moves backward briefly (0.4s)
  • Transitions to OS_obstacle_avoidance

• OS_obstacle_avoidance

  • Executes right turn (0.5s)
  • Transitions to OE_post_avoidance

• OE_post_avoidance

  • Resumes forward motion
  • Returns to OS_cleaning

🟣 Control Logic Summary

• FSM controls all robot behavior transitions
• Ultrasonic sensors continuously monitor obstacle distance
• If distance < 0.25m → obstacle avoidance sequence is triggered
• Idle state captures image and waits 5 seconds before cleaning
• Cleaning cycle runs for 30 seconds before reset
• Post-avoidance state ensures stable recovery to cleaning mode

🟣 System Behavior Summary

• Fully autonomous indoor cleaning operation
• Real-time obstacle detection and avoidance
• Structured OE/OS FSM ensures predictable behavior
• Camera-based environmental snapshot logging
• Motor control managed via L298N driver
• Stable time-controlled cleaning cycles

🟣 Future Improvements

• Improve obstacle detection accuracy using sensor fusion filtering
• Add mapping or SLAM-based navigation
• Optimize path planning for full room coverage
• Replace reactive avoidance with intelligent navigation algorithm
• Integrate WiFi-based remote monitoring dashboard

🟣 Technologies Used

• Python 3
• gpiozero library
• picamera2
• Raspberry Pi OS
• HC-SR04 Ultrasonic Sensors
• L298N Motor Driver