Autonomous Navigation Robot

Robotics Personal Project

Overview

A mobile robot capable of autonomous navigation using ROS and computer vision for obstacle detection and path planning.

Technologies Used

  • ROS (Robot Operating System)
  • Python
  • C++
  • OpenCV
  • LIDAR
  • Raspberry Pi

Links

Project Overview

This project involves building a mobile robot from scratch that can navigate autonomously in indoor environments. The robot uses a combination of LIDAR sensors and computer vision to detect obstacles and plan optimal paths to its destination.

Key Features

  • Autonomous Navigation: Uses ROS navigation stack for path planning and obstacle avoidance
  • Real-time Obstacle Detection: Implements computer vision algorithms to detect and classify obstacles
  • SLAM Integration: Simultaneous Localization and Mapping for environment understanding
  • Remote Monitoring: Web interface for monitoring robot status and sending commands

Technical Implementation

Hardware Components

  • Raspberry Pi 4 as the main controller
  • RPLIDAR A1 for 2D mapping
  • Raspberry Pi Camera Module for vision
  • DC motors with encoders for precise movement
  • IMU sensor for orientation tracking

Software Architecture

The system is built on ROS (Robot Operating System) with several custom nodes:

  1. Sensor Node: Processes data from LIDAR and camera
  2. Navigation Node: Implements path planning algorithms
  3. Control Node: Manages motor control and movement execution
  4. Vision Node: Runs OpenCV-based object detection

Challenges and Solutions

One of the main challenges was integrating multiple sensor inputs in real-time while maintaining responsive control. This was solved by implementing a multi-threaded architecture with separate ROS nodes for each sensor, allowing parallel processing.

Results

The robot successfully navigates through complex indoor environments, avoiding both static and dynamic obstacles. It can autonomously reach predefined waypoints with an accuracy of ±10cm.

Future Improvements

  • Integration of deep learning for improved object recognition
  • Multi-robot coordination capabilities
  • Enhanced localization using visual-inertial odometry