Micromouse Autonomous Robot

Overview

The Micromouse project involved building an autonomous maze-solving robot capable of navigating complex environments using computer vision and cloud computing capabilities. This project combined robotics, embedded systems, and cloud infrastructure to create an intelligent navigation system.

Project Background

Micromouse is a classic robotics challenge where small autonomous robots must solve a maze as quickly as possible. This implementation took a modern approach by leveraging:

• ROS2 (Robot Operating System 2) for modular robot software development
• Gazebo simulation for testing and development before physical deployment
• AWS Cloud computing for heavy computational tasks
• LiDAR-based 3D point cloud vision for environment perception

Technical Implementation

ROS2 Architecture

• Tailored the functionality of the robot by managing ROS2 packages
• Designed nodes for sensor processing, path planning, and motor control
• Implemented inter-process communication using ROS2 topics and services
• Developed launch files for coordinating multiple subsystems

Simulation Environment

• Created realistic maze environments in Gazebo simulator
• Configured physics parameters for accurate robot behavior modeling
• Enabled three-dimensional movement testing before hardware deployment
• Developed custom maze generation tools for varied testing scenarios

Perception System

• Integrated LiDAR sensor for 3D point cloud data acquisition
• Implemented real-time obstacle detection and mapping
• Developed localization algorithms for position tracking within the maze
• Created occupancy grid representations of the maze structure

Cloud Computing Integration

• Deployed computation-intensive algorithms on AWS Cloud servers
• Enabled offloading of path planning calculations to improve response time
• Implemented secure communication between robot and cloud services
• Utilized cloud resources for machine learning model inference

Maze Solving Algorithms

• Implemented flood-fill algorithm for optimal path discovery
• Developed wall-following strategies for initial maze exploration
• Created path optimization routines for minimizing solve time
• Designed adaptive algorithms that improve with each maze run

Technologies Used