MODEL DESCRIPTION the code is work progress i will deploy it soon

The "Explorer AMR" - ROS 2 SLAM Platform
Welcome to the Explorer AMR (Autonomous Mobile Robot) – my comprehensive graduation project designed to master Simultaneous Localization and Mapping (SLAM) and navigation in unknown environments.

This is not a simple shell; it is a complete, open-source robotics platform that bridges high-level autonomy software with robust, functional 3D-printed hardware. It is designed around the widely used ROS 2 (Robot Operating System) ecosystem, making it an ideal educational tool for universities, makers, and students of advanced robotics.

DESIGN CONCEPTS & SYSTEMS ARCHITECTURE

The Explorer AMR is engineered with two core design pillars: modularity and high-performance sensor integration. The sleek, functional matte-white shell is a structural chassis, not just a covering.

System Core: Autonomous intelligence is powered by a Raspberry Pi 5, which manages the heavy ROS 2 Nav2 stack. A low-level ESP32 handles real-time wheel odometry and motor control, communicating via micro-ROS.
Sensor Fusion (SLAM): The robot perceives its world using a top-mounted LiDAR unit (for mapping) and a front-facing Pi Camera (for visual data). An integrated MPU 6050 IMU provides precise orientation data, fusing with the 4WD encoder data for robust localization.
Aesthetic & Ventilation: The distinctive front fascia, marked with characteristic "X" and "<" designs near the camera lens, houses dual proximity sensor cutouts with hexagonal ventilation mesh for active internal cooling.
Drivetrain: A robust 4WD system using high-torque JGA25-370 DC motors allows the robot to navigate real-world indoor surfaces.

KEY USES & APPLICATIONS
This platform is a versatile educational and prototyping AMR.

Autonomous Mapping (SLAM): The primary use (demonstrated in image_14.png) is generating accurate 2D and 3D floor maps of previously unvisited indoor spaces, navigating complex layouts dynamically.
Educational ROS 2 Learning: Perfect for learning ROS 2 Nav2, AMCL, and Gmapping.
Prototype for Service Robots: The chassis can easily be extended (via top-mounting holes) to test configurations for delivery, environmental monitoring, or telepresence.
Indoor Exploration: Use it to automate environmental data collection, security patrols, or multi-room navigation testing.

NTERNAL HARDWARE BOM (Reference Drawing
1-Raspberry Pi 5 (The Brain)
2-ESP32 MCU (Motor & IMU interface)
3-RPLIDAR (or similar SLAM-capable LiDAR)
4-L298N Motor Drivers
5-MPU 6050 IMU
6-4x JGA25-370 DC Motors + 65mm Wheels
(Thank you for supporting this open-source graduation project! Good luck with your build!)

Autonomous Navigation Robot

0.2mm layer, 2 walls, 10% infill