AI ROS Mobile Robot with LiDAR SLAM & Jetson Nano Computing | Autonomous AI Platform for STEM Learning & Research | Supports OpenCV & TensorFlow for Vision Recognition, Object Detection & Path Planning | Wi-Fi Control & IMU Feedback System

AI mobile robot platform integrates ROS, SLAM, and AI vision to deliver a complete education and research solution for autonomous navigation and AI learning. AI robot combines Jetson Nano GPU processing and ESP32 real-time control to support SLAM mapping, path planning, and object recognition. SLAM robot collects LiDAR distance data and IMU feedback to generate precise 2D/3D maps for self-driving navigation experiments.

AI vision robot captures video through its HD camera and analyzes frames with OpenCV and TensorFlow for object and color detection. ROS software manages communication between sensors and controllers through nodes and topics, allowing students to visualize robot status in RViz and simulate autonomous behavior in Gazebo. Wi-Fi and Bluetooth modules provide real-time telemetry and remote control for IoT applications.

AI education robot teaches motion control through PID algorithms and encoder feedback. Students learn how to adjust velocity and heading for stable navigation on different surfaces. STEM curriculum includes Python and C++ coding for ROS nodes and machine-learning projects. AI learning robot supports voice commands and IoT integration for smart automation experiments.

AI autonomous platform uses a lightweight aluminum chassis and high-torque motors for durability and precision motion. IMU sensors monitor tilt and acceleration to ensure smooth movement and balance. AI computing on Jetson Nano allows students to deploy deep-learning models for real-time inference and object tracking. STEM robot system teaches sensor fusion, map optimization, and AI-driven decision logic within the ROS framework.

Educational institutions use this autonomous AI platform for AI robotics courses, navigation competitions, and research in autonomous systems. The open-source ROS architecture enables students to expand the system with new sensors and algorithms. AI robotics training bridges programming, electronics, and mechanics in a single hands-on STEM ecosystem.

1️⃣ AI Vision and SLAM Navigation Integration
AI vision robot combines camera and LiDAR data for environment mapping and object recognition. STEM students learn real-time perception and path optimization.

2️⃣ ROS Programming and Node Communication
ROS framework teaches data exchange between sensors and motors via topics and services. Students gain experience in Gazebo and RViz simulation.

3️⃣ Jetson Nano GPU Computing for AI Learning
AI processing runs TensorFlow models locally for deep-learning applications in vision and navigation.

4️⃣ Wireless Control and IoT Connectivity
Wi-Fi and Bluetooth enable remote monitoring and AIoT experiments for smart robotics education.

5️⃣ STEM Open-Source Research Platform
Open-source design encourages innovation in AI programming and autonomous systems. Ideal for universities and labs.