AI ROS2 Tracked Robot with Jetson Nano & LiDAR SLAM Navigation | Autonomous AI Vision Platform for STEM & Research Learning | Supports ROS2 Foxy, OpenCV & TensorFlow for Object Detection, Mapping & Path Planning | Wi-Fi Control System

AI ROS2 tracked robot redefines autonomous navigation and AI education through a complete hardware-software integration of Jetson Nano GPU computing and ROS 2 framework. AI robot combines LiDAR SLAM mapping, AI vision recognition, and dual-track mobility for stable movement across varied terrains. STEM students learn ROS 2 node communication, sensor fusion, and real-time path planning in a hands-on environment.

AI vision system uses HD camera and OpenCV/TensorFlow to detect objects and track motion in real time. LiDAR sensor scans the environment to build precise 2D/3D maps for SLAM localization and obstacle avoidance. The IMU and wheel encoders provide orientation and odometry feedback for smoother autonomous control. ROS 2 middleware manages distributed data exchange between nodes to enable synchronized operation of vision, motion and navigation modules.

AI computing powered by Jetson Nano GPU supports deep-learning inference for object classification and decision making. Students learn how AI algorithms interact with real-world sensors to create intelligent robotic behavior. Wi-Fi and Bluetooth modules allow remote monitoring and IoT integration for data logging and AIoT projects. STEM robot curriculum covers Python and C++ coding for ROS 2 nodes, topic creation, and service design.

Tracked mobility design uses dual rubber treads with high-torque motors and PID control for stability and precision. The low center of gravity and robust suspension make it ideal for research labs and educational environments. AI students learn to implement autonomous driving algorithms, sensor fusion, and mapping optimization within ROS 2 simulation tools such as Gazebo and RViz.

Educational institutions use this autonomous AI robot for navigation experiments, AI vision training, and research on machine learning-based path planning. Its open-source architecture encourages innovation and customization, enabling students to add sensors or refine AI models for advanced autonomous applications. AI robotics education bridges the gap between mechanics, electronics, and software engineering in one complete platform.

1️⃣ AI Vision and LiDAR SLAM Fusion
AI robot combines camera and LiDAR data for simultaneous mapping and object detection. Students learn environment perception and path planning with real-time sensor fusion.

2️⃣ ROS 2 Programming and Node Architecture
ROS 2 teaches next-generation distributed communication with Data Distribution Service (DDS). Students build nodes, topics, and services for sensor control and actuator management.

3️⃣ Jetson Nano GPU for AI Computation
Jetson Nano runs deep-learning models locally using TensorFlow and PyTorch for object tracking and classification.

4️⃣ Wireless Connectivity and AIoT Integration
Wi-Fi and Bluetooth enable data exchange and remote control. Students learn AIoT concepts and robotic telemetry applications.

5️⃣ Open-Source STEM Platform for Education and Research
Open-source hardware and ROS 2 software allow universities to expand projects and develop custom autonomous systems.

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