AI Vision Robot Learning Platform with Jetson & ROS Support | Autonomous Navigation with Camera, LiDAR & IMU Sensors | Dual Controller Design with ESP32 & Micro AI Module | Supports Object Recognition, SLAM Mapping & Path Planning for STEM & AI Research

This AI-powered educational robot platform combines computer vision, autonomous navigation, and embedded programming into one hands-on learning system for students and researchers. Built on a powerful Jetson-based core and ESP32 wireless controller, it supports real-time video processing, object tracking, and SLAM mapping within the ROS ecosystem.

Learners can explore AI concepts such as neural network inference and visual perception through Python and C++ coding. The platform includes a wide-angle HD camera, LiDAR scanner, and IMU motion sensor for environment recognition and localization. Through ROS topics and nodes, students visualize live telemetry and motion paths in RViz or simulate algorithms in Gazebo before field testing.

The dual-controller architecture separates low-level motion control (ESP32) from AI computation (Jetson core), ensuring real-time response and system stability. LiDAR data and camera streams are fused to enable intelligent obstacle avoidance and path planning. Students can train and deploy TensorFlow Lite or PyTorch models for tasks like color detection, face tracking, and line following.

The open-source hardware and software architecture encourages experimentation and modular expansion. Users can add ultrasonic sensors, robotic arms, or IoT modules to create integrated smart systems linking AI vision and cloud communication. All modules connect via plug-and-play interfaces, eliminating soldering and reducing setup time.

In STEM education contexts, teachers use the platform to demonstrate autonomous driving, path tracking, and sensor fusion principles. In research settings, it serves as a testbed for AI algorithms, embedded vision, and human–robot interaction. The compact chassis and high-precision motors provide stable movement and long-term durability, suitable for both classroom and lab environments.

By integrating AI processing, ROS communication, and real-world mobility, this robot offers a complete journey from code to motion to intelligence — bridging the gap between machine learning theory and applied robotics engineering.