Autonomous Ackermann Steering Robot Platform | ROS2 AI Navigation System with LiDAR Mapping, Camera Vision & SLAM | Dual Control with Arduino & ESP32 | Supports Path Planning & Obstacle Avoidance | Python & C++ STEM Robot for AI Research

The autonomous robot learning platform is built around the Ackermann-steering concept, reproducing the kinematics of real self-driving vehicles. Using the ROS 2 ecosystem, it provides an open-source educational environment that bridges mechanical control, sensor fusion, and AI decision making. Students and researchers can explore autonomous navigation, path tracking, and environment mapping through realistic projects.

The system features a dual-processor architecture combining Arduino for low-level motor control and ESP32 for high-speed wireless communication. This design enables real-time coordination between steering and propulsion modules while transmitting telemetry through Wi-Fi. Equipped with a precision Lidar sensor, IMU unit, and HD camera, the platform performs SLAM mapping, visual tracking, and obstacle avoidance simultaneously within ROS 2 nodes.

By writing control scripts in Python or C++, learners gain hands-on experience with ROS topics, services, and launch files. The pre-configured workspace includes navigation stacks and sample algorithms for PID control and trajectory generation. Data from Lidar and camera streams can be visualized in RViz, while motion models are tested in Gazebo simulation before real-world deployment.

The Ackermann steering mechanism teaches students the geometry of turning radii, differential constraints, and vehicle stability at various speeds. Unlike differential or Mecanum systems, this configuration closely represents automotive motion, making it ideal for autonomous driving research and university courses in robotics and mechanical engineering.

Learners can experiment with sensor fusion algorithms combining Lidar distance data and visual features to improve localization accuracy. The ESP32 processor handles cloud connectivity through MQTT or HTTP protocols, enabling IoT projects like remote data logging or fleet monitoring. Educators use this platform to demonstrate concepts of path planning (A*, Dijkstra, and DWA methods) and real-time feedback loops between control and sensing systems.

All components are modular and clearly labeled, supporting plug-and-play assembly without soldering. The documentation includes schematics, flowcharts, and example codes to help students build from basic manual control to fully autonomous navigation. A metal chassis ensures durability for classroom experiments, and the open architecture allows integration with AI vision modules and extra sensors for advanced projects.

Through practical learning, students understand real-world engineering principles of control theory, data fusion, and embedded programming while preparing for careers in autonomous systems and AI development. This platform creates a complete learning cycle — from code to motion to intelligence — turning robotics education into an immersive, research-grade experience.