AI Vision Robot Tank Kit This AI Vision Robot Tank Kit provides a practical, allinone platform for learning and developing robotics with Python and ROS. It combines rugged hardware and integrated software tools to support handson education, prototyping, and research without requiring prior expertise. Key benefits Learn robotics and Python by doing. The kit supports ROS workflows and JupyterLab for stepbystep coding, simulation, and realworld testing. Rapid prototyping and testing. Onboard processing, sensors, and motor control let you validate autonomous navigation and vision algorithms quickly. Versatile control options. Operate the robot from a mobile app, use handle control for FPV feedback, or run programs online via JupyterLab. Durable, modular design. The crawler platform and modular electronics make it easy to adapt, expand, and maintain. Main features Robust crawler design. Chassis made from green aluminum alloy oxide for durability and a professional finish. SLAM lidar. Realtime mapping and localization capability for autonomous navigation. Vision system. HD camera combined with a depth camera for object detection, obstacle avoidance, and scene understanding. torque gear motors. Drive system engineered for stable motion and precise control on tracked locomotion. Powerful robot expansion board. performance expansion board compatible with Yaboom for flexible I O and peripheral integration. Software and compatibility Python friendly. Designed for programmatic control and algorithm development in Python. ROS integration. Works with standard ROS tools including Rviz and MoveIt and supports Qt toolboxes for robot arm control and simulated environments. Simulation and visualization. Use Rviz and MoveIt for motion planning, testing, and realworld simulation before deploying code to hardware. Crossplatform control. Mobile app control, handlebased FPV operation, and JupyterLab online programming provide multiple development and operation workflows. Practical use scenarios Classroom and training. Teach SLAM, computer vision, motion planning, and ROS concepts with a tangible platform that reflects realworld robotics workflows. Research and prototyping. Validate autonomous navigation and perception algorithms using onboard lidar and depth sensing, then iterate quickly using simulation and JupyterLab. Hobbyist development and . Build custom behaviors, integrate sensors or manipulators, and test arm control and motion planning with Rviz and MoveIt. What this kit solves Removes hardware and integration barriers so learners and developers can focus on algorithms and system behavior. Provides a single platform for learning endtoend robotics workflows from simulation to realworld deployment. Supports multiple user skill levels with visual tools, app control, and programmatic access through Python and ROS. This kit is suitable for hobbyists, educators, and professional developers who need a robust, extensible platform for learning and developing robotics applications. Start developing vision, navigation, and manipulation capabilities using an integrated hardware and software toolkit.