Overview A rugged tracked robot platform built from green aluminum alloy oxide for durability and a low visual profile. The crawler chassis is compact and stable, designed for navigation, perception, and manipulation projects where reliable hardware and open control are required. Key benefits Ready for realworld robotics development: SLAM lidar and depth sensing let the platform map and navigate complex environments autonomously. Strong, durable construction: aluminum alloy oxide body provides a lightweight but sturdy frame suited to field use and repeated handling. performance mobility: torque gear motors deliver traction and precise motion control for traversing uneven surfaces and carrying payloads. Flexible expansion and integration: a powerful robot expansion board provides interfaces for sensors, actuators, and custom electronics. Open software support: native support for Python and ROS workflows accelerates development, testing, and deployment using familiar tools. What makes it stand out Integrated perception stack with SLAM lidar plus HD and depth cameras for simultaneous mapping, obstacle detection, and visual tasks. Full ROS compatibility using Rviz, Movelt, and Qt toolboxes for motion planning, visualization, and user interface development. Multiple control paths: mobile app for quick field control, handheld controller for FPV operation, and JupyterLab for interactive online programming and experiments. Key attributes Function: autonomous navigation, remote operation, and manipulation testing. Material: green aluminum alloy oxide chassis for strength and lightweight handling. Performance: torque gear motors for reliable traction and stable motion control. Compatibility: supports Python and ROS ecosystems, Rviz, Movelt, Qt, and JupyterLab for development and simulation. Size: compact tracked platform designed for portability and deployment in indoor and outdoor scenarios. Typical use scenarios Research and prototyping: rapid development of SLAM, perception, and navigation algorithms using ROS toolchains and visual debugging in Rviz and Movelt. Inspection and mapping: deploy the crawler to inspect confined areas or create accurate maps of indoor spaces using lidar and depth cameras. Education and training: handson learning for robotics courses and workshops with mobile app control, FPV operation, and JupyterLab programming exercises. What you get A complete crawler platform that combines robust mechanical design with advanced sensing and open software integration. Ideal for engineers, researchers, and educators who need a dependable base for developing autonomous navigation, vision processing, and robotic arm simulation workflows.