Compact, durable robot tank platform designed for development, testing, and realworld deployment. The green aluminum alloy oxide chassis provides a lightweight but sturdy base for mobility, sensing, and expansion. Key benefits Reliable offroad mobility: Crawler design plus torque gear motors deliver stable traction and precise motion in uneven terrain, reducing time spent tuning locomotion. Accurate mapping and perception: SLAM lidar together with HD and depth cameras enable robust localization, obstacle detection, and 3D scene understanding for navigation and inspection tasks. Ready for development and simulation: Native Python and ROS support with Rviz, MoveIt, and Qt toolboxes allows fast prototyping, robotic arm control, and realistic simulation of realworld scenarios. Flexible hardware expansion: Onboard robot expansion board supports sensor and actuator integration so you can add custom modules or control external devices without redesigning the platform. Multiple control options: Operate the robot through a mobile app, a handheld controller for FPV operation, or JupyterLab for online programming and algorithm testing. Technical highlights Material: green aluminum alloy oxide chassis for a balance of strength and weight Sensing: SLAM lidar, definition camera, and depth camera for complementary perception Actuation: torque gear motors for reliable drive performance Control and expansion: integrated robot expansion board for additional sensors and actuators Software compatibility: Python and ROS with Rviz, MoveIt, and Qt toolboxes; supports JupyterLab for online development Control interfaces: mobile app, handheld controller for FPV, and direct programming interfaces How this product solves common problems Reduces integration time by combining core perception, actuation, and control hardware on one platform Improves mapping and autonomy development with synchronized lidar and depth imaging for accurate environment models Simplifies testing and demonstration through multiple control modes and builtin support for common robotic toolchains Practical use scenarios Autonomous mapping and navigation research: Use SLAM lidar and depth sensing to develop and validate navigation algorithms in indoor and outdoor environments. Field inspection and monitoring: Deploy the crawler to inspect uneven terrain, infrastructure, or confined spaces while streaming HD and depth data for remote assessment. Education and prototyping: Teach ROSbased robotics, robotic arm control, and perception pipelines using Rviz and MoveIt, with handson JupyterLab programming for exercises and experiments. This platform is suited for developers, researchers, and educators who need a readytouse, extensible robot base that combines robust mobility, advanced sensing, and full compatibility with industrystandard robotic software tools.