Robust crawler chassis and precision sensing for flexible robotics projects This robot tank combines a rugged crawler design with advanced sensing and control to support research, development, education, and field inspection tasks. The chassis is crafted from green aluminum alloy oxide for a durable, lightweight platform that adapts to uneven terrain and repeated use. Key benefits Durable, versatile platform: Crawler tracks and an aluminum alloy oxide body provide stability and longterm durability for indoor and outdoor testing. Accurate mapping and perception: Builtin SLAM lidar plus HD and depth cameras enable realtime mapping, obstacle detection, and depthaware vision for navigation and manipulation. Strong mobility and payload support: torque gear motors deliver reliable traction and control for traversing obstacles and carrying additional sensors or manipulators. Expandable hardware: Includes a powerful robot expansion board to integrate additional modules, sensors, and actuators for custom applications. Native Python and ROS support: Works with ROS toolboxes including Rviz, Movelt, and Qt for robotic arm control, simulation, and visualization. Flexible control modes: Operate via a mobile app, a handheld controller for firstpersonview driving, or online programming through JupyterLab for remote development and testing. What makes it stand out Integrated perception and control stack lets teams move quickly from prototype to field trials without extensive hardware integration. Designed for both lowlevel motor control and level autonomy workflows using ROS and Python toolchains. Hardware and software tailored to support mobile manipulation, autonomous navigation, and data collection. Compatibility and performance Interfaces with standard ROS toolsets and Python development environments for seamless integration into existing robotics workflows. Sensor suite supports simultaneous localization and mapping and depthbased perception for robust autonomy. Expansion board supports additional peripherals and custom modules for specialized tasks. Practical use scenarios Autonomous mapping and inspection: Deploy the tank to create detailed maps of indoor facilities or outdoor sites, identify obstacles, and collect visual and depth data for analysis. Educational and research platform: Use ROS, Rviz, Movelt, and Python to teach motion planning, SLAM, and robotic arm control or to prototype algorithms. Remote operation and field testing: Combine FPV handle control and mobile app operation for teleoperation, while JupyterLab enables remote code development and data logging. This robot tank is designed to give developers, students, and field operators a durable, wellintegrated platform for building and testing autonomous and teleoperated robotic applications.