Sleek, durable crawler platform built for development, testing, and remote operation This robot tank combines a durable green aluminum alloy oxide chassis with precision sensing and modular control to simplify mobile robotics work. It is designed for reliable field use, flexible expansion, and straightforward programming and simulation. Key benefits Reliable navigation and mapping: SLAM lidar plus HD and depth cameras provide accurate environment sensing for mapping, obstacle avoidance, and autonomous path planning. Robust mobility: torque gear motors and a tracked crawler design deliver stable traction across uneven surfaces and predictable motion for payloads and sensors. Ready for development and simulation: Native Python and ROS support with Rviz, Movelt, and Qt toolboxes lets you prototype, visualize, and test algorithms before deploying to the physical robot. Modular expansion and control: A powerful robot expansion board and accessible I O make it easy to add sensors, manipulators, or custom electronics without hardware redesign. Multiple control options: Operate the robot from a mobile app, a handheld controller for FPV effects, or JupyterLab for online programming and batch testing. Important attributes Chassis material: Green aluminum alloy oxide for lightweight rigidity and corrosion resistance. Sensors: SLAM lidar, HD camera, depth camera for combined visual and range perception. Actuation: torque gear motors for stable tracked motion and controllable speed and torque. Control electronics: Integrated robot expansion board for motor drivers, sensor interfaces, and developer I O. Software compatibility: Python and ROS compatible; supports Rviz, Movelt, and Qt toolboxes for simulation, visualization, and GUI development. Control methods: Mobile app, handle controller for FPV operation, JupyterLab online programming environment. How it solves common problems Reduces development time by combining onboard sensing, a ready expansion interface, and outofthebox ROS tool integration for rapid algorithm testing and deployment. Improves field reliability through a rugged, corrosionresistant chassis and torque motors that maintain traction over rough terrain. Simplifies testing and demonstration by supporting simulation and visualization tools that mirror realworld behavior, enabling safer iteration before field trials. Enables multiuser workflows: operators can pilot manually for inspections while developers run experiments remotely through JupyterLab or visualize data in Rviz. Practical use scenarios Autonomous inspection and monitoring: Use SLAM and depth sensing to map indoor or structured outdoor areas, identify obstacles, and record visual and depth data for condition assessment. Research and education: Teach or test navigation, perception, and manipulation workflows with ROS, Rviz, and Movelt, transitioning smoothly from simulation to real hardware. Remote visual exploration and FPV tasks: Deploy the crawler for confinedarea exploration with HD and depth video streaming and handheld controller FPV operation for precise manual inspection. Who this is for Robotics researchers and students needing a rugged, extensible platform compatible with ROS and Python. Developers prototyping autonomous navigation, mapping, or manipulation workflows that require fast iteration between simulation and hardware. Field technicians and integrators who need a stable, sensorrich mobile base for inspections, data collection, or remote observation. What you get A robust tracked robot with aluminum alloy oxide chassis, SLAM lidar, HD and depth cameras, torque gear motors, and a dedicated robot expansion board. Endtoend software and control flexibility through ROS toolboxes, Python scripting, mobile app operation, handheld FPV control, and JupyterLab online programming. This platform focuses on durability, sensor fidelity, and developerfriendly integration to accelerate robotics development and field deployment.