Robust crawler platform built for development and field testing Key benefits Durable, lightweight construction for reliable deployment: machined from green aluminum alloy oxide to resist wear while keeping weight low for longer runtime and easier transport. Accurate perception and mapping: integrated SLAM lidar plus HD and depth cameras provide robust environment sensing for navigation, obstacle avoidance, and mapping tasks. Strong, precise motion: torque gear motors deliver stable crawler traction and smooth control across varied terrain. Ready for advanced control and simulation: full Python and ROS support with Rviz, Movelt, and Qt toolboxes enables rapid development, testing, and visualization of robotic arm and platform behaviors. Multiple control options to match your workflow: native mobile app control, handlebased control for FPV operation, and JupyterLab online programming for interactive development. Core features Crawler chassis crafted from green aluminum alloy oxide for a balance of durability and weight. SLAM lidar for realtime mapping and localization. HD camera and depth camera for visual perception and 3D scene understanding. torque gear motors for reliable crawler propulsion and payload handling. Expandable robot control interface via an onboard robot expansion board for easy hardware extension. Software integration with Python and ROS, including Rviz, Movelt, and Qt toolboxes. Crossplatform operation: mobile app, handle control for FPV effects, and JupyterLab online programming support. Why this product solves common problems Reduces setup time for robotics projects by combining sensing, actuation, and control hardware in a single readytouse platform. Improves robustness of outdoor and indoor trials with aluminum alloy construction and torque motors that handle uneven terrain. Simplifies development and validation through direct ROS and Python compatibility and builtin toolboxes for simulation and visualization. Practical use scenarios Research and education: teach or prototype autonomous navigation and manipulation algorithms using ROS, Rviz, and Movelt simulations, then transition to realworld testing with the same software stack. Inspection and site surveying: deploy the crawler to map unknown indoor environments with SLAM lidar and capture visual and depth data for postprocessing. Remote operation and demonstration: use the mobile app or handle control for live FPV operation during demonstrations, field trials, or remote monitoring tasks. Compatibility and expandability Supports Python and ROS development workflows and integrates with common ROS toolboxes such as Rviz, Movelt, and Qt for visualization and control. Onboard expansion board enables connection of additional sensors and actuators to the platform for specific projects. What to expect in performance Stable, precise locomotion from torque gear motors. Reliable sensing with combined SLAM lidar, HD camera, and depth camera for mapping, obstacle detection, and 3D perception. Flexible control and programming options to fit classroom, lab, or field environments. This crawler platform is designed for engineers, researchers, and educators who need a durable mobile robot base with integrated sensing and flexible software support for rapid development and realworld testing.