Robotic crawler platform engineered for development, research, and field applications. Built from green aluminum alloy oxide for a lightweight, durable chassis and a compact, versatile footprint. Key benefits Reliable outdoor and indoor mobility: Crawler tracks plus torque gear motors deliver stable motion over uneven terrain and precise lowspeed control for manipulation tasks. Accurate perception and navigation: SLAM lidar combined with HD and depth cameras enables robust mapping, obstacle detection, and 3D scene understanding for autonomous missions. Ready for integration and expansion: Onboard robot expansion board provides interfaces for sensors, actuators, and a robotic arm, reducing setup time for custom payloads and experiments. Full software compatibility: Native support for Python and ROS toolboxes including Rviz, MoveIt, and Qt for visualization, motion planning, and custom UIs, plus JupyterLab for interactive development. Flexible control modes: Operate via mobile app, handle controller for first person view teleoperation, or program remotely through JupyterLab for automated workflows and testing. Core specifications and performance highlights Chassis material: green aluminum alloy oxide for corrosion resistance and reduced weight. Sensors: SLAM lidar for mapping, HD camera for visual data, depth camera for 3D perception. Actuation: torque gear motors for carrying payloads and precise manipulation. Control hardware: integrated robot expansion board for I O, motor drivers, and sensor interfaces. Software: Python and ROS support with Rviz, MoveIt, Qt toolboxes and JupyterLab compatibility for simulation and realworld control. Practical use scenarios Autonomous inspection and mapping: Deploy the crawler in warehouses or complex indoor spaces to create SLAM maps, identify obstacles, and collect HD and depth imagery for asset inspection. Research and education: Use the platform as a handson ROS development kit for teaching motion planning with MoveIt, visualization with Rviz, and realtime experiments through JupyterLab. Remote teleoperation and data collection: Operate via mobile app or handle controller for FPV exploration, remote monitoring, or sample retrieval in constrained or hazardous areas. Why this platform solves common problems Reduces integration time with preinstalled sensor suite and expansion board for straightforward hardware connection. Bridges simulation and reality using ROS toolchains, enabling workflows that transition from Rviz and MoveIt simulation to realworld robotic arm control. Supports multiple control pathways to match operator preferences and project requirements, from manual teleoperation to scripted autonomous missions. Compatibility and extensibility Designed for ROS and Python ecosystems, compatible with standard ROS nodes and packages. Expansion board provides standard interfaces for adding custom sensors, manipulators, and communication modules. Suitable for academic labs, prototyping teams, and field robotics groups needing a reliable, expandable crawler platform.