Robust, modular crawler platform built for reliable field use and advanced development Key benefits Durable, versatile chassis: Constructed from green aluminum alloy oxide for a lightweight yet sturdy crawler that resists wear during testing and deployment. Accurate mapping and perception: Integrated SLAM lidar plus HD and depth cameras provide realtime environment mapping, obstacle detection, and depth understanding for autonomous navigation. Strong, dependable motion: torque gear motors deliver smooth tracked movement and the torque needed to carry sensors, manipulators, or additional payloads. Expandable control and I O: Builtin robot expansion board supports custom peripherals and sensor modules for prototyping and system upgrades. Developmentready software integration: Native Python and ROS support with Rviz, MoveIt, and Qt toolboxes for visualization, motion planning, robotic arm control, and realworld simulation. Multiple control modes: Operate via mobile app, handheld controller for firstpersonview driving, or JupyterLab for interactive online programming and experiment reproducibility. Features and what they solve SLAM lidar plus HD and depth cameras: Enable autonomous navigation in unknown or dynamic environments, reducing the time needed to build maps and tune navigation algorithms. torque gear motors with tracked drive: Provide traction and stability across varied surfaces, minimizing slippage and improving positional accuracy when carrying payloads. Aluminum alloy oxide crawler body: Offers a balance of low weight and structural strength to protect electronics while keeping the platform transportable. Robot expansion board: Simplifies integration of arms, grippers, additional sensors, and custom electronics without extensive hardware redesign. ROS toolchain compatibility (Rviz, MoveIt, Qt): Accelerates development of perceptiontoaction workflows, allows realistic simulation, and makes robotic arm control repeatable and debuggable. Crossplatform control options: Support for mobile app, handle controller for FPV operation, and JupyterLab for scripted experiments enables flexible workflows for operators, developers, and educators. Compatibility and performance Software: Compatible with Python and ROS workflows, including visualization and motion planning using Rviz and MoveIt, and GUI development with Qt toolboxes. Development environment: JupyterLab support enables interactive coding, data logging, and experiment sharing through notebooks. Control interfaces: Mobile application control and handheld controller support firstpersonview driving and remote operation, while programmatic control offers automated behaviors and custom algorithms. Material and build: Green aluminum alloy oxide chassis for corrosion resistance and reduced weight; tracked crawler design for stable mobility on uneven terrain. Practical applications Autonomous research platform: Use the integrated SLAM lidar and cameras with ROS toolchain to develop and validate navigation, mapping, and perception algorithms in indoor and outdoor testbeds. Field inspection and data collection: Deploy the crawler for remote visual and depth inspection of confined or uneven spaces, combining FPV operation for telepresence and automated mapping for documentation. Education and prototyping: Ideal for robotics courses and labs where students learn ROS, Python, simulation with Rviz and MoveIt, and hardware integration via the expansion board. What makes it stand out Integrated sensing and control stack ready for realworld tasks and classroom use. Durable aluminum crawler body combined with torque motors for reliable field performance. Flexible control options from handson remote driving to scripted development in JupyterLab, enabling rapid iteration across research, teaching, and prototyping. Includes: tracked crawler platform with green aluminum alloy oxide chassis, SLAM lidar, HD and depth cameras, torque gear motors, and an onboard robot expansion board for peripherals. Software support for Python, ROS, Rviz, MoveIt, Qt toolboxes, mobile app control, handheld FPV controller, and JupyterLab programming.