Product overview A purposebuilt crawler robot platform with a green aluminum alloy oxide chassis, designed for reliable mobility, precise sensing, and flexible development. Combines SLAM mapping, depth perception, torque drive, and a programmable expansion board to accelerate robotics prototyping, research, and field tasks. Key benefits Faster development and integration: Preconfigured hardware and ROS Python support reduce setup time for mapping, perception, and manipulation projects. Accurate environment understanding: SLAM lidar plus HD and depth cameras enable precise mapping, obstacle detection, and visual perception for navigation and inspection. Strong mobility and payload handling: torque gear motors and a robust crawler design deliver stable traction and the ability to carry sensors and manipulators. Flexible expansion and control: A powerful robot expansion board provides I O and communication headers for adding sensors, arms, or custom actuators without major rewiring. Multiple control options: Choose from mobile app control, handlebased FPV teleoperation, or codedriven workflows in JupyterLab for handson or automated operation. Core specifications and functions Chassis material: Green aluminum alloy oxide for a durable, lightweight body and corrosion resistance. Mobility: Crawler/tank drive architecture with torque gear motors for stable motion over uneven surfaces. Perception: SLAM lidar for mapping and localization, HD camera for visual data, and a depth camera for 3D perception and obstacle avoidance. Expansion: Onboard robot expansion board with extensive I O and interface support for sensors, actuators, and peripherals. Software and integration: Native support for Python and ROS. Compatible with Rviz, MoveIt, and Qt toolboxes for robotic arm control and realworld simulation. Programming and control environments: Mobile app for quick control, handle control for FPV teleoperation, and JupyterLab for interactive online programming and experiments. Why this platform stands out Integrated sensing and computeready expansion reduce the time between idea and working prototype. ROS and Python compatibility provide access to established toolchains such as Rviz and MoveIt for visualization, motion planning, and robotic arm control. Multiple operator interfaces meet different workflows, from handson teleoperation to scripted experiments and autonomous routines. Typical use scenarios Indoor mapping and inspection: Perform SLAMbased mapping of warehouses, labs, or facilities to generate accurate floor maps and detect obstacles for maintenance planning. Manipulation research and training: Use MoveIt and the robotic arm toolboxes to develop pickandplace, grasping, and manipulation workflows in simulation and on the physical platform. Remote field scouting and FPV operation: Teleoperate the crawler with a handle controller or mobile app for visual inspection, site reconnaissance, or routine monitoring in confined or hazardous areas. Compatibility and deployment notes Designed for ROS and Python development environments. Integrates with Rviz, MoveIt, Qt toolsets, and JupyterLab workflows. Expansion board supports common sensor and actuator interfaces for . Check your addon hardware for interface compatibility before installation. What you can achieve This crawler platform is suited for prototyping autonomous navigation, perceptiondriven inspection, educational robotics, and robotic manipulation projects where rapid integration, reliable sensing, and flexible control options are required.