Robust crawler platform designed for development, research, and field tasks Key benefits Ready for realworld robotics projects: durable crawler chassis and aluminum alloy oxide shell provide stability and corrosion resistance for repeated use. Precise mapping and perception: integrated SLAM lidar plus HD and depth cameras deliver accurate environment sensing for navigation and object detection. Flexible development and control: full Python and ROS support with Rviz, MoveIt, and Qt toolboxes enables rapid prototyping, simulation, and robotic arm control. Multiple control options: operate the robot through a mobile app, a handheld controller for FPV driving, or JupyterLab for online programming and algorithm testing. Expandable and powerful: torque gear motors and a powerful robot expansion board provide the torque and I/O needed for manipulators, sensors, and custom modules. Core features and specifications Chassis and material: crawler design with green aluminum alloy oxide finish for a sleek, wearresistant exterior and low maintenance. Sensing: SLAM lidar for simultaneous localization and mapping, HD camera for visual tasks, and depth camera for 3D perception and obstacle avoidance. Motion: torque gear motors paired with a tracked drive give stable traction on varied terrain and precise lowspeed control. Computation and expansion: integrated robot expansion board for advanced control and connectivity to add sensors, arms, or custom electronics. Software compatibility: native support for Python and ROS workflows, including visualization and motion planning via Rviz and MoveIt, and GUI tools via Qt. Practical applications Autonomous mapping and inspection: quickly build SLAMbased mapping routines to survey warehouses, labs, or outdoor sites while streaming video and depth data. Robotics education and research: use ROS, Rviz, MoveIt, and JupyterLab to teach perception, planning, and control, or to prototype manipulation tasks with a robotic arm. Remote monitoring and teleoperation: drive the crawler via app or handheld controller for patrols, inspections, or FPV exploration in confined or hazardous areas. Why this platform solves common problems Reduces integration time by combining proven sensors, robust actuation, and a capable expansion board in one platform. Supports realistic simulation and ondevice testing through standard ROS toolchains, so transfer from development to deployment is smoother. Offers flexible control modes for teams with different skill sets, from appdriven operators to developers using Python and JupyterLab. Compatible workflows and tools Works with standard ROS toolboxes including Rviz and MoveIt for visualization and motion planning. Supports development in Python and online programming through JupyterLab. Control options include mobile apps and handheld controllers for FPV operation. This crawler platform is intended for developers, researchers, educators, and field technicians who need a durable, sensorrich, and softwarefriendly robot base for mapping, perception, and robotic arm integration.