Robust crawler platform for development, testing, and remote operations Key benefits Faster development and reliable field testing. Integrated sensors and compute let you validate navigation, perception, and manipulation workflows without building a custom base. Flexible control and programming. Choose between handson operation, mobile app control, FPV handling, or full online programming with JupyterLab. Accurate mapping and obstacle avoidance. Onboard SLAM lidar and depth sensing provide realworld mapping and collision awareness for safer autonomous runs. What makes it stand out Durable, precision chassis: Constructed from green aluminum alloy oxide for a sleek, corrosionresistant finish and reliable structural performance. Comprehensive sensing suite: SLAM lidar plus HD and depth cameras deliver dense environmental data for mapping, object detection, and 3D reconstruction. Powerful actuation and expansion: torque gear motors provide stable traction and precise motion control. A dedicated robot expansion board supports advanced peripherals and control logic. Native Python and ROS compatibility: Supports ROS workflows with toolboxes such as Rviz, MoveIt, and Qt for robotic arm control and realworld simulation. Functions and compatibility Navigation and mapping: Realtime SLAM for autonomous path planning and environment mapping. Perception: Synchronized HD and depth cameras for object recognition, localization, and scene understanding. Manipulation and simulation: Interfaces with ROS toolchains including Rviz and MoveIt for motion planning and simulated testing before deployment. Control methods: Mobile app control, handlebased FPV control, and online programming through JupyterLab provide a range of interaction models for development and demonstration. Practical applications Research and education: Teach or prototype autonomy, perception, and manipulation workflows using standard ROS and Python tools. Remote inspection and monitoring: Perform visual and lidar inspections in warehouses, factories, or confined spaces using FPV or automated patrol routes. Prototyping robotic manipulation: Integrate a robotic arm, use MoveIt and Rviz for motion planning, and iterate algorithms with live sensor feedback. Technical highlights Material: Green aluminum alloy oxide chassis Sensors: SLAM lidar, HD camera, depth camera Actuation: torque gear motors Control and expansion: Dedicated robot expansion board with Python and ROS support Development environments: Rviz, MoveIt, Qt toolboxes, and JupyterLab online programming Who this is for Engineers, researchers, and educators who need a ready platform for autonomous navigation, perception experiments, and manipulation prototyping with multiple control and programming options.