Robust crawler platform built for development, research, and field use Key benefits Reliable mobility: The aluminum alloy oxide crawler chassis in a green finish provides durable protection and a lowprofile platform for rough surfaces and extended testing. Accurate environment perception: SLAM lidar combined with HD and depth cameras enables precise mapping, obstacle detection, and depthaware navigation. Strong drive and payload capability: torque gear motors deliver steady traction and stable motion suitable for carrying sensors or a small manipulator. Ready for development and simulation: A performance robot expansion board supports onboard processing and integrates with common robotics toolchains for rapid prototyping. Flexible control options: Operate the robot from a mobile app, a handheld controller for FPV operation, or through JupyterLab for online programming and experimentation. Core features and specifications Chassis material: Green aluminum alloy oxide for durability and corrosion resistance. Sensing: SLAM lidar for simultaneous localization and mapping; HD camera and depth camera for vision and 3D perception. Actuation: torque gear motors for reliable crawling performance and payload handling. Electronics: Powerful robot expansion board to manage sensors, motors, and communication. Software compatibility: Native support for Python and ROS. Compatible with Rviz, MoveIt, and Qt toolboxes for visualization, motion planning, and GUI development. Programming and control: Supports JupyterLab online programming, mobile app control, and handle controller input for firstpersonview operation. How it solves common problems Speeds up development: Preintegrated sensors and a readytouse expansion board remove the need to wire disparate parts, letting teams focus on algorithms and applications. Reduces testing friction: SLAM lidar and depth sensing provide reliable situational awareness so navigation and mapping experiments progress faster and with fewer false starts. Enables multiple workflows: Whether you need handson remote control, visual FPV testing, or scripted experiments in JupyterLab, the platform supports the control method suited to your task. Practical use scenarios Research and education: Use the platform in university labs or training programs to teach SLAM, computer vision, and robotic manipulation with ROS, Rviz, and MoveIt. Inspection and mapping: Deploy the crawler for indoor inspection or mapping tasks where compact, groundlevel sensing and stable traction are required. Prototyping mobile manipulation: Combine the platform with a robotic arm and use the Qt toolboxes and ROS planners to develop and simulate manipulation workflows in realistic environments. Why choose this platform Integrated hardware and software stack that reduces setup time. Multiple control and programming options to fit diverse project needs. Rugged, serviceable construction suitable for repeated testing and field experiments. What you get A crawler robot with aluminum alloy oxide chassis in green. SLAM lidar, HD camera, depth camera, torque gear motors, and an onboard expansion board. Compatibility with Python, ROS, Rviz, MoveIt, Qt, mobile app control, handle controller FPV, and JupyterLab programming. This platform is designed for engineers, researchers, and educators who need a dependable, configurable robot base that supports advanced perception, motion planning, and flexible control workflows.