Yahboom Transbot SE ROS Robot Car with 3DOF Arm & 2MP PTZ, for Jetson Nano B01 / Raspberry Pi 5

Overview

Transbot SE is a ROS Robot Car (tracked crawler platform) designed for AI vision and robotics development with Jetson Nano B01 or Raspberry Pi 5. It uses an all-aluminum alloy body and integrates a 3DOF robotic arm plus a 2DOF camera PTZ for vision-based control, tracking, and robot arm simulation with MoveIt.

Need help choosing a version (with/without Jetson Nano or Raspberry Pi) or preparing the software environment? Contact support via https://rcdrone.top/ or email support@rcdrone.top.

Key Features

• Tracked crawler chassis with differential track structure for off-road driving
• All-aluminum alloy body
• 3DOF robotic arm (intelligent serial bus servo) for gripping/handling and simulation workflows
• 2DOF camera PTZ with 2MP camera (horizontal/vertical rotation)
• 520 encoder motors
• Built-in AI vision stack: OpenCV image processing, MediaPipe machine learning, YOLO object recognition, and an AI deep learning framework
• Interconnection control options shown: Remote control APP, Jupyter web programming control, ROS system control, cross-platform interconnection control, multi-vehicle formation control
• Programming: Python programming and C++ programming are indicated

AI Vision & Control Functions (Shown)

• OpenCV image processing: Object Detection (recognize specific object categories), AR Vision (12 effects displayed through chessboard paper), AR QR code (generate and recognize AR QR codes), Face recognition (autonomous training and real-time recognition through real-time collection of facial images)
• AI visual gameplay: Color Tracking, Object Tracking (camera PTZ tracks objects in real time), Robotic arm handling (QR code command-based handling), Autopilot (custom color selection; follows a recognized color path)
• MoveIt robotic arm control: forward/inverse kinematics algorithm, Cartesian path planning, collision detection, MoveIt simulation
• Gesture recognition control (MediaPipe): palm-controlled chassis movement, gesture-controlled robotic arm action groups, gesture-controlled chassis movement, arm attitude control (robotic arm imitates arm posture and palm open/close)
• MediaPipe development: gesture recognition, face recognition, 3D object recognition (examples shown: “SHOE”, “CHAIR”, “CUP”, “CAMERA”, etc.)
• Deep learning examples shown: KNN recognition of handwritten digits; YOLO object recognition (custom object recognition via training custom datasets using the YOLOv5 algorithm)

Specifications

Version Options (Shown)

• Jetson Nano version: With Jetson Nano 4GB (SUB version) / Without Jetson Nano
• Raspberry Pi version: With Raspberry Pi 5-4GB / Without Raspberry Pi (requires Raspberry Pi with 4GB or more of RAM)

Applications

• ROS learning and robot motion control development
• Computer vision projects (OpenCV), gesture recognition (MediaPipe), and object recognition (YOLO)
• Robotic arm simulation and planning experiments with MoveIt (kinematics, Cartesian planning, collision detection)
• Remote control and web-based programming control demonstrations (APP control, Jupyter, ROS system control)

Tutorials & Learning Resources

Tutorial link:http://www.yahboom.net/study/Transbot-SE

Course Catalog (Shown)

• Introduction of Transbot SE: About Transbot SE; Precautions for use and battery safety; First Trial
• First Trial: WiFi network configuration; APP Control; USB wireless handle control; Handle video control
• Hardware control course: About expansion board and update firmware; Close self-starting process; Install Transbot SE library; Control buzzer and button; Control PWM servo; Control bus servo; Control motor; Control robot movement
• Linux operating system configuration: Virtual machine installation and use; Linux basics; Remote control; Multi-machine communication configuration; Static IP and hotspot mode; Web page real-time monitoring; Expansion tutorial; Write system images
• Docker use: Docker overview and docker installation; Common commands for docker image containers; Docker images deeply understand and publish images; Docker hardware interaction and data processing; Start the Dobot container
• ROS Basic course: ROS introduction; Project file structure; Common commands and tools; Publisher; Subscribers; Customize topic messages and use; Client; Server; Custom service messages and usage; TF release and monitoring
• OpenCV courses: Getting started with Open Source CV; Open Source CV geometric transformation; Open Source CV image processing and drawing text line segments; Open Source CV image beautification; AR vision; AR QR code; ROS+Opencv foundation; ROS+Opencv application; MediaPipe development
• ROS robot course: PID algorithm; Basic communication; Keyboard control; Handle control; Robot state estimation; Data calibration
• ROS simple camera course: HD camera calibration; HD camera color tracking; HD camera color tracking (chassis); HD camera object tracking; KCF target tracking; HD camera face tracking; HD camera robotic arm carrying; HD camera Autopilot
• ROS robotic arm control tutorial: MoveIt configuration; MoveIt control the real machine; MoveIt moves randomly; MoveIt kinematics design; MoveIt cartesian path; MoveIt avoiding; MoveIt scene design; MoveIt trajectory planning
• ROS multi-robot control: Multi-robot control; Multi-robot queue performance; Multi-robot robot arm dancing
• ROS robotic arm MoveIt control course: MoveIt configuration; MoveIt control the real machine; MoveIt moves randomly; MoveIt kinematics design; MoveIt cartesian path; MoveIt avoiding; MoveIt scene design; MoveIt trajectory planning; Mediapie palm control car; Mediapipe gesture control robotic arm; Mediapipe gesture control car; Mediapipe arm attitude control
• Deep learning courses: KNN recognizes handwritten digits; Basic use of TensorFlow; Basic use of PyTorch (jetson); yolov5 model training (jetson); yolov5+tensorrt acceleration (jetson); yolov4-tiny

Details

Transbot SE is a tracked ROS robot platform built for AI vision projects, featuring an all‑metal body, a 3DOF arm, and a 2DOF camera gimbal.

A full software stack supports OpenCV vision, MediaPipe gesture control, YOLO recognition, and MoveIt-based robotic arm simulation.

The tracked crawler chassis and differential drive are designed for stable movement on varied indoor and outdoor surfaces.

Compatible with Raspberry Pi 5 for ROS development and smoother on-board vision processing.

Choose a kit with Jetson Nano or Raspberry Pi included, or a no-board version if you already have your own controller.

Built-in OpenCV demos include object detection, AR effects with marker boards, and QR code generation/recognition workflows.

AI visual gameplay adds color tracking, PTZ-based object tracking, QR-command pick-and-place, and color-path autopilot.

MoveIt integration supports kinematics, Cartesian planning, and collision checking for robotic arm development and simulation.

MediaPipe gesture control enables palm-driven movement plus gesture-triggered robotic arm action groups and posture mirroring.

Sample projects include gesture/face recognition, 3D object labels, KNN digit recognition, and YOLO dataset training examples.

Use the remote-control app for quick driving, camera features, and interactive AI modes without a full desktop setup.

Multiple control paths are supported, including Jupyter web programming, ROS system control, and cross-platform interconnection.

A structured course catalog guides setup, ROS basics, vision features, and advanced control topics.

Tutorial resources are available online to help build the software environment and start ROS and vision demos faster.

Hardware highlights include the 2DOF camera PTZ, 3DOF serial-bus arm, encoder motors, and an optional ROS main control board.

The expansion board breakout simplifies wiring for motors, serial devices, USB peripherals, and common sensors.

Detailed dimensions help plan mounting space, lab layouts, and accessory integration.

A complete parameter table compares controller options and summarizes power, interfaces, operating system, and assembly details.

3DOF robotic arm dimensions and key servo parameters (YB-SD15M joints and YB-S06 claw) help with layout planning and power selection.

The 2DOF camera PTZ module includes a 2MP 1080p USB 2.0 camera and a compact mount with dimensions labeled in millimeters for easier integration.

The 520 encoder geared motor uses a 12V brushed design with a 1:56 reduction ratio and a Hall encoder (3.3–5V), rated at 205±10 rpm after deceleration.

The 12V 4400mAh lithium battery pack uses a T-type discharge plug and lists 8.8A rated and 10A max discharge current.

The Yahboom Transbot SE ROS robot kit includes the frame and top plate, tracks and wheels, a 3DOF robotic arm, 2DOF camera PTZ, motors, battery, charger, and cables.

The Transbot SE ROS robot parts packages include Jetson Nano or Raspberry Pi options with accessories like a cooling fan, antennas, and TF storage.