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ROSPug – Quadruped Bionic Robot Dog
ROSPug – Quadruped Bionic Robot Dog
ROSPug – Quadruped Bionic Robot Dog
ROSPug – Quadruped Bionic Robot Dog
ROSPug – Quadruped Bionic Robot Dog

ROSPug – Quadruped Bionic Robot Dog

  • NVIDIA Jetson Nano + Dual-Controller Design – Combines Jetson Nano AI computing with a high-frequency MCU for real-time precision gait control and complex AI challenge execution.
  • 12 High-Voltage Strong-Magnetic Serial Bus Servos (30KG) – Distributed across elbow, shoulder, and hip joints; aluminum alloy body with metal-bearing-reinforced calf joints for lightweight, high-strength bionic movement.
  • TOF Lidar + HD Wide-Angle Camera – Enables SLAM mapping navigation, path planning, dynamic obstacle avoidance, target tracking, and first-person-view (FPV) streaming via app.
  • Self-Developed Dynamic Balancing Kinematics – Supports ripple and trot gaits with real-time IMU-based posture correction, self-balancing, and yaw-angle correction on any incline.
  • Rich AI Vision Applications – Color recognition, line following, face detection, ball shooting, emotion recognition, MediaPipe body and gesture recognition, and April Tag tracking.
  • Multi-Control & Open-Source – WonderROS app (iOS/Android), PC software with drag-and-drop action editor, wireless PS2 handle, and Gazebo simulation; full Python open-source code provided.

 

Price on request
  • Trusted dealerDirect manufacturer partnership
  • Pan-India deliveryInsured shipping, all states
  • Post-sale supportTraining + service included
Overview

What makes it work

< Hiwonder ROSPug Quadruped Robot Dog — A+ Content | xBoom India
Quadruped Robot Dog · ROS · NVIDIA Jetson Nano · xBoom India

Hiwonder ROSPug
Quadruped Bionic Robot Dog

Professional Quadruped Robot Dog with ROS, SLAM Navigation & Dynamic Balance

Hiwonder ROSPug is a smart quadruped robot dog built upon the Robot Operating System (ROS). It is equipped with 12 high-voltage strong-magnetic serial bus servos and integrates high-performance components including NVIDIA Jetson Nano, TOF Lidar, HD camera, IMU sensor, and OLED display. Featuring a self-developed dynamic balancing kinematics algorithm, it switches seamlessly between multiple gaits. ROSPug supports Gazebo simulation, provides users a valuable platform to learn and validate quadruped kinematics algorithms and path planning, and can perform SLAM mapping navigation, dynamic obstacle avoidance, climbing, and obstacle bypassing.

Jetson Nano + MCU Dual-Controller 12 × 30KG Serial Bus Servos TOF Lidar SLAM Navigation Gazebo Simulation Open-Source Python Ripple + Trot Gait
Technical Specifications
ROSPug — Full Specifications
SpecificationDetails
ControllerNVIDIA Jetson Nano (dual-controller: Jetson Nano for AI + MCU for high-frequency servo control)
Servos12 high-voltage strong-magnetic serial bus servos, 30KG torque — elbow, shoulder, hip joints per leg
BodyFull aluminum alloy; metal-bearing-reinforced calf joints; link structure leg design
SensorsTOF Lidar, HD wide-angle camera, IMU sensor (6-axis), OLED display
FrameworkROS — Ubuntu 18.04, ROS Melodic; Gazebo simulation support
SLAMSLAM mapping, TEB path planning, dynamic obstacle avoidance, autonomous navigation
GaitsRipple Gait, Trot Gait — dynamic switching via self-developed algorithm
AI VisionColor recognition, line following, face detection, ball shooting, emotion recognition, MediaPipe, April Tag, circular drift
ControlWonderROS app (iOS/Android), graphical PC software (drag-and-drop), wireless PS2 handle
SimulationGazebo simulation; ROS kinematics validation; quadruped IK analysis tool
ProgrammingPython — fully open-source with detailed annotations; IK source code included
Battery11.1V high-voltage LiPo
OSUbuntu 18.04 + ROS Melodic
Product Introduction
Biomimetic Quadruped AI Robot — Built for ROS Research
ROSPug walking posture aluminum alloy body
Driven by Jetson Nano — Dual Controller

12 High-Voltage Servos with Aluminum Alloy Body

ROSPug employs 12 high-performance servos distributed across its elbow, shoulder, and hip joints of each leg, closely mimicking the posture of a real quadruped animal. Its entire body is crafted from aluminum alloy, with the calf joint reinforced by metal bearings, ensuring both low weight and high strength.

ROSPug features a link structure design that enhances the speed of the calf joint and ensures smooth motion without interference, thereby extending the leg's rotation range. The 12 intelligent serial bus servos provide 30KG torque with exceptional accuracy, data feedback, easy wiring, and support for a robust 12V voltage power supply.

The dual-controller design combines the advanced AI computing capability of the Jetson Nano with the high-frequency control functions of the MCU. This integration enhances operational accuracy, enabling the system to tackle more complex challenges and explore a wider range of creative AI applications.

ROSPug SLAM mapping navigation lidar
SLAM Development & AI Application

Lidar SLAM Mapping, Path Planning & Obstacle Avoidance

ROSPug AI robot dog is powered by Jetson Nano and features high-performance TOF Lidar and an HD wide-angle camera, enabling the validation of various creative AI applications. The Lidar enables ROSPug to perform SLAM mapping and navigation, supporting path planning, fixed-point navigation, and dynamic obstacle avoidance.

ROSPug can perform tasks such as SLAM mapping navigation, path planning, dynamic obstacle avoidance, climbing, obstacle bypassing, and many other applications. Hiwonder also offers expansion solutions for ROSPug's capabilities, including deep learning, machine vision, and other secondary development projects to meet users' specific research needs.

ROSPug AI vision color recognition face detection
AI Vision — Unlimited Creativity

Color Tracking, Face Detection & MediaPipe Control

ROSPug is equipped with an HD wide-angle camera and utilizes the OpenCV library for efficient image processing, enabling a diverse range of AI applications including target recognition, localization, line following, obstacle avoidance, face detection, ball shooting, color tracking, and April Tag recognition.

ROSPug can recognize color lines and calculate the location of the line so as to adjust its walking gait and realize line following. Using independent visual judgment, ROSPug can identify the position and height of stairs and autonomously navigate up and down — and the IMU sensor enables real-time posture correction to maintain balance on inclined surfaces.

  • Color recognition and tracking — perform different actions per color
  • Autonomous stair climbing via visual judgment
  • MediaPipe gesture and body recognition
  • April Tag detection and coordinate recognition
  • Ball shooting and circular drift using Lidar + vision
ROSPug PC software control open source Python
Open-Source Python & Multi-Platform Control

Graphical PC Software, App Control & Gazebo Simulation

Using the graphical PC software, you can effortlessly control servos and customize actions by simply dragging sliders — without the need for programming. All intelligent Python code is open source with detailed annotations for easy self-study. Hiwonder provides detailed quadruped kinematics analysis, ROS-based inverse kinematics functions, and parameter debugging software.

ROSPug employs the ROS framework and supports Gazebo simulation, providing users a valuable platform to validate quadruped kinematics algorithms and path planning in a virtual environment without physical hardware requirements. The WonderROS app (iOS/Android) enables remote robot control and live FPV camera viewing.

Image Gallery
ROSPug — All Product Images
ROSPug stair climbing ROSPug obstacle avoidance ROSPug Gazebo simulation
Spec Infographic
ROSPug — Key Specifications at a Glance

ROSPug — ROS Quadruped Robot Dog Specifications

NVIDIA Jetson Nano · Dual Controller · 12 DOF · TOF Lidar · Dynamic Balance · Open-Source Python
12
DOF Joints
30KG Each
2
Controllers
Jetson + MCU
TOF
Lidar
SLAM Mapping
Al-Mg
Alloy Body
Metal Bearings
ROS
Framework
Gazebo Sim
2
Gaits
Ripple + Trot
IMU
Self-Balance
6-Axis
11.1V
LiPo Battery
High Voltage
Product Videos
ROSPug — Demo & Tutorial Videos
ROSPug — Official Demo: SLAM, Gait Switching & AI Vision
ROSPug — SLAM Navigation & Dynamic Obstacle Avoidance
ROSPug — MediaPipe Gesture & Face Recognition Demo
Frequently Asked Questions
ROSPug FAQ
What makes ROSPug's dual-controller design special?
ROSPug features a dual-controller design that combines the advanced AI computing capability of the Jetson Nano with the high-frequency control functions of the MCU. This integration allows the AI controller to handle vision, SLAM, and decision-making tasks while the MCU maintains precise, low-latency servo control — enhancing operational accuracy and enabling the system to tackle more complex robotic challenges simultaneously.
What gaits does ROSPug support and can I customize them?
ROSPug supports ripple gait and trot gait out of the box, with dynamic switching via the self-developed balancing algorithm. Using the inverse kinematics source code provided, you can customize touch time, lift time, lifted height, and stride for each leg. The visual PC software allows drag-and-drop endpoint editing for action group creation without any coding required.
Can ROSPug climb stairs autonomously?
Yes. ROSPug uses independent visual judgment to identify the position and height of stairs within its camera view, then autonomously adjusts its gait to climb up and down. The IMU sensor enables real-time posture correction — regardless of surface inclination, ROSPug dynamically adjusts its joints to maintain balance. It can also navigate obstacles and bypass barriers using Lidar-guided path planning.
What AI vision features are built in?
ROSPug supports color recognition and tracking, line following, face detection and tracking, emotion recognition, ball shooting, MediaPipe body and gesture recognition, April Tag detection, obstacle avoidance, and circular drift using Lidar combined with visual localization.
Is Gazebo simulation supported for development?
Yes. ROSPug fully supports Gazebo simulation within the ROS framework. You can validate quadruped kinematics algorithms, test path planning, and simulate obstacle avoidance scenarios virtually before running experiments on the physical robot. RViz provides visual data for observing the robot's end-effector trajectory and center of gravity to optimize algorithms.
Specifications

The full sheet

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