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ROSPugtutorial: schematics, source codes, videos & experimental projects. All you need for STEAM education is right here.https://wiki.hiwonder.com/projects/ROSPug/en/latest/.
Hiwonder ROSPug is smart quadruped robot dog built upon Robot Operating System (ROS). It is equipped with 12 high-voltage strong-magnetic serial bus servos and integrates a range of high-performance components, including NVIDIA Jetson Nano controller, TOF Lidar, HD camera, IMU sensor, OLED display, and more. Featuring self-developed dynamic balancing kinematics algorithm, it can switch seamlessly between multiple gaits.
ROSPug supports Gazebo simulation, providing users with a valuable platform to learn and validate quadruped kinematics algorithms and path planning. Thanks to the robust computing power of its controller, ROSPug can perform tasks such as SLAM mapping navigation, path planning, dynamic obstacle avoidance, climbing, obstacle bypassing, and many other applications. We also offer solutions for expansion of ROSPug’s capabilities, including deep learning, machine vision, and other development projects to meet users’ specific needs.
Hiwonder ROSPug employs 12 high-performance servos, distributed across its elbow, shoulder, and hip joints of each leg, closely mimicking the post 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 AI robot dog is powered by Jetson Nano and features high-performance Lidar and an HD wide-angle camera, enabling the validation of various creative AI applications.
Hiwonder 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.
ROSPug features high-performance intelligent serial bus servos with a torque of 30KG, providing exceptional accuracy, data feedback, easy wiring, and support for a robust 12V voltage power supply.Dual-Controller Design for Efficient CollaborationROSPug features a dual-controller design that combines the advanced AI capabilities 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 possibilities.
Using 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.
To support users in exploring ROSPug’s capabilities, we offer detailed quadruped kinematics analysis, ROS-based inverse kinematics functions, and parameter debugging software.
Android and iOS mobile APP are available. Via the APP, you can remotely control the robot and view what the robot sees.
ROSPug can recognize red lines, and then calculate the location of the line so as to adjust the walking gait and realize line following.
By utilizing visual recognition to identify the circular pile and measuring the distance between itself and the pile using Lidar, the robot can adjust its moving direction to perform circular drifting.
Through independent vision judgment, the position of the stairs in front is identified and the scene of autonomously going up and down the stairs is realized.
ROSPug utilizes OpenCV to determine ball’s position and employs a PID algorithm for real-time ball tracking. This approach allows it to fine-tune its gait to achieve accurate ball shooting, considering the distance from the target and the target’s location.
Global Popular Robotic Communication FrameworkROS is an open-source meta operating system for robots. It provides some basic services, such as hardware abstraction, low-level device control, implementation of commonly used functionality, message-passing between processes, and package management. And it also offers the tools and library functions needed to obtain, compile, write, and run code across computers. It aims at providing code reuse support for robotics research and development.
ROSPug utilizes the ROS framework and offers Gazebo simulation support. Gazebo provides a novel approach for controlling ROSPug and validating algorithms within a simulated environment, reducing the need for physical experiments and enhancing efficiency.
| Size | 334*211*210 mm |
| Main controller | Jetson Nano |
| Torque of joints | 30KG.cm 12V |
| Material | Aluminum alloy structure + rubber (foot pad) |
| DOF | 12DOF |
| Weight | About 1.65KG |
| Camera pixel | 8 megapixel |
| Power | 11.1V 3500mAh 5C LiPo battery |
| App | WonderROS (Android/iOS) |
| Built-in host | Jetson Nano |
| GPU | NVIDIA Maxwell architecture, equipped with 128 NVIDIA CUDA cores |
| CPU | Quad-core ARM Cortex-A57 MPCore processor |
| Memory | 4GB 64-bit LPDDR4 |
| Storage | 64GB SD card |
| Package size | 44.5*33*18cm |
| Package weight | 3.1kg |
ROSPug Packing List