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First Raspberry Pi 5 Robot: MicroROS robot is based on Raspberry Pi 5 development board, supports Raspberry Pi 5 power protocol, can provide 5.1V/5A power supply for Raspberry Pi 5, to avoid reboot and other abnormalities, to play the full performance of Raspberry Pi 5, the visual image processing is clearer. Built-in ESP32 coprocessor, Wi-Fi and Bluetooth function, can handle a number of sensor data. Note: Not compatible with Raspberry Pi 4B. Fully compatible with ROS2: Based on the humble version of the ROS2 system, it is widely used, stable and has many case tutorials; MicroROS communication technology is used between the main control and the driver board, the code is open-source, the programming software, one-step learning. No need to worry about the robot hardware is not compatible, provides an adaptive ROS2 exclusive function set. MS200 is fully compatible with ROS2: supports indoor and outdoor, supports 360° scanning and detection, and realizes three-dimensional mapping; the MicroROS control board supports WiFi, Bluetooth, serial port and other communication functions, supports power supply for Raspberry Pi 5PD, and can be directly connected to ROSS. 2 Environment for Use: Easy to use and efficient - Built-in 310° motor - The metal material is wear-resistant and durable, and provides precise and stable movement control. AI Visual Game: 2MP 2-degree-of-freedom HD gimbal is used for AI depth visual development, remote control via APP or handle, combined with the high-performance Raspberry Pi 5, smoothly implementing face, QR code and position recognition, object tracking, object line. E-following Autonomous driving and gesture recognition control. Scope of delivery: You will receive a programmable robot with metal frame, most accessories already installed, including expansion boards for onboard ESP and IMU co-processing, 310 code motor, 7.4V recyclable battery, Raspberry Pi 5 (depending on the version), Pi5 active heat sink, radar and 2 DOF camera. High-performance hardware combined with solid e-racing content, including Yahboom training and theory courses.

【ROS2 AI Vision Hexapod Robot】The hexapod robot is a programming kit specifically for the robot operating system (Ubuntu), based on the Raspberry Pi5 or Jetson Nano development kit. It adopts a 6-foot 18dof bionic joint design to provide excellent stability and flexibility in various indoor and outdoor terrains. You can learn machine AI vision, bionic gait, 3D modeling navigation, RVIZ simulation and other knowledge. Please note: the robot cannot be used without a development board. 【High-performance hardware configuration upgrade】The voice interaction module supports voice command control of Muto RS robot interaction; the Astra Pro Plus depth camera not only completes the high-definition AI vision function, but is also responsible for processing data entry and three-dimensional navigation mapping; meeting the accuracy. YDLIDAR 4ROS radar on-demand capability in indoor and outdoor large scenes; all-aluminum alloy chassis body with built-in IMU. 【Multi-platform control, experience AI intelligent gameplay】Can be controlled through USB wireless controller, APP remote control, and PC web version. Through the APP, we can not only realize car body remote control and various performance modes, but also see the first visual picture of the robot. In addition, you can quickly switch to other smart game modes such as color tracking, line of sight, face detection, tag detection, etc. 【35kg servo motor + alloy alloy structure】3Equipped with a high-torque 35KG servo motor, 270° controllable 35kg intelligent serial bus servo, height adjustable; The solid all-aluminum alloy structure ensures the durability and durability of the product in harsh environments sex. 【Providing ROS2 tutorial】 Yahboom provides support ROS courses, including robot motion control, AI visual interaction, mapping navigation, docker container development, depth visual tracking, radar tracking and obstacle avoidance, 3D mapping navigation, voice interaction and other related electronic tutorials Tutorial For hexapod robot motion learning and verification, there is always online help to provide technical support.

【ROS Robot Learning Kit】The newly upgraded version is compatible with Orin Nano SUPER and Orin NX SUPER robot kits, and can provide stable power for the SUPER kits. We provide 5 ROS X3 development board versions, and multiple accessory versions are available. Orin Nano SUPER, Orin NX SUPER, Jetson Nano and RPi5. The performance issues of different development boards are different. 【AI Visual Explore】Equipped with ROS1/ROS2 operating system; ridar: can use voice to control multi-point navigation, supporting path planning and independent exploration and mapping;RTAB-3D mapping and navigation, and depth image data can be used to obtain real size information; AI visual depth gameplay: attitude detection, overall detection, and three-dimensional detection and recognition can be completed through MediaPipe development; YOLO model training, TensorRT. 【Multi-platform control】Compatible with a variety of remote control methods, including mobile phone remote control APP, game controller, ROS system, PC computer keyboard and Jupyter Lab control, etc. Suitable for multi-scene control use. *Mobile APP control supports iOS/Android. Compatible with ROS1/ROS2, Ubuntu22.04 【Excellent hardware configuration】Aluminum alloy chassis, 360° omnidirectional Mecanum wheel, all-round movable robot; optional radar, high-quality depth camera, voice alternation module, display screen, professionally designed expansion according to different versions panel. Professional ROS robot supports more DIY development. Users can re-develop existing courses and learn from them.(If you would like custom accessories, please contact Yahboom) 【Rich materials and technical support】Provide 204 bilingual video courses and online technical help. The course content includes: ROSMASTER X3 assembly, Linux operating system, ROS and openCV series courses, depth camera and radar exploration, which is a deep learning process. From pictorial navigation lectures and simple to deep learning measured navigation. Material website: http://www.yahboom.net/study/ROSMASTER-X3