Yahboom Muto RS 18DOF ROS2 Hexapod Robot Kit with AI Large Model, LiDAR & Depth Camera for Raspberry Pi 5

Overview

The Yahboom Muto RS is a desktop-level AI large model bionic hexapod robot built on the ROS2 operating system and designed to work with Raspberry Pi (including Raspberry Pi 5 options). It uses an all-aluminum alloy body and an 18 DOF joint structure driven by 18PCS 35KG serial bus servos, and integrates sensors such as a depth camera and LiDAR plus a voice interaction module. With Python3 programming and built-in algorithms (including inverse kinematics), it supports AI visual interaction, SLAM mapping/navigation, voice interaction, deep learning, and RViz simulation for ROS development and education.

Key Features

• 18 DOF motion joints with aluminum alloy structural parts; three joints per leg; 18 high-performance servos.
• 18PCS 35KG metal serial bus servos for stable, coordinated motion control.
• Inverse kinematics algorithm precision control; supports triangular gait walking and adjustable stride frequency.
• Motion adjustability: X/Y translation, 360° self-rotation, body height adjustment, posture superimposition (high/medium/low stance walking), and adjustable walking speed (linear velocity, angular velocity, height, step height, stride length).
• Multimodal AI large model integration: scalable RAG knowledge base, dual-modal dynamic feedback reasoning architecture, text semantic understanding, and natural speech dialogue.
• Depth camera + visual recognition: depth camera obstacle detection, 3D real-time mapping, depth distance measurement, and 3D point cloud recognition.
• LiDAR-based environmental perception: 360° omnidirectional sensing, mapping and navigation, path planning, dynamic obstacle avoidance, multi-point navigation, and road network planning.
• Supported frameworks/algorithms (listed): MediaPipe, OpenCV; Gmapping, Cartographer; slam_toolbox; Radar odometer RF2O; DWA path planning.
• AI visual interaction functions (listed): KCF object tracking, color tracking, QR code command control, visual line tracking.
• Voice interactive control: voice commands can control motion state; supports functions such as color tracking, color recognition, and visual line patrol.
• Cross-platform control: iOS/Android remote control app, iOS/Android mapping navigation app, PC host computer control, and 2.4G/USB wireless handle control.
• FPV real-time video transmission: connect to a local area network via mobile phone app to view real-time HD video captured by the robot.
• Multi-machine interconnection control: supports multi-robot simultaneous navigation with dynamic obstacle avoidance on the same map, and synchronous control via a single host computer.
• Teaching mode: manual single-leg movement on the host robot can be mirrored by a slave robot performing the same action.
• Learning resources: “200+ course examples” are referenced; accompanying ROS courses and AI large language model application examples are described (tutorial URL removed for compliance).

For pre-sales selection help or setup support, contact https://rcdrone.top/ or email support@rcdrone.top.

Specifications

Configuration Differences (as listed)

Raspberry Pi 5 (information shown)

Applications

• ROS2 learning and development for multi-legged (hexapod) locomotion and inverse kinematics.
• SLAM mapping/navigation experiments: single-point and multi-point navigation, road network planning, and dynamic obstacle avoidance.
• Computer vision and perception projects using depth camera and AI visual recognition (OpenCV / MediaPipe referenced).
• Voice interaction and multimodal large-model demonstrations (text/voice/visual integration referenced).
• Multi-robot synchronization control and multi-robot navigation (multi-machine interconnection control referenced).

Manuals

Tutorial resources are referenced for this product (manufacturer study page mentioned in source; external URL removed for compliance).

Details

Built on ROS2 for Raspberry Pi, Muto RS brings 18-DOF hexapod mobility together with AI perception for desktop robotics learning.

From SLAM mapping and navigation to vision and voice interaction, the platform is designed as an all-in-one ROS2 development kit.

Multimodal AI workflows pair with road-network planning concepts to support research demos and classroom instruction.

Choose a configuration that matches your controller and sensor needs, with options centered on Raspberry Pi compute.

Text, voice, and vision models can be integrated to build embodied intelligence behaviors in Python and ROS2.

Use high-level commands for movement, perception Q&A, target tracking, and autonomous navigation tasks.

SLAM-based perception supports multi-point navigation and target search behaviors across mapped environments.

Higher-level interaction demos include intent understanding, imitation learning behaviors, and environment exploration.

Built-in ROS2 packages connect LiDAR and depth camera data for mapping, point clouds, and obstacle awareness.

Vision algorithms and voice commands add hands-free control, with support for multi-robot coordination features.

Teaching mode and a full 18-DOF joint layout make it easier to demonstrate gaits and coordinated leg motion.

Inverse kinematics and gait planning help translate posture and stride settings into stable hexapod motion.

Adjust body height, stance, and walking speed to match different surfaces, demos, and navigation scenarios.

FPV video and action-mimic behaviors make demos more interactive for labs, clubs, and presentations.

Program in Python and control the robot from mobile apps, a PC host, or a wireless handle depending on your setup.

Develop and test in RViz simulation, then deploy to the ROS2 stack for repeatable robotics experiments.

Yahboom Muto RS ROS2 learning materials cover AI vision, Mediapipe, road network navigation, and ROS2 basics video tutorials.

The Muto RS hexapod uses a modular stack with components like lidar, a depth camera, Raspberry Pi controller, and serial bus servos for coordinated leg motion.

Orbbec Astra Pro Plus depth sensing and a 2D LiDAR SLAM module provide depth and mapping inputs for ROS2 robotics projects.

The kit includes an AI voice module with a wired speaker plus a 7.4V 9900mAh lithium battery pack for onboard power.

The Yahboom Muto RS ROS2 hexapod includes a multi-view mm dimension reference to help plan mounting clearance and placement.

The package list includes the assembled MUTO robot chassis plus optional add-ons like a Raspberry Pi 5, SLAM Lidar, and depth camera, along with power and audio accessories.