Introduction
As robotics research moves from simple wheeled platforms to complex embodied intelligence systems, multi-legged robots are becoming a critical experimental direction. Compared with traditional robot cars, hexapod robots introduce higher-dimensional motion control, richer environmental interaction, and significantly more challenging algorithm validation scenarios.
Muto RS, developed by Yahboom, is a desktop-level AI large model hexapod robot designed specifically for ROS2 developers, robotics researchers, and advanced learners who want to explore bionic locomotion, multi-sensor fusion, and embodied intelligence on a real, controllable hardware platform.
Watch the Demo
1. 18DOF Hexapod Structure: A True Bionic Motion Platform
One of the core strengths of Muto RS is its 18-degree-of-freedom hexapod structure.
Each leg is driven by high-torque 35KG metal serial bus servos, allowing precise control of:
Gait planning
Balance adjustment
Terrain adaptation
Posture correction
Compared with quadruped or wheeled robots, a hexapod platform provides:
Higher stability
More flexible motion combinations
Better suitability for uneven terrain experiments
This makes Muto RS ideal for bionic locomotion algorithms, inverse kinematics validation, and multi-legged coordination research.
2. Built on ROS2 + Raspberry Pi 5: Open, Scalable, Developer-Friendly
Muto RS is fully developed on ROS2, supporting modern robotics workflows including:
Modular node architecture
Topic-based communication
RViz visualization and simulation
Powered by Raspberry Pi 5, the platform offers:
Python3 programming
Strong community support
Easy integration with AI frameworks and Docker containers
This combination ensures that Muto RS is not a closed demo robot, but a long-term scalable research platform suitable for continuous project expansion.
3. Multi-Sensor Fusion: From Perception to Understanding
Muto RS integrates multiple perception modules:
Depth camera for 3D visual perception
LiDAR for mapping and navigation
Voice interaction module for human-robot communication
With these sensors, users can implement:
3D SLAM mapping and navigation
LiDAR obstacle avoidance and tracking
AI visual recognition and interaction
Voice-controlled task execution
This multi-sensor setup enables experiments that go beyond perception, moving toward environmental understanding and decision-making.
4. AI Large Model Integration: Toward Embodied Intelligence
Unlike traditional ROS robots that focus only on motion and perception, Muto RS introduces AI large model capabilities.
By combining:
Visual perception
Voice interaction
High-level reasoning models
Muto RS supports advanced scenarios such as:
Natural language command understanding
Scene-aware behavior execution
Embodied intelligence experiments
Multi-task coordination and reasoning
This makes it suitable for AI research, intelligent service robot exploration, and next-generation human-robot interaction studies.
5. Multi-Robot Collaboration & Simulation Support
Muto RS also supports:
Multi-machine communication
Multi-robot synchronization control
RViz simulation and virtual testing
Researchers can verify algorithms in simulation before deploying them to real hardware, significantly improving development efficiency and reliability.
Tutorial:
https://www.yahboom.net/study/Muto-RS
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