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Walking on Rough Terrain with Any Number of Legs
Zhuoyang Chen1, Xinyuan Wang2, Shai Revzen3
1Electrical Engineering and Computer Science, University of Michigan, 1301 Beal Avenue, Ann Arbor, Ann Arbor, Michigan, 48109-1382, United States.
Bioinspiration & Biomimetics
|July 29, 2026
Summary
This study introduces a novel control architecture for multi-legged robots, inspired by arthropod agility. The system adapts to terrain, offering a computationally efficient solution for robotic locomotion.
Area of Science:
- Robotics
- Biomimicry
- Control Systems
Background:
- Robots require advanced control for navigating complex, unstructured environments.
- Existing multi-legged robot control methods include machine learning, Central Pattern Generators (CPGs), and open-loop control.
- Arthropod locomotion offers a model for agile and robust robotic movement.
Purpose of the Study:
- To develop and validate a novel, computationally light-weight control architecture for multi-legged robots.
- To enable robots to navigate rough terrain adaptively.
- To bridge the gap between event-driven and CPG-based control systems.
Main Methods:
- A segmental robot control architecture was designed with identical state machines per segment.
- The architecture utilizes input from preceding segments for coordinated control.
- Simulations were conducted for robots with 6 to 16 legs on rough terrain.
Main Results:
- The proposed architecture demonstrated adaptive control, coupling to the ground when present.
- It generated fictive locomotion in the absence of ground contact.
- The control system was validated in simulation for a range of leg configurations (6-16 legs).
Conclusions:
- The developed architecture offers an adaptive and computationally efficient controller for multi-legged robots.
- It serves as a potential baseline for machine learning controller development.
- The approach mimics arthropod agility for improved robotic navigation.
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