A Multimodal Metameric Earthworm-Like Robot for Locomotion in Multiterrain Environments

In this research, we introduce a novel multimodal locomotion robot by integrating foldable wheeled structures with antagonistically deformable worm-like structures. This design reconciles the inherent challenges faced by conventional worm-like and wheeled robots. Our robot showcases two locomotion modes and five distinct motion types: worm-like rectilinear, sidewinding, circular crawling, rectilinear rolling, and spot turning. This versatility enables the robot to adapt to diverse environments and tasks. We have developed kinematic models for both crawling and rolling locomotion modes. By correlating these models with gait and control signals, we can qualitatively predict the characteristics of robot trajectories and locomotion performance indexes, underscoring the key role of kinematic models in locomotion. Furthermore, we experimentally demonstrate the robot's multiterrain adaptability. The robot adeptly navigates horizontal, curved, and vertical industrial pipes, along with narrow sewers, utilizing the worm-like crawling mode. Moreover, it efficiently traverses open terrains, including carpeted floors, mossy ground, gravel paths, grassland, steps, asphalt pavement, and uphill, employing the rolling mode. Impressively, the robot swiftly readjusts its posture after descending steps and promptly reorients itself through spot turns, showcasing its agility. This work highlights the advantages of combining multiple motion modalities and integrating structural and control signals, providing valuable insights for the advancement of future bionic and multimodal robots.

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Institutions

Publication Details

Journal
IEEE/ASME Transactions on Mechatronics
Published
2024-11-13
DOI
https://doi.org/10.1109/tmech.2024.3490795
Citations
6
Primary Topic
Modular Robots and Swarm Intelligence
Type
article
Field-Weighted Citation Impact
1.28

Funders

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article

A Multimodal Metameric Earthworm-Like Robot for Locomotion in Multiterrain Environments

Hongbin Fang, Zhihai Bi, Zihan He, Qiwei Zhang
6 citations
IEEE/ASME Transactions on Mechatronics
Modular Robots and Swarm Intelligence
1.28
article

A Multimodal Metameric Earthworm-Like Robot for Locomotion in Multiterrain Environments

Hongbin Fang, Zhihai Bi, Zihan He, Qiwei Zhang
article en
6 citations

Abstract

In this research, we introduce a novel multimodal locomotion robot by integrating foldable wheeled structures with antagonistically deformable worm-like structures. This design reconciles the inherent challenges faced by conventional worm-like and wheeled robots. Our robot showcases two locomotion modes and five distinct motion types: worm-like rectilinear, sidewinding, circular crawling, rectilinear rolling, and spot turning. This versatility enables the robot to adapt to diverse environments and tasks. We have developed kinematic models for both crawling and rolling locomotion modes. By correlating these models with gait and control signals, we can qualitatively predict the characteristics of robot trajectories and locomotion performance indexes, underscoring the key role of kinematic models in locomotion. Furthermore, we experimentally demonstrate the robot's multiterrain adaptability. The robot adeptly navigates horizontal, curved, and vertical industrial pipes, along with narrow sewers, utilizing the worm-like crawling mode. Moreover, it efficiently traverses open terrains, including carpeted floors, mossy ground, gravel paths, grassland, steps, asphalt pavement, and uphill, employing the rolling mode. Impressively, the robot swiftly readjusts its posture after descending steps and promptly reorients itself through spot turns, showcasing its agility. This work highlights the advantages of combining multiple motion modalities and integrating structural and control signals, providing valuable insights for the advancement of future bionic and multimodal robots.

IEEE/ASME Transactions on MechatronicsVol. 30(5)
Fudan University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 22%
Modular Robots and Swarm Intelligence
1.28
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