Hard-to-soft part ratios in natural crawlers inform hybrid robotic design

Jiayi Lei, Changhong Linghu, Min Pan, K Jimmy Hsia

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Abstract

Most robots to date consist of either entirely hard parts or entirely soft parts. Nature, however, achieves a balance between hard and soft components, offering both adaptability and strength for adequate load-carrying capacity and locomotion in diverse environments. Inspired by nature, hybrid robotic designs, combining the flexibility of soft robots with the control and load-bearing capacity of hard robots, are being pursued. However, a proper range of the Hard-to-Soft Part Ratio (H2S-R) for the desirable performance of hybrid robots remains unclear. With a focus on crawlers, this study comprehensively compiles the H2S-R in crawling animals of four locomotion mechanisms: two-anchor, peristaltic, undulatory, and multi-legged crawling, and analyzes the trends and scaling in their performances. Results show distinct ranges of H2S-R in natural crawlers: 0.0005–0.003 for two-anchor crawlers, 0.08–0.09 for peristaltic, 0.02–0.08 for undulatory, and 1.0–2.4 for multi-legged. Within these ranges, except for the undulatory locomotion, increasing the H2S-R generally enhances their normalized crawling speed measured in body-lengths per second. Data for natural crawlers also show different ranges of normalized speeds as well as distinct scaling laws for different mechanisms. Comparisons with the performance of natural crawlers indicate that robotic crawlers have yet to achieve their full potential in crawling speed. This study provides guidelines for designing bio-inspired hybrid robots with potential applications in search and rescue, environmental monitoring, space exploration, and medical robotics.
Original languageEnglish
Article number102394
JournalExtreme Mechanics Letters
Early online date25 Jul 2025
DOIs
Publication statusPublished - 1 Sept 2025

Data Availability Statement

Data will be made available on request.

Funding

K.J.H., C.L. and J.L. acknowledge the financial support by the Ministry of Education of Singapore under Academic Research Fund Tier 2 (MOE-T2EP50122-0005). MP thanks the Advanced Research and Invention Agency (ARIA) Robot Dexterity Programme, SMRB-PR01-P17, for funding research. J.L. acknowledges the funding support for this project from Nanyang Technological University under the Undergraduate Research Experience on CAmpus (URECA) programme.

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