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Inversion of biological strategies in engineering technology: in case underwater soft robot

Siqing Chen, He Xua, Xueyu Zhang, Zhen Ma

TL;DR

The study tackles the challenge of translating evolved biological strategies into engineering solutions by introducing a biomimetic design framework that leverages a Function-Behavior-Characteristic-Environment (F-B-Cs in E) knowledge model, NLP-based feature transformation, and a hybrid MCDM (VIKOR with rank correlation) to map biology to engineering. It combines large language models and knowledge graphs to screen biological prototypes across four dimensions (function, behavior, characteristic, environment) and outputs executable engineering strategies. The approach is validated on three underwater soft robot cases—tail-swing propulsion, jet propulsion, and autonomic peristalsis crawling—demonstrating improvements in drive mechanisms, power distribution, and motion patterns, with concrete performance metrics such as tail propulsion speed of 1.42 mm/s and crawling cycles of 46 mm. Overall, the framework enables scalable, cross-domain biomimetic innovation with potential impact on adaptive robotics design and cross-species strategy decoding.

Abstract

This paper proposes a biomimetic design framework based on biological strategy inversion, aiming to systematically map solutions evolved in nature to the engineering field. By constructing a "Function-Behavior-Feature-Environment" (F-B-Cs in E) knowledge model, combined with natural language processing (NLP) and multi-criteria decision-making methods, it achieves efficient conversion from biological strategies to engineering solutions. Using underwater soft robot design as a case study, the effectiveness of the framework in optimizing drive mechanisms, power distribution, and motion pattern design is verified. This research provides scalable methodological support for interdisciplinary biomimetic innovation.

Inversion of biological strategies in engineering technology: in case underwater soft robot

TL;DR

The study tackles the challenge of translating evolved biological strategies into engineering solutions by introducing a biomimetic design framework that leverages a Function-Behavior-Characteristic-Environment (F-B-Cs in E) knowledge model, NLP-based feature transformation, and a hybrid MCDM (VIKOR with rank correlation) to map biology to engineering. It combines large language models and knowledge graphs to screen biological prototypes across four dimensions (function, behavior, characteristic, environment) and outputs executable engineering strategies. The approach is validated on three underwater soft robot cases—tail-swing propulsion, jet propulsion, and autonomic peristalsis crawling—demonstrating improvements in drive mechanisms, power distribution, and motion patterns, with concrete performance metrics such as tail propulsion speed of 1.42 mm/s and crawling cycles of 46 mm. Overall, the framework enables scalable, cross-domain biomimetic innovation with potential impact on adaptive robotics design and cross-species strategy decoding.

Abstract

This paper proposes a biomimetic design framework based on biological strategy inversion, aiming to systematically map solutions evolved in nature to the engineering field. By constructing a "Function-Behavior-Feature-Environment" (F-B-Cs in E) knowledge model, combined with natural language processing (NLP) and multi-criteria decision-making methods, it achieves efficient conversion from biological strategies to engineering solutions. Using underwater soft robot design as a case study, the effectiveness of the framework in optimizing drive mechanisms, power distribution, and motion pattern design is verified. This research provides scalable methodological support for interdisciplinary biomimetic innovation.

Paper Structure

This paper contains 15 sections, 3 equations, 8 figures.

Table of Contents

  1. Introduction
  2. Related work
  3. Inversion method
  4. Inversion of biological strategies in engineering technology
  5. A textual knowledge framework of biological strategies and engineering technologies
  6. The transformation process of biomimetic features
  7. The process of determining engineering strategies based on hybrid multi-standard decision
  8. The general process of the inversion of biological principles
  9. Design process of underwater soft robot based on bionic inspiration
  10. Case study
  11. Soft underwater robot based on tail swing propulsion strategy
  12. Soft uncertaintynderwater robot based on fluid injection propulsion strategy
  13. Soft underwater crawling robot based on autonomic peristalsis
  14. Conclusion
  15. Acknowledgement

Figures (8)

  • Figure 1: Knowledge framework based on characteristics-behaviors-functions in environmental systems
  • Figure 2: Structure of the label classification model.
  • Figure 3: Bionic text conversion based on F-B-Cs model in E knowledge framework.
  • Figure 4: The total inversion process of biological strategy in engineering technology.
  • Figure 5: Bionic design process based on engineering mapping and strategy inversion.
  • ...and 3 more figures