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

2025-04-16

AI Summary

Conventional biomimetic design of underwater soft robots relies heavily on empirical analogy, lacking systematic mapping mechanisms from biological principles to engineering implementations. Method: This study proposes a bio-strategy reverse-mapping framework, introducing a structured “Function–Behavior–Characteristic-in-Environment” (F-B-Cs in E) knowledge model. It integrates biological functional semantic modeling, NLP-driven cross-domain retrieval, and multi-criteria decision analysis (MCDA) to enable interpretable and scalable translation of natural evolutionary solutions into engineered actuation mechanisms, energy distribution strategies, and locomotion patterns. Contribution/Results: The framework transcends traditional analogy-based biomimicry, achieving a 23.6% improvement in actuation efficiency and enhanced adaptability to complex hydrodynamic environments in underwater soft robot case studies—demonstrating both methodological generality and practical engineering viability.

Technology Category

• Intelligent Robots: Embodied AI
• Machine Learning: Bio-inspired Learning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Web data generation and simulation

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.

Problem

• Mapping biological strategies to engineering solutions systematically
• Converting biological features to robot design using NLP methods
• Optimizing underwater soft robot mechanisms via biomimetic framework

Innovation

• Biomimetic design framework with biological strategy inversion
• F-B-Cs in E model with NLP and decision-making
• Underwater soft robot optimization via biomimetic solutions