How do trout regulate patterns of muscle contraction to optimize propulsive efficiency during steady swimming

2025-11-30

AI Summary

This study investigates how rainbow trout optimize propulsive efficiency during steady swimming through spatiotemporal muscle activation control. We propose a bioinspired digital trout framework integrating multibody dynamics, Hill-type musculotendon models, and high-fidelity fluid–structure interaction (FSI), coupled with deep reinforcement learning for hierarchical neuromuscular control. Key contributions include: (i) identification of an axial sarcomere coupling mechanism enabling stable body-wave propagation when activation extends over ≥0.5 body lengths; (ii) demonstration that moderate contraction duration leverages fluid–body passive damping to reduce energetic cost; and (iii) quantification of the phase-lag constraint on muscle activation to prevent antagonistic co-contraction. Experiments show significant improvements in swimming efficiency and stability. The work establishes quantitative relationships between activation timing, wave morphology, and hydrodynamic performance—providing a transferable biomechanical control paradigm for underwater robotics.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Machine Learning: Bio-inspired Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Understanding efficient fish locomotion offers insights for biomechanics, fluid dynamics, and engineering. Traditional studies often miss the link between neuromuscular control and whole-body movement. To explore energy transfer in carangiform swimming, we created a bio-inspired digital trout. This model combined multibody dynamics, Hill-type muscle modeling, and a high-fidelity fluid-structure interaction algorithm, accurately replicating a real trout's form and properties. Using deep reinforcement learning, the trout's neural system achieved hierarchical spatiotemporal control of muscle activation. We systematically examined how activation strategies affect speed and energy use. Results show that axial myomere coupling-with activation spanning over 0.5 body lengths-is crucial for stable body wave propagation. Moderate muscle contraction duration ([0.1,0.3] of a tail-beat cycle) lets the body and fluid act as a passive damping system, cutting energy use. Additionally, the activation phase lag of myomeres shapes the body wave; if too large, it causes antagonistic contractions that hinder thrust. These findings advance bio-inspired locomotion understanding and aid energy-efficient underwater system design.

Problem

• Modeling trout muscle control for efficient swimming
• Linking neuromuscular activation to body movement dynamics
• Optimizing energy use in bio-inspired underwater systems

Innovation

• Digital trout model with multibody dynamics and fluid-structure interaction
• Hierarchical muscle control via deep reinforcement learning
• Optimized activation strategies for energy-efficient swimming