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<p>Inspired by the exceptional locomotive efficiency and adaptability of snakes across diverse terrains, this study presents a bio-inspired control framework for snake robots that integrates a Central Pattern Generator (CPG) based on the Matsuoka oscillator with Genetic Algorithm (GA)-optimized parameters to achieve adaptive multi-modal locomotion in unstructured terrains. Inspired by snakes’ exceptional terrain adaptability, the proposed CPG architecture generates rhythmic undulatory motions and enables seamless transitions between serpentine, rectilinear, and concertina gaits through dynamic parameter modulation. Leveraging the Matsuoka oscillator’s inherent stability and nonlinear dynamics, the system adapts to environmental variations while maintaining rhythmic output. Key innovations include the application of GA for optimizing critical parameters, which reduced motion cycle duration by 25%, improved convergence speed by 150%, and minimized steady-state amplitude fluctuations by 40% compared to baseline models. Experimental validation demonstrated the robot’s ability to navigate cluttered environments and inclined surfaces while carrying payloads, supported by its modular elastic-wheel design. This work pioneers a synergistic approach combining biologically inspired control with evolutionary optimization, offering a scalable framework for adaptive robotics. The results highlight significant advancements in gait versatility, energy efficiency, and terrain adaptability, with broad implications for search-and-rescue, environmental monitoring, and industrial inspection applications.</p>

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Bio-inspired CPG Control with Genetic Algorithm-optimized Multi-modal Serpentine Locomotion in Unstructured Terrains

  • 期刊论文

  • 发表日期：

    2026年6月30日

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  • Junqi Mu,
  • Yiyang Wei,
  • Xuping Hou,
  • Xiaofeng Zong

摘要

Inspired by the exceptional locomotive efficiency and adaptability of snakes across diverse terrains, this study presents a bio-inspired control framework for snake robots that integrates a Central Pattern Generator (CPG) based on the Matsuoka oscillator with Genetic Algorithm (GA)-optimized parameters to achieve adaptive multi-modal locomotion in unstructured terrains. Inspired by snakes’ exceptional terrain adaptability, the proposed CPG architecture generates rhythmic undulatory motions and enables seamless transitions between serpentine, rectilinear, and concertina gaits through dynamic parameter modulation. Leveraging the Matsuoka oscillator’s inherent stability and nonlinear dynamics, the system adapts to environmental variations while maintaining rhythmic output. Key innovations include the application of GA for optimizing critical parameters, which reduced motion cycle duration by 25%, improved convergence speed by 150%, and minimized steady-state amplitude fluctuations by 40% compared to baseline models. Experimental validation demonstrated the robot’s ability to navigate cluttered environments and inclined surfaces while carrying payloads, supported by its modular elastic-wheel design. This work pioneers a synergistic approach combining biologically inspired control with evolutionary optimization, offering a scalable framework for adaptive robotics. The results highlight significant advancements in gait versatility, energy efficiency, and terrain adaptability, with broad implications for search-and-rescue, environmental monitoring, and industrial inspection applications.