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March 21, 2026Biomimetics2 citationsOpen Access

Deep-Sea Biomimetic Manta Ray Robots: A Comprehensive Review Based on Operational Depth Spectrum, Structures, Energy Optimization, and Control Systems

LYLugang YeHLHongyuan LiuQDQiulin Ding

Key Points

  • This review examines the evolution and performance of biomimetic manta ray robots for deep-sea exploration.
  • Conducted a thorough literature review on biomimetic manta ray designs.
  • Analyzed the mechanical and operational challenges in deep-sea environments.
  • Investigated advances in energy efficiency and control mechanisms, such as deep reinforcement learning.
  • Identified unique structural design strategies crucial for pressure adaptation in deep-water settings.
  • Showed that hybrid gliding-flapping drives significantly enhance the endurance and efficiency of submersibles.
  • Outlined the shift from traditional control systems to intelligent models using deep reinforcement learning for better maneuverability.

Abstract

As deep-sea exploration transitions from large-scale search to precision pinpoint operations, the inherent limitations of traditional “rigid-body and propeller” vehicles—specifically in low-speed maneuverability, environmental compliance, and acoustic stealth—are becoming increasingly apparent. Leveraging its unique integrated “gliding-flapping” locomotion and exceptional maneuverability, the manta ray serves as an ideal biological prototype for next-generation deep-sea operational platforms. From a systems engineering perspective, this paper provides a comprehensive review of the current research status and technical evolution of biomimetic manta ray submersibles. First, a technical pedigree centered on “operational depth” is established, illustrating how design paradigms transition from “mechanism replication” in shallow waters to “pressure adaptation” at full-ocean depths. Second, the mechanical challenges in structural design are explored, demonstrating that a “rigid-flexible” gradient distribution strategy is critical to resolving the conflict between pressure resistance and propulsive compliance. Regarding energy and propulsion, the synergistic effects of hybrid gliding-flapping drives and integrated structural batteries in enhancing long-range endurance and energy efficiency are analyzed. Finally, the evolution of motion control architectures—transitioning from spinal-cord-inspired Central Pattern Generator (CPG) rhythmic control to Deep Reinforcement Learning (DRL) featuring embodied intelligence—is outlined.

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Cite This Study

Ye et al. (2026) studied this question.

synapsesocial.com/papers/69be35f96e48c4981c6747d2https://doi.org/10.3390/biomimetics11030216
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