Core Insights - Bionics is driving innovation in intelligent robotic systems, particularly through the design and development of biomimetic robotic fish inspired by the superior maneuverability and perception of fish in complex underwater environments [1][4] - Traditional visual and sonar sensing technologies face performance limitations in challenging underwater conditions, while fish possess a unique lateral line system that provides a new paradigm for underwater robotic perception [1][4] - The research team from Peking University has proposed a novel data-driven framework that integrates modal decomposition and physical modeling for self-motion state estimation in biomimetic robotic fish [1][4] Summary by Sections Introduction to Bionics and Robotic Fish - Bionics connects nature and engineering, leading to advancements in intelligent robotic systems [1] - Fish exhibit exceptional maneuverability and perception in complex underwater environments, inspiring the design of biomimetic robotic fish [1] Challenges in Underwater Robotics - Traditional sensing technologies struggle in low-light and acoustically noisy underwater environments [1] - The unique lateral line system of fish, which senses water flow and pressure changes, offers a new approach for underwater robots [1] Development of Artificial Lateral Line System (ALLS) - Researchers have developed ALLS to simulate fish perception capabilities, applicable in flow field sensing, obstacle avoidance, and multi-fish coordination [1][4] - Accurate estimation of self-motion states, such as speed and trajectory, is crucial for autonomous intelligent robots [1] Proposed Framework for Self-Motion State Estimation - The new framework combines Proper Orthogonal Decomposition (POD) with physical modeling to estimate self-motion states [2][6] - The method extracts dominant modes related to fish movement from spatiotemporal pressure data collected by artificial lateral line sensors [2][6] Results and Validation - The model demonstrates robustness and generalization across various oscillation parameters, fish morphologies, and complex flow fields [4][14] - The research provides an efficient and reliable autonomous perception strategy for underwater biomimetic robots [4][14] Optimization of Sensor Layout - The study reveals redundancy in sensor arrangements and proposes optimized sensor layout strategies to reduce system complexity [11][13] - The optimal sensor distribution is validated through experiments, enhancing the understanding of fluid dynamics principles [13] Generalization to Complex Scenarios - The proposed method shows strong generalization capabilities in dynamic conditions and across different fish models [14] - It maintains robust state perception even in complex flow fields with wake interference, showcasing practical value [14] Conclusion and Future Directions - This research not only offers a solution for motion state estimation in robotic fish but also provides insights into the sensory mechanisms of real fish [16] - The integration of natural intelligence with engineering practices paves the way for the future development of intelligent, autonomous, and collaborative underwater robots [16]