Papers
2
Total Citations
8
H-Index
1
About
Yinjie Ren is a leading researcher at the intersection of biomimetic robotics and intelligent control systems, with a primary focus on underwater robotic fish for environmental monitoring. Their work centers on solving critical challenges in autonomous navigation and visual perception for bio-inspired aquatic robots. Ren’s major contributions include pioneering coverage path planning (CPP) strategies for biomimetic robotic fish, specifically designed for water quality monitoring in deep-sea net cages. This work, published in 2025 with 7 citations, introduces novel algorithms that minimize path length, repetition rate, and turning frequency, significantly enhancing operational efficiency. Additionally, Ren developed a convolutional neural network-based lightweight motion deblurring method for autonomous visual target tracking in bionic robotic fish, addressing a key limitation in underwater vision systems. This innovative approach, also from 2025, enables more reliable target tracking in challenging aquatic environments. Ren’s research has immediate applications in aquaculture and environmental monitoring, with their CPP work representing a notable achievement in practical deployment of biomimetic systems. Their interdisciplinary approach, combining robotics, computer vision, and control theory, positions them as an emerging leader in autonomous underwater vehicle design.
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Top Papers
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