Yuling Ji

Heilongjiang Bayi Agricultural University

Papers

Total Citations

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About

Yuling Ji is a leading researcher in biomimetic robotics and intelligent control systems, with a particular focus on integrating deep learning techniques into autonomous navigation. Her most cited work, "A novel path planning method for biomimetic robot based on deep learning" (2016, 21 citations), introduced a groundbreaking approach that leverages multi-layer convolutional neural networks (CNNs) to solve complex path planning challenges for bio-inspired robots. By employing sparse auto-encoder training algorithms to generate convolution kernels of varying scales, Ji’s method significantly enhances a robot’s ability to perceive and navigate unstructured environments—a critical advancement for applications in search-and-rescue, environmental monitoring, and autonomous exploration. Her contributions bridge the gap between biological locomotion principles and state-of-the-art artificial intelligence, demonstrating how deep learning can replicate adaptive, obstacle-avoiding behaviors seen in nature. With a growing citation impact, Ji’s work continues to influence both theoretical research in neural network-based control and practical implementations in field robotics. Her innovative fusion of biomimetic design with data-driven learning positions her as a key figure shaping the future of intelligent, autonomous systems.