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About

Lingyan Ran is a leading researcher in vision-based robotics, specializing in autonomous navigation and robotic manipulation. Her work centers on developing efficient, learning-driven systems that enable robots to perceive and interact with their environments using unconventional visual inputs. Ran’s most influential contribution is her pioneering use of uncalibrated spherical images for mobile robot navigation, as demonstrated in her highly cited 2017 paper (107 citations). This work introduced a Convolutional Neural Network (CNN) framework that bypasses traditional calibration requirements, significantly simplifying deployment for wheeled and quadrotor platforms. More recently, Ran addressed a critical challenge in industrial automation with DSC-GraspNet (2023, 17 citations), a lightweight CNN that achieves a strong balance between grasp detection accuracy and computational speed—essential for real-time robotic pick-and-place tasks. By advancing both navigation and manipulation, Ran’s research bridges fundamental perception problems with practical, deployable solutions. Her work has garnered over 130 total citations, reflecting its impact on autonomous robotics, particularly in scenarios requiring robust, calibration-free visual systems.