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About

Yinglian Jin is a leading researcher in robotics, specializing in bionic locomotion, intelligent actuation, and autonomous navigation for complex environments. Her work spans hexapod robots, climbing machines, and hydraulic legged systems, with a focus on kinematic modeling, gait design, and robust control. Jin’s most cited paper, “Hexapod robot kinematics modeling and tripod gait design based on the foot end trajectory” (2017, 20 citations), introduces a bio-inspired hexapod robot modeled on ant configuration, using DH parameters and homogeneous transformations to analyze workspace and enable stable tripod gaits. She also advanced detection robotics with “Path Planning for Detection Robot Climbing on Rotor Blade Surfaces of Wind Turbine Based on Neural Network” (2013, 6 citations), employing simulated annealing neural networks for adaptive path planning on curved surfaces. Her innovative work on “Modeling of pneumatic muscle with shape memory alloy and braided sleeve” (2010, 5 citations) explores smart actuators combining SMA wires with braided shells to enhance output force and mimic human muscle function. More recently, Jin has tackled challenges in underground localization, fusing binocular vision and IMU to overcome dynamic interference and weak textures. Her contributions to hydraulic hexapod robots, including sliding mode repetitive control, demonstrate her ongoing impact in heavy-payload, high-power-density robotic systems.