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About

Renjing Gao is a rising innovator in the field of soft robotics and intelligent automation, with a research focus on shape memory alloy (SMA) actuators and mobile robot navigation. Her work bridges the gap between novel material-driven mechanisms and practical robotic applications. Gao’s major contributions include the development of a dual-stage shape memory alloy actuated gripper (DAG), which enhances grasp performance through primary and secondary actuation—a design that has garnered early recognition with 7 citations. She also pioneered a multi-posture 3D printed actuator composed of VT-type bidirectional deflection fishbone-like modules, enabling complex deformation control for versatile robotic tasks. In the domain of autonomous navigation, Gao proposed the Super JPS (S‐JPS) algorithm, an optimal global path planning method that introduces super jump points to significantly improve efficiency for wheeled mobile robots. Her work, published in high-impact venues, demonstrates a unique ability to integrate material science with algorithmic design. With a growing citation record and a clear trajectory of innovation, Renjing Gao is establishing herself as a key contributor to next-generation robotic systems.