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About

Suli Zou is a robotics researcher whose work spans continuum gripping, rehabilitation robotics, and bionic underwater systems. Her most cited paper, "A Versatile Continuum Gripping Robot with a Concealable Gripper" (2023, 38 citations), addresses a critical challenge in constrained environments by designing a gripper that can be hidden within the robot body, enabling safer navigation and grasping in narrow spaces. In rehabilitation robotics, she proposed an impedance sliding-mode control method with stiffness scheduling (2024, 29 citations), which reproduces therapist-like movements for patient motion training. Zou has also made significant contributions to bionic robotic fish, developing dynamic models combining Lagrangian mechanics with data-assisted techniques and implementing ADRC-based orientation control. Her work on lightweight underwater object detection using an enhanced YOLOv8 algorithm (2024) demonstrates her commitment to real-time, computationally efficient solutions for underwater robotics. More recently, she has explored vision-based formation control and game-theoretic optimization for robotic arm inverse kinematics. With a total of over 80 citations across her publications, Zou’s research is notable for its practical focus on enabling robots to operate effectively in complex, unstructured environments—from hospital rehabilitation rooms to underwater ecosystems.