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About

Yuelei Liu is a robotics researcher whose work focuses on enhancing the safety and autonomy of humanoid robotic arms, particularly for applications in elderly care and service robotics. Her key research areas include path planning, sensorless force detection, and intelligent control systems. Liu’s most notable contribution is an improved Rapidly-exploring Random Tree (RRT) path planning algorithm, designed to help robotic arms navigate complex, obstacle-filled environments—a critical advancement for service robots operating in unstructured homes. This work has garnered 13 citations. She has also pioneered sensorless external force detection methods using Backpropagation (BP) neural networks, enabling robotic arms to sense collisions and external forces without expensive joint torque sensors. These methods, with 8 and 3 citations respectively, improve robot safety and reduce hardware costs, making human-robot interaction more practical and affordable. Liu’s research bridges the gap between theoretical control algorithms and real-world deployment, addressing the pressing need for reliable, cost-effective service robots as populations age. Her work is foundational for researchers developing safer, more intelligent robotic assistants.