Lun Zhao

Hubei University of Technology, Guilin University of Electronic Technology

Papers

Total Citations

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About

Lun Zhao is a rising researcher in the fields of intelligent robotics and human-robot interaction, with a focus on enhancing the autonomy and adaptability of wearable and service robots. His work centers on two key areas: locomotion mode recognition for lower limb exoskeletons and path planning for autonomous robots. In his most-cited paper (2024, 7 citations), Zhao proposed a novel SE-DenseNet-LSTM hybrid model that integrates a dense convolutional network with long short-term memory and a channel attention mechanism. This model significantly improves the accuracy of recognizing human locomotion modes—such as walking, stair climbing, and running—enabling more flexible and responsive control of powered exoskeletons, a critical step toward seamless human-robot synergy. Additionally, his work on improving the Dynamic Window Approach (DWA) algorithm for epidemic prevention robots (2024, 1 citation) demonstrates his versatility, applying intelligent path planning to real-world public health challenges. Though early in his career, Zhao’s contributions are already shaping the future of assistive robotics and autonomous navigation, making him a promising voice in the intersection of deep learning and robotic control.