Papers
3
Total Citations
23
H-Index
3
About
Pengda Liu is an emerging researcher whose work sits at the intersection of robotics, computer vision, and intelligent systems, with a particular focus on service robotics for real-world applications. Liu's research addresses one of the most pressing societal challenges of our time: developing autonomous robotic systems capable of supporting an aging global population in indoor environments. A central thread running through Liu's work is the development of advanced object detection frameworks for service robots operating in complex, cluttered indoor scenes. Notably, Liu has contributed novel adaptations of deep learning architectures, including an improved Mask RCNN pipeline, to enable reliable multi-target detection under challenging real-world conditions. Complementing this vision-focused research, Liu has also made meaningful contributions to robot dynamics and control, developing sliding-mode control strategies enhanced by extended state observers for precise trajectory tracking in life support robots. Together, these papers have accumulated over 20 citations since 2023, a promising trajectory for an early-career researcher. Liu's work is particularly relevant for students and engineers interested in human-robot interaction, eldercare technology, and the integration of intelligent perception with robust motion control in assistive robotics systems.
Research Focus
Key Achievements
Top Papers
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