Peizhong Ge

Papers

Total Citations

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About

Peizhong Ge is a researcher advancing the safety and autonomy of human–robot collaboration, with a primary focus on collision detection and torque prediction in robotic systems. His work addresses a critical challenge in cooperative robotics: ensuring human safety without relying on expensive or bulky external sensors. Ge’s major contributions include pioneering a collision detection method based on time-series analysis (TSA), which leverages internal robot signals to identify unexpected contacts. This approach, detailed in his 2020 paper, has garnered 13 citations and laid the groundwork for more intelligent detection systems. Building on this, Ge introduced a Long Short-Term Memory (LSTM) neural network model for external torque prediction in 6-DOF robots, published in 2023. This work, with 11 citations, demonstrates his ability to integrate deep learning with real-time robotic control, enabling more accurate and proactive collision detection. Ge’s research is notable for its practical impact, offering cost-effective solutions that enhance safety in industrial and service robotics. His achievements highlight a commitment to making robots safer and more responsive in shared workspaces, a vital step toward widespread human–robot interaction.