Peng Zou

Zhejiang University of Technology, Zhejiang University

Papers

Total Citations

H-Index

About

Peng Zou is a robotics researcher specializing in robotic assembly automation, force control, and machine learning-based manipulation strategies. His work primarily focuses on solving the challenging peg-in-hole assembly problem — a fundamental yet complex task in industrial robotics that requires precise coordination between perception, planning, and control. Zou's most notable contribution is the development of intelligent assembly frameworks that integrate learning-based optimization with force control strategies. His 2020 paper, which has garnered 30 citations, introduced a multilayer perceptron network to address hole-searching problems combined with a hybrid force-position controller, significantly advancing the automation of precision assembly tasks. Building on this foundation, his earlier 2019 work (14 citations) established robust sensor-guided methodologies, including wrist force sensor calibration techniques to compensate for load gravity and sensor bias — a critical prerequisite for reliable force-feedback-driven assembly. Together, these contributions demonstrate Zou's systematic approach to bridging the gap between learning-based intelligence and classical control theory in robotic systems. His research holds meaningful implications for flexible manufacturing and industrial automation, offering pathways toward robots capable of performing delicate assembly operations with minimal human intervention. Students interested in robot manipulation, force control, or reinforcement learning in robotics will find his work particularly instructive.