Zhi Chen

CAE (Canada), Chinese University of Hong Kong, Shenzhen

Papers

Total Citations

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About

Zhi Chen is a researcher whose work spans robotics, artificial intelligence, and autonomous systems, with particular focus on robot path planning, deep learning-based manipulation, and evolutionary computation. His most influential contribution, a 2011 paper on an improved artificial potential field method for mobile robot path planning, has garnered 68 citations and represents a meaningful advancement in the field — addressing longstanding limitations of traditional approaches by substituting force vectors with potential field intensity and introducing a repulsion coefficient to improve navigational reliability. This work has become a notable reference point for researchers tackling autonomous navigation challenges. Chen's interests extend into modern deep learning applications, as demonstrated by his 2020 investigation into robotic manipulation of flexible printed circuit boards, tackling the technically demanding problem of automating soldering processes for deformable, miniature components in 3C manufacturing environments. His earlier work from 2003 surveying genetic programming reflects a long-standing engagement with bio-inspired computational methods and their applications across pattern recognition, neural architecture synthesis, and robotic control. Collectively, Chen's research career illustrates a sustained commitment to advancing intelligent, adaptive robotic systems across both foundational algorithmic and applied industrial contexts.