Yanglong Zheng

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About

Yanglong Zheng is a leading researcher in robotic assembly and human-robot collaboration, with a primary focus on dual-arm robot coordination and sensor-based manipulation. His most significant contributions center on peg-in-hole assembly tasks, where he has pioneered two-phase assembly schemes using force/torque (F/T) sensors to enable compliant, human-inspired behaviors in dual-arm robots like Baxter. His 2017 paper on this topic, with 38 citations, introduced a novel approach that mimics human coordinated actions to improve precision and adaptability in industrial assembly. A subsequent 2020 paper, cited 30 times, further advanced this work by developing a master-slave coordination framework, demonstrating how compliant robots can achieve reliable assembly through force-guided strategies. Zheng’s research has practical implications for manufacturing automation, reducing the need for rigid programming by leveraging sensor feedback. He has also explored robot teaching methods using motion tracking with particle filters, though this work has garnered fewer citations. His achievements highlight a commitment to bridging human dexterity and robotic efficiency, making his work essential for researchers in intelligent robotics and industrial automation.