Papers
2
Total Citations
53
H-Index
2
About
Peng Wu is a researcher whose work spans robotics, autonomous navigation, and intelligent manufacturing systems. His research bridges foundational algorithmic challenges in robot motion planning with emerging applications in human-robot collaboration and sustainable manufacturing. Wu's most recognized contribution addresses a longstanding limitation in robot path planning: the "local minimum" and "unreachable target" problems inherent to traditional artificial potential field methods. His 2021 paper proposing an improved artificial potential field combined with a dynamic window approach for amphibious robot fish has garnered 47 citations, demonstrating significant influence within the robotics and autonomous systems community. This work reflects his ability to diagnose core algorithmic shortcomings and engineer practical solutions for complex, real-world robotic platforms. More recently, Wu has expanded his research horizon toward sustainable manufacturing intelligence. His 2025 work introduces a knowledge graph-driven process reasoning framework for human-robot collaborative disassembly of end-of-life products, leveraging graph attention networks and a novel SURD semantic mechanism to support intelligent task allocation — a timely contribution given growing global interest in circular economy principles. Together, Wu's portfolio reveals a researcher committed to advancing both the autonomy of robotic systems and their meaningful integration into human-centered, environmentally conscious industrial workflows.
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Top Papers
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