Pei Jiang
Papers
24
Total Citations
609
H-Index
12
About
Pei Jiang is a dynamic researcher whose work bridges two compelling frontiers of modern robotics: energy-efficient industrial robot systems and bio-inspired soft robotics. With a publication record spanning machine learning, mechanical design, and advanced manufacturing, Jiang has established himself as a versatile contributor to the robotics community, accumulating over 500 citations across his most influential works. In the domain of industrial robotics, Jiang has made significant strides in energy consumption modeling and optimization. His LSTM-based energy prediction framework (2023, 95 citations) addresses the practical challenge of unavailable dynamic parameters in real-world settings, while complementary trajectory planning and computational optimization studies further demonstrate his systematic approach to sustainable robot operation across milling, polishing, and laser processing applications. Equally impressive is Jiang's contributions to soft robotics. His electroadhesion-augmented soft grippers (2019, 91 citations) and comprehensive review of soft actuator design (2022, 62 citations) reflect a deep engagement with bio-inspired systems capable of navigating unstructured environments. His more recent work on multi-material embedded 3D printing and soft wall-climbing robots signals a forward-looking integration of fabrication innovation with functional versatility. For students exploring sustainable automation or next-generation robotic systems, Jiang's body of work offers both foundational insight and cutting-edge inspiration.
Research Focus
Key Achievements
Top Papers
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- 5An Efficient Computation for Energy Optimization of Robot Trajectory46 citations · 2021
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