Papers
7
Total Citations
44
H-Index
3
About
Yuming Qi is a robotics and intelligent manufacturing researcher whose work focuses on path planning, mobile robot navigation, and industrial automation. His most significant contribution is the development of a multi-destination global path planning algorithm for mobile robots, which optimizes travel through obstacle-dense environments by introducing an "optimal obstacle value" concept—a novel approach that addresses the inefficiency of traditional single-destination planners. This work, published in 2021, has garnered 18 citations, reflecting its relevance to autonomous navigation challenges. Qi also advanced the field of digital twin technology for flexible production lines, proposing a framework that replaces serial manufacturing with integrated, network-expandable systems (15 citations). His additional research includes improving particle swarm optimization for robot path planning, designing a permanent magnetic adsorption wall-climbing robot for industrial derusting, and developing adaptive control methods for SCARA robots using RBF neural networks with fuzzy compensation. Notably, Qi applied self-organized critical theory to predict industrial robot faults, shifting maintenance from reactive to predictive. His recent work on accelerated degradation modeling using Wiener processes further demonstrates his commitment to enhancing robot reliability and lifespan.
Research Focus
Key Achievements
Top Papers
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- 3Path Planning of Mobile Robot Based on Improved Particle Swarm3 citations · 2020
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- 5RBF Network Adaptive Control of SCARA Robot Based on Fuzzy Compensation2 citations · 2020
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