Baojun Shi
Papers
4
Total Citations
43
H-Index
4
About
Baojun Shi is a researcher whose work spans metaheuristic optimization, robotics mechanism analysis, and intelligent fire detection. Among his most influential contributions is the development of the RRT-Based Optimizer (2025), a novel metaheuristic algorithm inspired by rapidly-exploring random trees, which has already garnered 17 citations for addressing the no-free-lunch theorem’s challenge of solving diverse real-world optimization problems. In robotics, Shi advanced the mobility analysis of multi-loop coupled mechanisms using screw theory (2014, 10 citations), proposing a method that improves the load-to-weight ratio of construction robots—a critical step toward more efficient and robust robotic systems. He also contributed to public safety with a Random Forest and backpropagation neural network approach for video-based fire detection (2022, 10 citations), overcoming the low sensitivity of traditional detectors. More recently, Shi tackled environmental interference in inspection robotics with an adaptive reflection detection strategy for pointer meters using YOLOv5s (2023, 6 citations). His work demonstrates a strong interdisciplinary impact, bridging algorithmic innovation and practical engineering challenges, with growing recognition in both optimization and applied robotics communities.
Research Focus
Key Achievements
Top Papers
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