Papers
8
Total Citations
28
H-Index
3
About
Xingang Miao’s research bridges robotics and industrial automation, with a focus on object detection, path planning, and intelligent control for specialized applications. His most cited work, "LPO-YOLOv5s: A Lightweight Pouring Robot Object Detection Algorithm" (2023, 8 citations), tackles the challenge of deploying deep learning models in resource-constrained casting environments, improving accuracy for molten metal pouring tasks. He further advances this domain with "CP-RDM: A New Object Detection Algorithm for Casting and Pouring Robots" (2024, 2 citations), refining detection in complex workshop settings. Miao also contributes to nursing robotics, developing safety path planning via an improved A* algorithm (2019, 4 citations) and simulating dual-arm nursing robot workspaces (2019, 3 citations). His earlier work on welding robots includes error compensation using wavelet neural networks (2011, 5 citations) and torch pose fitting with fuzzy control (2010, 2 citations). Across these areas, Miao’s research demonstrates a consistent drive to enhance robot autonomy and precision in challenging real-world environments, from foundries to healthcare.
Research Focus
Key Achievements
Top Papers
- 1LPO-YOLOv5s: A Lightweight Pouring Robot Object Detection Algorithm8 citations · 2023
- 2
- 3Nursing Robot Safety Path Planning Based on Improved A star Algorithm4 citations · 2019
- 4Workspace Simulation and Analysis of a Dual-Arm Nursing Robot3 citations · 2019
- 5CP-RDM: a new object detection algorithm for casting and pouring robots2 citations · 2024
- 6Research on Track Fitting of Big Frame Intersection Line Seams2 citations · 2011
- 7
- 8