Papers
56
Total Citations
707
H-Index
14
About
Minzhou Luo is a prominent robotics researcher whose work spans robot kinematics, intelligent control, manipulation, and autonomous mobile systems. Over two decades of contributions, Luo has consistently tackled fundamental challenges in robotic precision and reliability, developing innovative solutions that bridge theoretical rigor and practical application. Luo's most influential work addresses the inverse kinematics problem in robot control, with a 2017 paper on PSO-optimized BP neural network algorithms for puncture robot positioning earning 87 citations — a testament to the practical significance of improving surgical and industrial robot accuracy. His research on kinematic calibration, sensorless collision detection, and adaptive fuzzy sliding mode controllers further demonstrates a sustained commitment to safe, precise robotic operation across redundant and dual-arm systems. Beyond manipulation, Luo has made notable contributions to mobile robotics, proposing adaptive federated Kalman filter algorithms for indoor positioning (50 citations) and sensor-fusion-based object detection that integrates LiDAR point clouds with visual data. His 2021 work on inflatable particle-jamming grippers reflects creative forays into soft robotics, while earlier studies on underactuated robot hands and finger posture design showcase a career grounded in dexterous mechanical design. With over 370 cumulative citations, Luo's research continues to shape intelligent robotic systems research globally.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5Object Detection Based on Fusion of Sparse Point Cloud and Image Information33 citations · 2021
- 6
- 7Grasp characteristics of an underactuated robot hand29 citations · 2004
- 8
- 9A unified dynamic control method for a redundant dual arm robot23 citations · 2015
- 10Analysis and design for changing finger posture in a robotic hand21 citations · 2010