Max Q.‐H. Meng
Chinese University of Hong Kong, University of Alberta, Purdue University West Lafayette, Harbin Institute of Technology, Chinese University of Hong Kong, Shenzhen, Southern University of Science and Technology, National University of Singapore, Institute of Electrical and Electronics Engineers, Shenzhen Institutes of Advanced Technology, The University of Tokyo, Shenzhen Academy of Robotics, Hefei Institutes of Physical Science, Institute of Intelligent Machines, University of Hong Kong, City University of Hong Kong, Shenzhen Research Institute
Papers
339
Total Citations
9,565
H-Index
49
About
Max Q.-H. Meng is a distinguished robotics and artificial intelligence researcher whose work spans robot motion planning, simultaneous localization and mapping (SLAM), biologically inspired neural networks, and medical robotics. He is perhaps best known for pioneering the integration of neural network approaches into real-time robotic navigation, with his early foundational work on dynamic collision-free trajectory generation and bioinspired neurodynamics accumulating hundreds of citations and laying groundwork that continues to influence the field today. His contributions to learning-based optimal path planning, particularly the Neural RRT* algorithm (533 citations), have significantly advanced how robots efficiently explore and navigate complex state spaces. Meng has also made substantial contributions to robust RGB-D SLAM in dynamic environments, developing motion removal techniques that improve reliability in real-world settings. A particularly notable dimension of his research is medical robotics: his work on magnetic localization for capsule endoscopy opened new frontiers in minimally invasive diagnostics, while his more recent overview of autonomous robotic ultrasound systems reflects his ongoing commitment to clinical applications. With multiple papers exceeding 150 citations, Meng's research has demonstrably shaped modern autonomous robotics across both industrial and healthcare domains.
Research Focus
Key Achievements
Top Papers
- 1Neural RRT*: Learning-Based Optimal Path Planning533 citations · 2020
- 2Improving RGB-D SLAM in dynamic environments: A motion removal approach384 citations · 2016
- 3An efficient neural network approach to dynamic robot motion planning250 citations · 2000
- 4Motion removal for reliable RGB-D SLAM in dynamic environments202 citations · 2018
- 5Neural network approaches to dynamic collision-free trajectory generation187 citations · 2001
- 6A Bioinspired Neurodynamics-Based Approach to Tracking Control of Mobile Robots157 citations · 2011
- 7Efficient magnetic localization and orientation technique for capsule endoscopy157 citations · 2005
- 8EB-RRT: Optimal Motion Planning for Mobile Robots153 citations · 2020
- 9EFFICIENT MAGNETIC LOCALIZATION AND ORIENTATION TECHNIQUE FOR CAPSULE ENDOSCOPY143 citations · 2005
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