We at r π Lab (Robotics, Perception & AI Lab) aim at elevating the societal impact of robotics research by delivering tangible services and benefits to general public through solid theoretical investigations and focused technological developments. Build on our internationally leading achievements, our current research efforts are concentrated on

 

• Robotic wireless capsule endoscopy with image based automatic diagnosis,
• Surgical robotic system to facilitate orthopedic surgery and painless/scarless NOTES surgical procedures,
• Fundamental sciences and key enabling technologies for quality and healthy living of the elderly aging population, such as robotic assistive devices, non-touching and wearable sensing and perception devices and systems for healthcare monitoring, among others.
• Robotic perception and scenario intelligence to enable service robots to interact better with general public and people with better understanding of the human intension and the working scenarios and environments, with practical solutions such as empowering robots to move and work in densely populated areas and environment just like human beings, collecting and deploying passenger trolleys in busy airports and operating untrained elevators, autonomously with support from cloud-based AI decision platforms.

 

For the past 18 years, total research funding: HK$34,067,594 and RMB¥14,970,000 (equivalent to US$6.5 millions, avg. ~US$360K/Y).

 

For the past 28 years, total research outputs: 152 journal & 468 conference papers, 9 book chapters, 36 patents, and 146 invited talks.

 

Publication citations:  Google Scholar Citation Indices: h-index = 46 (33 since 2013) with 8,280 citations (4,789 since 2013).

                              Thomson Reuters ResearchID Citation Metrics: h-index = 27 with 514 papers and 2,818 citations to 484 papers.

 

Journal publication samples since 2016:

 

1. J. Wang, S. Song, H. Ren, C. Lim, and M. Q.-H. Meng, “Surgical instrument tracking by multiple monocular modules and a sensor fusion approach,” IEEE Transactions on Automation Science and Engineering, an IEEE Early Access Article to appear in 2018 with
2. C. Wang, J. Cheng, J. Wang, X. Li, and M. Q.-H. Meng, “Efficient object search with belief road map using mobile robot,” IEEE Robotics and Automation Letters, vol. 3, no. 4, pp. 3081-3088, Oct. 2018.
3. Y. Sun, M. Liu, and M. Q.-H. Meng, “Motion removal for reliable RGB-D SLAM in dynamic environments,” Robotics and Autonomous Systems, vol. 108, pp. 115-128, Oct. 2018.
4. W. Chi, J. Wang, and M. Q.-H. Meng, “A gait recognition method for human following in service robots,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 48, no. 9, pp. 1429-1440, Sep. 2018.
5. Y. Yuan, D. Li, and M. Q.-H. Meng, “Automatic polyp detection via a novel unified bottom-up and top-down saliency approach,” IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 4, pp. 1250-1260, Jul. 2018.
6. Y. Yuan, X. Yao, J. Han, L. Guo, and M. Q.-H. Meng, “Discriminative joint-feature topic model with dual constraints for WCE classification,” IEEE Transactions on Cybernetics, vol. 48, no. 7, pp. 2074-2085, Jul. 2018.
7. S. Song, C. Zhang, L. Liu, and Max Q.-H. Meng, "Preliminary study on magnetic tracking-based planar shape sensing and navigation for flexible surgical robots in transoral surgery: Methods and phantom experiments," International journal of computer assisted radiology and surgery, vol. 13, no. 2, pp. 241-251, Feb. 2018.
8. H. Dai, S. Song, X. Zeng, S. Su, M. Lin, and M. Q.-H. Meng, "6-D electromagnetic tracking approach using uniaxial transmitting coil and tri-axial magneto-resistive sensor," IEEE Sensor Journal, vol. 18, no. 3, pp. 1178-1186, Feb. 2018.
9. Y. Zeng, H. Yu, H. Dai, S. Song, M. Lin, B. Sun, W. Jiang. and Max Q.-H. Meng, "An improved calibration method for a rotating 2D LIDAR system," Sensors, vol. 18, no. 2, 12 pages, Feb. 2018.
10. Z. Min, H. Ren, and Max Q.-H. Meng, "Estimation of surgical tool-tip tracking error distribution in coordinate reference frame involving pivot calibration uncertainty," Healthcare Technology Letters, vol. 4, no. 5, pp. 193-198, Nov. 2017.
11. S. Song, X. Qiu, W. Liu, and M. Q.-H. Meng, "An improved 6D pose detection method based on opposing-magnet pair system and constraint multiple magnets tracking algorithm," IEEE Sensor Journal, vol. 17, no. 20, pp. 6752-6759, Aug. 2017.
12. X. Qiu, S. Song, and M. Q.-H. Meng, “A novel 6-D pose detection method using opposing–magnet pair system,” IEEE Sensors Journal, vol. 17, no. 9, pp. 2642-2643, May 2017.
13. Y. Yuan and M. Q.-H. Meng, “Deep learning for polyp recognition in wireless capsule endoscopy images,” Medical Physics, vol. 44, no. 4, pp. 1379-1389, Apr. 2017.
14. Y. Sun, M. Liu, and M. Q.-H. Meng, “Improved RGB-D SLAM in dynamic environments: A motion removal approach,” Robotics and Autonomous Systems, vol. 89, pp. 110-122, Mar. 2017.
15. S. Song, X. Qiu, J. Wang, and M. Q.-H. Meng, “Design and optimization strategy of sensor array layout for magnetic localization system,” IEEE Sensor Journal, vol. 17, no. 6, pp. 1849-1857, Mar. 2017.
16. L. Wang, M. Liu, and M. Q.-H. Meng, “A hierarchical auction-based mechanism for real-time resource allocation in cloud robotic systems,” IEEE Transactions on Cybernetics, vol. 47, no. 2, pp. 473-484, Feb. 2017.
17. Y. Yuan, B. Li, and M. Q.-H. Meng, “WCE abnormality detection based on saliency and adaptive locality-constrained linear coding,” IEEE Transactions on Automation Science and Engineering, vol. 14, no. 1, pp. 149-159, Jan. 2017.
18. C. Luo, SX Yang, X Li, and M. Q.-H. Meng, “Neural-dynamics-driven complete area coverage navigation through cooperation of multiple mobile robots,” IEEE Transactions on Industrial Electronics, vol. 64, no. 1, pp. 750-760, Jan. 2017.
19. S. Song, C. Hu, and M. Q.-H. Meng, “Multiple objects positioning and identification method based on magnetic localization system,” IEEE Transactions on Magnetics, vol. 52, no. 10, pp. 1-4, Oct. 2016.
20. S. Song, X. Qiu, J. Wang, and M. Q.-H. Meng, “Real-time tracking and navigation for magnetically manipulated untethered robot,” IEEE Access, vol. 4, pp. 7104-7110, Oct. 2016.
21. C. Hu, Y. Ren, X. You, W. Yang, S. Song, S. Xiang, X. He, Z. Zhang, and M. Q.-H. Meng, “Locating intra-body capsule object by three-magnet sensing system,” IEEE Sensor Journal, vol. 16, no. 13, pp. 5167-5178, Jul. 2016.
22. L. Wu, J. Wang, L. Qi, K. Wu, H. Ren, and M. Q.-H. Meng, “Simultaneous hand-eye, tool-flange, and robot-robot calibration for comanipulation by solving the AXB = YCZ problem,” IEEE Transactions on Robotics, vol. 32, no. 2, pp. 413-428, Apr. 2016.
23. Y. Yuan, B. Li, and M. Q.-H. Meng, “Improved bag of feature for automatic polyp detection in wireless capsule endoscopy images,” IEEE Transactions on Automation Science and Engineering, vol. 13, no. 2, pp. 529-535, Apr. 2016.
24. Y. Yuan, B. Li, and M. Q.-H. Meng, “Bleeding frame and region detection in the wireless capsule endoscopy video,” IEEE Journal of Biomedical and Health Informatics, vol. 20, no. 2, pp. 624-630, Mar. 2016.
25. H. Liu, J. Gu, M. Q.-H. Meng, and W.-S. Lu, “Fast weighted total variation regularization algorithm for blur identification and image restoration,” IEEE Access, vol. 4, pp. 6792-6801, Jan. 2016.