Manufacturing Engineering and Engineering Management

City University of Hong Kong

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Micro-robotics network and biomedical devices

The micro robotics research group was formally established in MEEM department in 2007, after several-year research collaboration amongst a few members. The goal of the group is to enable the formation of a world-class research team in Hong Kong on the chosen area, being internationally competitive with a critical mass.

Background

In recent years, interest in a number of technologically significant procedures with micro robotics technology, such as table-top manufacturing and operations with robot network, team intelligence of swarms of micro robots, the creation of transgenic organisms through insertion of genetic material into embryos and other related areas has grown substantially. Increasing demands for these and other relevant processes have provided new research opportunities within the field of robotics and automation, at the micro-scale. In particular, there are two significant issues to be addressed: micro robot network and micro robot manipulation especially for bioengineering.

Study on micro robotics network systems has become more necessary than ever before to successfully execute various tasks in a dynamic and uncertain environment. A typical application is that swarm of micro robots engaged in multi-robot tasks as a team must alter their formation according to the changing environment, and in response to unexpected changes in the task requirements. Relevant applications include tasks such as exploration, cooperative robot reconnaissance and manipulation, formation flight control, satellite clustering, and control of groups of unmanned micro vehicles. The developments of robotics theory up to today, however, are far away from satisfactory in addressing the challenges arising from those new applications.

On the other hand, the last few years have witnessed a growing interest in the development of methodologies for the manipulation of micro-scale biological materials for various biological processes which include: pro-nuclei DNA injection into cell embryos (oocytes) to produce transgenic organisms, microRNA injection into cells and embryos, intracytoplasmic sperm injection, gene therapy, and many others. While some of these processes have been automated, many, such as injection of material into embryos, are carried out manually by workers using high power microscopes. The success rate achieved by these highly skilled workers is relatively low, due to the significant challenges to control forces applied to biological cells, and variability of human manual control. Hence, there exists a clear demand for automation of micro-scale biomedical activities with robotics devices.

The research group will focus on the research areas of micro-robotics network and biomedical devices, and deliver a proof-of-concept demonstration incorporating robotics, automation, controls, vision, intelligence, nano and biological science.

Group Members

The core members of the group are:

• Prof. Gang Feng
• Dr. Dong Sun
• Dr. Youfu Li
• Prof. Hanxiong Li

Group Projects

• Project 1: Advanced Micro Robotics in Robot Network and Bio-manipulation (equipment grant)
• Project 2: Development of Automatic Biological Cell Manipulation System
• Project 3: Micro manipulation of biological cells with automatic robot systems
• Project 4: A 3D Intelligent Modelling, Learning and Control for Complex Tempo-Spatial Processes
• Project 5: Active Omni-Directional 3D Visual Sensing: Auto-Recalibration and 3D Reconstruction
• Project 6: Analysis and Synthesis of Switched Nonlinear Hamiltonian Control Systems
• Project 7: Automation of Inserting Genetic Materials into Embryos with a Robotic Bio-Manipulation System

Research Publications

1. H. Huang, D. Sun, J. K. Mills, and S. H. Cheng, "Robotics cell injection system with vision and force control: Towards automatic batch biomanipulation," IEEE Transactions on Robotics, vol. 25, 2009.



2. D. Sun, C. Wang, W. Shang, and G. Feng, "A synchronization approach to formation controls of multiple mobile robots," IEEE Transactions on Robotics, 2009.



3. H. Huang, D. Sun, J. K. Mills, W. J. Li, and S. H. Cheng, "Visual-based impedance control of out-of-plane cell injection systems", IEEE Transactions on Automation Science and Engineering, vol. 6, no. 3, July 2009.



4. Y. Tan, D. Sun, W. Huang, S. H. Cheng, "Mechanical modeling of biological cells in microinjection," IEEE Transactions on Nanobioscience, vol. 7, no. 4, pp. 257-266, Dec. 2008.



5. Z. Li, D. Sun, Z. Zhang, J. Song, and D. Xiao, "Control mechanism analysis of small-agent networks using a distinguished node model for urban traffic control," IEEE Transactions on Automation Science and Automation, vol. 5, no. 3, pp. 420-430, July 2008.



6. K Yuan, H. X. Li, and J. Cao, "Robust stabilization of the distributed parameter system with time delay via fuzzy control", IEEE Transactions on Fuzzy Systems, vo. 16, no. 3, pp. 567-584, 2008.



7. H. Wu, and H. X. Li, "H fuzzy observer-based control for a class of nonlinear distributed parameter systems with control constraints", IEEE Transactions on Fuzzy Systems, vol. 16, no. 2, pp. 502-516, 2008.



8. H. X. Li, and Z. Liu, "A Probabilistic Neural-Fuzzy Learning System for Stochastic Modelling", IEEE Transactions on Fuzzy Systems, vol. 16, no. 4, pp. 898-908, 2008.



9. Z. Liu, H. X. Li, and Y. Zhang, "Stochastic and incomplete data-based modeling with probabilistic wavelet system", IEEE Transactions on Systems, Man & Cybernetics-B, vol. 28, no. 2, pp. 310-319, 2008.



10. S. Wu and Y. F. Li, Flexible Signature Descriptions for Adaptive Motion Trajectory Representation, Perception and Recognition, Pattern Recognition, Vol. 42, No. 1, pp. 194-214, Jan. 2009.



11. S. Wu, and Y. F. Li, "On Signature Invariants for Effective Motion Trajectory Recognition," International Journal of Robotics Research, vol. 27, no. 8, pp. 895-917, August 2008.



12. S. Chen, Y. F. Li, at al, Vision Processing for Realtime 3D Data Acquisition Based on Coded Structured Light, IEEE Transactions on Image Processing, Vol. 17, No. 2, pp. 167-176, Feb. 2008.



13. Y. Wu, Y. F. Li, and Z. Hu, "Detecting and handling unreliable points for camera parameter estimation," International Journal of Computer Vision, vol. 79, no. 2, pp. 209-223, August 2008.



14. B. Zhang, Y. F. Li, and Y. Wu, "Self-recalibration of a structured light system via plane-based homography," Pattern Recognition, vol. 40, Issue 4, pp. 1368-1377, April 2007.



15. T.J. Zhang, G. Feng, and X.J. Zeng, "Output tracking of constrained nonlinear processes with offset-free input-to-state stable fuzzy predictive control," Automatica, vol.45, no.4, pp.900-909, April 2009.



16. H. Huang, G. Feng, and J. D. Cao, "Robust state estimation for uncertain neural networks with time-varying delay," IEEE Transactions on Neural Networks, vol.19, no.8, pp.1329-1339, August 2008.



17. G. Feng, M. Chen, D. Sun, and T. J. Zhang, "Approaches to robust filtering design of discrete time fuzzy dynamic systems," IEEE Transactions on Fuzzy Systems, vol.16, no.2, pp.331-340, April 2008.



18. T.J. Zhang, G. Feng, and J.H. Lu, "Fuzzy constrained min-max model predictive control based on piecewise lyapunov functions," IEEE Transactions on Fuzzy Systems, vol.15, no.4, pp.686-698, August 2007.



19. Y. Z. Wang, G. Feng, and D. Z. Cheng, "Simultaneous stabilization of a set of nonlinear port-controlled Hamiltonian Systems," Automatica, vol.43, no.3, pp.403-415, March 2007.

Last modified on 20 March, 2009