Shuai Li’s homepage
Dr. Shuai Li (李帥)
Department of Computing
The Hong Kong Polytechnic University
Hom, Kowloon, Hong Kong
Looking for highly
motivated PhD students with strong background in robotics, control theory or
convex optimization (one of the above mentioned fields, not all of them) or
with research/working experience in programming and embedded system
development. If you have interest, please feel free to contact me.
(Multi-robot coordination and cooperation, Kinematic and dynamic analysis of
robotic systems, Path and motion planning, SLAM)
of Networked Systems (Multiple Manipulator Coordination, Multiple Mobile
Robots, Laser Array, Large-scale Battery Array, etc.)
Neural Networks (Optimal neural network design, Convergence analysis,
Convergence speed-up, applications to WSNs, robotics, winner-take-all problem)
Sensing Networks (Multi-robot source seeking, Multi-robot Plume front tracking)
Estimation, Optimization and Control (Consensus, Consensus filter)
Systems and control
of robotic systems
Plume Front Tracking
The recent deep water
horizon oil spill has posed great challenges to both robotics and ocean
engineering communities. It took months to estimate the extent of the
underwater plume, and the accuracy of these estimates will likely be debated
for years to come. The challenges motivate us to consider utilizing advanced
robotic techniques to monitor and track the propagation of oil plumes. We
propose a model-based method to track the dynamic plume front using multi-robot
platforms. Different from existing work purely relying on gradient
measurement, the transport model of pollution source is explicitly considered
in tracking control design. We ﬁrst study the problem using a single robot and
solve the problem in an estimation and control framework. We then extend it to
the multi-robot case in a nearest-neighbor communication structure, and have
the robots take formation along the plume front. The distributed control is
scalable to a large number of robots.
The snapshots of
the robot movements for a typical simulation run with 30 robots for cooperative
plume front tracking, where the pseudo-color indicates the concentration
distribution at each time step with the scale shown in the color-bar aside, the
black curve is the contour of the plume front, the hollow square in blue and
the hollow circle in red represent the position of the follower robots and the
true position of the boundary robots, respectively, the positions marked with
S1 and S2 are the two sources starting propagating chemicals in the ﬂow ﬁeld at
time t = 0.
In this work, we
consider such a problem: the spoilage of chemical sources propagates with the ﬂow
in the space or water and forms a time-varying concentration ﬁeld. We want to
design algorithms to drive a group of robots to track the plume front.
Potential applications include the monitoring of hazardous waste propagation,
the coverage of polluted areas, the surrounding of gas leakages, and chemical
plume tracing in underwater environments. In addition, the design of efficient
plume tracking algorithms for robotic system may also be helpful to the
understanding of many natural phenomenon, such as the
pheromone plume tracing demonstrated by male moth for mating, and the
navigation by exploiting the odor plume dynamics. In our formulation, each
robot maintains a gradient estimation, moves to the source by tracing the
gradient, and all together keeps a pre-deﬁned formation in movement. We present
two control algorithms with all-to-all and limited communications,
respectively. The estimation error is taken into account to derive robust
control algorithms. Comparing to existing methods, the proposed algorithm with
limited communications is fully distributed. Theoretical analysis validates the
effectiveness of our methods. Experimental results on the E-puck robot platform
demonstrate satisfactory performances in a light source seeking application.
source seeking experiment with the limited communication on three E-puck robots
winner-take-all (WTA) competition is widely observed in both inanimate and
biological media and society. Many mathematical models are proposed to describe
the phenomena discovered in different ﬁelds. These models are capable of
demonstrating the WTA competition. However, they are often very complicated due
to the compromise with experimental realities in the particular ﬁelds; it is
often difﬁcult to explain the underlying mechanism of such a competition from
the perspective of feedback based on those sophisticate models. In this work,
we make steps in that direction and present a simple model, which produces the
WTA competition by taking advantage of selective positive–negative feedback
through the interaction of neurons. Compared to existing models, this model has
an explicit explanation of the competition mechanism. The ultimate convergence
behavior of this model is proven analytically. Both theoretical and numerical
results validate the effectiveness of the dynamic equation in describing the
nonlinear phenomena of WTA competition. The proposed model is as simple as
follows in a compact form,
where u is
the input vector, x is the state vector, c0 and c1 are two positive constants.
Localization and Mapping (SLAM)
is a fundamental problem in multi-sensor fusion, tracking, and localization.
The joint compatibility test is commonly regarded as the true solution to the
problem. However, traditional joint compatibility tests are computationally
expensive, are sensitive to linearization errors, and require the knowledge of
the full covariance matrix of state variables. This work proposes a
posterior-based joint compatibility test scheme to conquer the three problems
mentioned above. This work also shows how to apply the proposed method to
various simultaneous localization and mapping (SLAM) algorithms. Theoretical
analysis and experiments on both simulated data and popular datasets show the
proposed method outperforms some classical algorithms, including sequential
compatibility nearest neighbor (SCNN), random sample consensus (RANSAC), and
joint compatibility branch and bound (JCBB), on precision, efﬁciency, and
the Intel Research Center dataset. (a) shows the map
constructed by overlapping raw observations according to the optimized poses,
and in the ﬁgure, green points are raw laser observations; blue triangles are
robot poses; and randomly colored blobs are extracted features. (b) shows the generated graph, and in the ﬁgure, green lines
indicate links of associations; yellow stars denote landmarks; and blue
triangles are robot poses. The convergence of the map and the links in the
graph show that the proposed method has high detection rate and low false
Muhammad Usman Rafique
M. Zhou, X. Luo and Z. You, Distributed Winner-take-all in Dynamic Networks, Regular paper,
IEEE Transactions on Automatic Control, 2016.
Y. Zhang and L. Jin, Kinematic Control of Redundant
Manipulators Using Neural Networks, IEEE Transactions on Neural Networks and
Learning Systems, 2016.
J. He, U. Rafique, and Y. Li, Distributed Recurrent
Neural Networks for Cooperative Control of Manipulators: A Game-Theoretic
Perspective, IEEE Transactions on Neural Networks and Learning Systems, 2016.
Y. Zhang, S. Li and Y. Zhang, Noise-Tolerant
ZNN Models for Solving Time-Varying Zero-Finding Problems: A Control-Theoretic
Approach, Brief paper, IEEE Transactions on Automatic Control, 2016.
X. Luo, M. Zhou, M.
Shang, S. Li and Y. Xia, A Novel
Approach to Extracting Non-Negative Latent Factors From Non-Negative Big Sparse
Matrices, IEEE Access, 2016.
Y. Zhang, S. Li, Integration-Enhanced
Zhang Neural Network for Real-Time Varying Matrix Inversion in the Presence of
Various Kinds of Noises, IEEE Transactions on Neural Networks and Learning
M. Mao, J. Li, L. Jin, S. Li and
Y. Zhang, Enhanced discrete-time Zhang neural network for time-variant matrix
inversion in the presence of bias noises, Neurocomputing,
Z. You, M. Zhou, X.
Luo, S. Li, Highly Efficient
Framework for Predicting Interactions Between Proteins, IEEE Transactions on
M. Khan, S. Li, Q. Wang and Z. Shao, Distributed
Multi-robot Formation and Tracking Control in Cluttered Environment, ACM Transactions
on Autonomous and Adaptive Systems, 2016.
H. Wang, X. Liu, P.
Liu and S. Li, Robust adaptive fuzzy
fault-tolerant control for a class of non-lower-triangular nonlinear systems
with actuator failures, Information Sciences, 60-74,2016
Y. Wang, Z. Qin, R.
Chen, Z. Shao, Q. Wang, S. Li and L.
Yang, A Real-Time Flash Translation Layer for NAND Flash Memory Storage Systems,
IEEE Transactions on Multi-Scale Computing Systems, 2016.
M. Khan, S. Li, Q. Wang and Z. Shao, CPS
Oriented Control Design for Networked Surveillance Robots with Multiple
Physical Constraints, IEEE Transactions on Computer-aided Design of Integrated
Circuits and Systems,2016.
M. Khan, S. Li, Q. Wang and Z. Shao, Formation
Control and Tracking for Co-operative Robots with Non-holonomic Constraints,
Journal of Intelligent and Robotic Systems, 82(1): 163-174, 2016.
H. Wang, H. Yang, X.
Liu, L. Liu, S. Li, Direct adaptive
neural control of nonlinear strict-feedback systems with un-modeled dynamics using
small-gain approach, International Journal of Adaptive Control and Signal
X. Luo, M. Zhou, H.
Leung, Y. Xia, Q. Zhu, Z. You, S. Li,
An Incremental-and-Static-Combined Scheme for
Matrix-Factorization-Based Collaborative Filtering, IEEE Transactions on
Automation Science and Engineering, 13(1), 333-343, 2016.
X. Luo, M. Zhou, S. Li, Z. You, Y.
Xia, and Q. Zhu. A Non-negative Latent Factor Model
for Large-scale Sparse Matrices in Recommender Systems via Alternating
Direction Method. IEEE Transactions on Neural Networks
and Learning Systems, 27(3), pp 579 - 592, 2016.
L. Wong, Z. You, S. Li, Y. Huang,
and G. Liu. Detection of protein-protein interactions from amino acid
sequences using a rotation forest model with a novel PR-LPQ descriptor, Advanced
Intelligent Computing Theories and Applications, pp. 713-720, 2016.
X. Luo, M. Zhou, S. Li, Z. You, Y.
Xia, Q. Zhu, and H. Leung. An Efficient Second-order Approach to
Factorizing Sparse Matrices in Recommender Systems. IEEE Transactions on
Industrial Informatics, 2015, 11(4): 946-956.
X. Luo, Z. Ming, Z. You, S. Li, Y. Xia, and
H. Leung. Improving Network Topology-based Protein Interactome
Mapping via Collaborative Filtering, Knowledge-Based Systems, 90: 23-32, 2015.
Y. Huang, Z. You, X. Luo, S. Li, L.
Huang. Improved Protein-Protein Interactions
Prediction via Weighted Sparse Representation Model combining Continuous
Wavelet Descriptor and PseAA Composition, International
Journal of Genomics, 2015.
Z. You, H. Guo, X. Luo, Z. Zhao, Inverse-free Extreme Learning Machine with
Optimal Information Updating, IEEE Transactions on Cybernetics, 2015.
Z. Wang, X. Liu, K.
Liu, S. Li, H. Wang, Backstepping-based Lyapunov
Function Construction using Approximate Dynamic Programming and Sum of Square
Techniques, IEEE Transactions on Cybernetics, 2016.
A. Muhammad, S. Li, Dynamic Neural Networks for
Kinematic Redundancy Resolution of Parallel Stewart Platforms, IEEE
Transactions on Cybernetics, 2015.
X. Luo, M. Zhou, S. Li, Z. You, Y. Xia, Q. Zhu and H.
Leung, A Hessian-free Optimization-based Approach to Factorizing
Incomplete Matrices in Recommender Systems, IEEE Transactions on Industrial
X. Luo, Z. You, M.
Zhou, S. Li, H. Leung, Y. Xia, Q.
Zhu, A Highly Efficient Approach to Protein Interactome
Mapping Based on Collaborative Filtering Framework, Scientific reports, Nature,
Z. You, S. Li, X. Gao,
X. Luo, Z. Ji, Large-scale Protein-protein Interactions Detection by
Integrating Big Biosensing Data with Computational
Model, BioMed research international, 2014.
Cheng, S. Yang, S. Li, Deployment
and Tracking in Distributed Sensor Networks, International Journal of
Distributed Sensor Networks, 2014.
C. Pham, A. Jaekel, M. Matin,
A. Amin, Y. Li Editorial: Perception, Reaction, and Cognition in Wireless
Sensor Networks, International Journal of Distributed Sensor Networks, 2013.
R. Kong, and Y. Guo, Cooperative Distributed Source Seeking by Multiple Robots:
Algorithms and experiments, IEEE/ASME Transactions on Mechatronics, 2014
S. Li and Y. Guo, Distributed
Consensus Filtering on Directed Switching Graphs, International Journal of
Robust and Nonlinear Control, 2014.
Y. Lou and B. Liu, Bluetooth Aided Mobile Phone Localization: a Nonlinear Neural
Circuit Approach, ACM Transactions on Embedded Computing Systems, 2014, 13
S. Li and
Y. Li, Nonlinearly Activated Neural Network for Solving Time-varying Complex
Sylvester Equation, IEEE Transactions on Cybernetics, 2013.
B. Liu and Y. Li, Selective Positive-negative Feedback Produces the
Winner-take-all Competition in Recurrent Neural Networks, IEEE Transactions on
Neural Networks and Learning Systems, 2013, 24(2), 301-309
Y. Li, S. Li,
M. Meng, Q. Song, Fast and Robust Data
Association Using Posterior Based Approximate Joint Compatibility Test, IEEE
Transaction on Industrial Informatics, 2013.
Y. Li and Z. Wang, A Class of Finite-time Dual Neural Networks for Solving
Quadratic Programming Problems and Its k-winners-take-all Application, Neural
Networks, 2013, 39: 27-39
S. Li and
Y. Guo, Dynamic Consensus Estimation of Weighted Average on Directed Graphs,
International Journal of Systems Science, 2013.
F. Qin, A Dynamic Neural Network Approach for Solving Nonlinear Inequalities Defined
on A Graph and Its Application to Distributed, Routing-free, Range-free
Localization of WSNs, Neurocomputing, 2013, 117:
Y. Guo, J. Fang, and H. Li, Average consensus with weighting matrix design for
quantized communication on directed switching graphs, International Journal of
Adaptive Control and Signal Processing, 2013, 27: 519-540.
Y. Li, B. Liu, et al, Model-free Control of Lorenz Chaos Using an Approximate
Optimal Control Strategy, Communications in Nonlinear Science and Numerical
Simulation, 2012, 17: 4891-4900.
Y. Wang, J. Yu, et al. A Nonlinear Model to Generate the Winner-take-all
Competition, Communications in Nonlinear Science and Numerical Simulation,
2013, 18: 435–442.
H. Cui, Y. Li, et al. Decentralized Control of Collaborative Redundant
Manipulators with Partial Command Coverage via Locally Connected Recurrent
Neural Networks, Neural Computing and Applications. 2013, 23:1051-1060
Z. Wang, Y. Li, Using Laplacian Eigenmap as Heuristic
Information to Solve Nonlinear Constraints Deﬁned on A Graph and Its
Application in Distributed Range-free Localization of Wireless Sensor Networks,
Neural Processing Letters, 2013, 37:411–424.
S. Chen and B. Liu, Accelerating a Recurrent Neural Network to finite-time
Convergence for Solving Time-varying Sylvester Equation by Using a
Sign-Bi-Power Activation Function, Neural Processing Letters, 2013, 37:189–205
J. Yu, M. Pan, et al, Winner-take-all based on discrete-time dynamic feedback,
Applied mathematics and Computation, 2012, 219: 1569-1575
S. Chen, B. Liu, et al, Decentralized kinematic control of a class of
collaborative redundant manipulators via recurrent neural networks, Neurocomputing, 2012, 91:1-10
B. Liu, B. Chen, et al. Neural Network Based Mobile Phone Localization Using
Bluetooth Connectivity, Neural Computing and Applications, 2013, 23: 667-675.
Y. Li, S. Li, A Biologically Inspired Solution to Simultaneous
Localization and Consistent Mapping in Dynamic Environments, Neurocomputing, 2013, 104:170-179.
S. Chen, S. Li,
B. Liu, et al. Self-learning Variable Structure Control for a Class of
Sensor-actuator Systems. Sensors. 2012,
L. Sun, M. Meng, S.
Li, et al: A Novel CPG with Proprioception and its Application on the Locomotion
Control of Quadruped Robot. International Journal on Information Acquisition,
2009, 6: 33-46.
Steinhorst, Z. Shao, S. Chakraborty, M. Kauer, S. Li, M.
Lukasiewycz, S. Narayanaswamy,
M. Rafique, Q. Wang:
Distributed reconfigurable Battery System Management Architectures, 22nd Asia
and South Pacific Design Automation Conference, ASP-DAC 429-434, 2016.
He, S. Li, Z. Shen, M. Khan, Z. Shao and Q. Wang, WiP
Abstract: A Quadcopter Swarm for Active Monitoring of Smog Propagation,
International Conference on Cyber-Physical Systems, 2015.
Huang, Z. You, S. Li, Z. Zhu, Using
Chou’s amphiphilic Pseudo-Amino Acid Composition and Extreme Learning Machine
for prediction of Protein-protein interactions. International
Joint Conference on Neural Networks (IJCNN), 2952-2956, 2014.
Y. Guo, and B. Bingham, Multi-Robot Cooperative Control for Monitoring and Tracking
Dynamic Plumes, IEEE International Conference on Robotics and Automation
(ICRA), Hong Kong, June 2014.
and Y. Guo, Discrete-time Consensus Filters on Directed Switching Graphs, IEEE
International Conference on Control and Automation (ICCA), Taiwan, June 18-20,
Y. Guo and Y. Braiman, Synchronizing Coupled
Semiconductor Lasers under General Coupling Topologies, American Control
Conference (ACC), Washington DC, June 17 - July 19, 2013
S. Li and
Y. Guo, Distributed Consensus Filter on Directed Graphs with Switching
Topologies, American Control Conference (ACC), Washington DC, June 17 - July
S. Li and
Y. Guo, Distributed Source Seeking by Cooperative
Robots: All-to-All and Limited Communications, IEEE International Conference on
Robotics and Automation (ICRA), Minneapolis, USA, May 14-18, 2012
Y. Guo, J. Fang and H. Li, Robust H_infinity
Consensus on Directed Networks with Quantized Communication, The Seventh IEEE
Sensor Array and Multichannel Signal Processing Workshop (SAM), Hoboken, NJ,
June 17-20, 2012
Y. Lu, S. Li and
Y. Guo, Synchronization of Coupled Laser Arrays with All-to-all and Limited
Coupling Topology, ASME Dynamic Systems and Control Conference, FL, Oct. 17-19,
S. Chen, Y. Lou, et al. A Recurrent Neural Network for
Inter-localization of Mobile Phones. International Joint Conference on
Neural Networks (IJCNN), Brisbane, Austrilia, June,
S. Li and
Y. Guo, Neural-Network Based AUV Path Planning in Estuary Environments, The
10th World Congress on Intelligent Control and Automation(WCICA2012), Beijing,
S. Li, M.Q.H.
Meng, W. Chen, et al. SP-NN: A Novel Neural Network
Approach for Path Planning, International Conference on Robotics and Biomimetics (ROBIO). Sanya,
Z. Zhang, Y. Ma. A Sensor Networks Based Method for Detecting Mine Methane,
International Conference on Information Acquisition (ICIA), Jeju
City, Korea, 2007:403-407
M.Q. H. Meng, H. Liang, et al. A Localization Error
Estimation Method Based on Maximum Likelihood for Wireless Sensor Networks,
IEEE International Conference on Mechatronics and Automation(ICMA), Harbin,
S. Li, Y. Wang and C. Long, Securing the Control of
Euler–Lagrange Systems in networked environments with Model-Free Sliding Mode
Control, in the book Security for multi-hop wireless networks, Publisher: CRC
S. Li and
Y. Li, Chapter 12: Distributed Range-free Localization of Wireless Sensor Networks
via Nonlinear Dynamics, in the book wireless sensor networks, edited by M. Matin, Publisher: InTech, 2012.
S. Zhang, S.
Li and Y. Guo, Chapter 7: Cooperative Control Design for Nanorobots in Drug Delivery. In Book: Selected Topics in
Micro/nano-robotics for Biomedical Applications,
edited by Y. Guo, Springer, New York, 2012.
Y. Li, S. Li,
Y. Sun, et al, A Method and Means to Generate 3D Point Clouds from Coloured
Lidar, Chinese Patent CN103308925A, May, 2013.
H. Liang, S.
Li, W. Chen, et al, A Mapping System and Its Corresponding Method for
Mobile Robots Navigation, Chinese Patent 200710019784.2,
16, Feb. 2007
COMP436: Distributed Objects and
Middleware (Fall 2014, Fall 2015, Fall 2016)
Software Engineering (Spring 2015, Spring 2016)
Introduction to Human Computer Interaction (Spring 2016)
Distributed Winner-Take-All for Competitive Control of Networked
Systems, Sponsor: Hong Kong
Research Grants Council (RGC) Early Career Scheme (ECS), PI. Project No. 25214015, Duration: 2015-2018.
Limited Communication Constrained Multi-robot Cooperation based on
Distributed Dynamic Neural Networks, Sponsor: National
Science Foundation of China (NSFC), PI. Project No. 61401385, Duration: 2015-2018.
Software Defined Battery: A Novel Architecture for Large Scale
Battery. Sponsor: Hong Kong Polytechnic University
Departmental General Research Fund (DGRF), PI. Project
No. G-UA7L, Duration: 2015-2018.
for the following journals and conferences:
Transactions on Signal Processing (TSP)
Transactions on Neural Networks and Learning Systems (TNNLS)
Transactions on Mechatronics (TMECH)
of the Institute of Measurement and Control (TIMC)
Processing Letter (SPL)
Processing Letters (NEPL)
Journal of Communication Systems (IJCS)
Journal of Computer and Information Technology (IJCIT)
of Communication and Computer (JCC)
Conference on Robotics and Automation (ICRA)
International Conference on Intelligent Robots and Systems (IROS)
Control Conference (ACC)
Control Conference (ECC)
Conference on Control, Automation, Robotics and Vision (ICARCV)