Skip to main content

Research Repository

Advanced Search

A teleoperation framework for mobile robots based on shared control (2019)
Journal Article
Luo, J., Lin, Z., Li, Y., & Yang, C. (2020). A teleoperation framework for mobile robots based on shared control. IEEE Robotics and Automation Letters, 5(2), 377-384. https://doi.org/10.1109/lra.2019.2959442

Mobile robots can complete a task in cooperation with a human partner. In this letter, a hybrid shared control method for a mobile robot with omnidirectional wheels is proposed. A human partner utilizes a six degrees of freedom haptic device and elec... Read More about A teleoperation framework for mobile robots based on shared control.

Robotic grasp detection based on image processing and random forest (2019)
Journal Article
Zhang, J., Li, M., Feng, Y., & Yang, C. (2020). Robotic grasp detection based on image processing and random forest. Multimedia Tools and Applications, 79, 7427-7446. https://doi.org/10.1007/s11042-019-08302-9

© 2019, The Author(s). Real-time grasp detection plays a key role in manipulation, and it is also a complex task, especially for detecting how to grasp novel objects. This paper proposes a very quick and accurate approach to detect robotic grasps. Th... Read More about Robotic grasp detection based on image processing and random forest.

Physical human-robot collaboration: Robotic systems, learning methods, collaborative strategies, sensors, and actuators (2019)
Journal Article
Ogenyi, U. E., Liu, J., Yang, C., Ju, Z., & Liu, H. (2021). Physical human-robot collaboration: Robotic systems, learning methods, collaborative strategies, sensors, and actuators. IEEE Transactions on Cybernetics, 51(4), 1888 - 1901. https://doi.org/10.1109/tcyb.2019.2947532

This article presents a state-of-the-art survey on the robotic systems, sensors, actuators, and collaborative strategies for physical human-robot collaboration (pHRC). This article starts with an overview of some robotic systems with cutting-edge tec... Read More about Physical human-robot collaboration: Robotic systems, learning methods, collaborative strategies, sensors, and actuators.

Bayesian estimation of human impedance and motion intention for human-robot collaboration (2019)
Journal Article
Yu, X., He, W., Li, Y., Xue, C., Li, J., Zou, J., & Yang, C. (2021). Bayesian estimation of human impedance and motion intention for human-robot collaboration. IEEE Transactions on Cybernetics, 51(4), 1822 - 1834. https://doi.org/10.1109/tcyb.2019.2940276

This article proposes a Bayesian method to acquire the estimation of human impedance and motion intention in a human-robot collaborative task. Combining with the prior knowledge of human stiffness, estimated stiffness obeying Gaussian distribution is... Read More about Bayesian estimation of human impedance and motion intention for human-robot collaboration.

A robot learning framework based on adaptive admittance control and generalizable motion modeling with neural network controller (2019)
Journal Article
Wang, N., Chen, C., & Yang, C. (2020). A robot learning framework based on adaptive admittance control and generalizable motion modeling with neural network controller. Neurocomputing, 390, 260-267. https://doi.org/10.1016/j.neucom.2019.04.100

© 2019 Elsevier B.V. Robot learning from demonstration (LfD) enables robots to be fast programmed. This paper presents a novel LfD framework involving a teaching phase, a learning phase and a reproduction phase, and proposes methods in each of these... Read More about A robot learning framework based on adaptive admittance control and generalizable motion modeling with neural network controller.

A fast and robust deep convolutional neural networks for complex human activity recognition using smartphone (2019)
Journal Article
Qi, W., Su, H., Yang, C., Ferrigno, G., De Momi, E., & Aliverti, A. (2019). A fast and robust deep convolutional neural networks for complex human activity recognition using smartphone. Sensors, 19(17), https://doi.org/10.3390/s19173731

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. As a significant role in healthcare and sports applications, human activity recognition (HAR) techniques are capable of monitoring humans’ daily behavior. It has spurred the demand for intelli... Read More about A fast and robust deep convolutional neural networks for complex human activity recognition using smartphone.

Neural network enhanced robot tool identification and calibration for bilateral teleoperation (2019)
Journal Article
Su, H., Yang, C., Mdeihly, H., Rizzo, A., Ferrigno, G., & De Momi, E. (2019). Neural network enhanced robot tool identification and calibration for bilateral teleoperation. IEEE Access, 7, 122041-122051. https://doi.org/10.1109/ACCESS.2019.2936334

© 2013 IEEE. In teleoperated surgery, the transmission of force feedback from the remote environment to the surgeon at the local site requires the availability of reliable force information in the system. In general, a force sensor is mounted between... Read More about Neural network enhanced robot tool identification and calibration for bilateral teleoperation.

Deep neural network approach in human-like redundancy optimization for anthropomorphic manipulators (2019)
Journal Article
Su, H., Qi, W., Yang, C., Aliverti, A., Ferrigno, G., & De Momi, E. (2019). Deep neural network approach in human-like redundancy optimization for anthropomorphic manipulators. IEEE Access, 7, 124207-124216. https://doi.org/10.1109/ACCESS.2019.2937380

© 2013 IEEE. Human-like behavior has emerged in the robotics area for improving the quality of Human-Robot Interaction (HRI). For the human-like behavior imitation, the kinematic mapping between a human arm and robot manipulator is one of the popular... Read More about Deep neural network approach in human-like redundancy optimization for anthropomorphic manipulators.

MPC-based 3-D trajectory tracking for an autonomous underwater vehicle with constraints in complex ocean environments (2019)
Journal Article
Zhang, Y., Liu, X., Luo, M., & Yang, C. (2019). MPC-based 3-D trajectory tracking for an autonomous underwater vehicle with constraints in complex ocean environments. Ocean Engineering, 189, https://doi.org/10.1016/j.oceaneng.2019.106309

© 2019 Elsevier Ltd This paper presents a novel three-dimension (3-D) underwater trajectory tracking method for an autonomous underwater vehicle (AUV) using model predictive control (MPC). First, the 6-degrees of freedom (DoF) model of a fully-actuat... Read More about MPC-based 3-D trajectory tracking for an autonomous underwater vehicle with constraints in complex ocean environments.

Unknown dynamics estimator-based output-feedback control for nonlinear pure-feedback systems (2019)
Journal Article
Na, J., Yang, J., Wang, S., Gao, G., & Yang, C. (2021). Unknown dynamics estimator-based output-feedback control for nonlinear pure-feedback systems. IEEE Transactions on Systems Man and Cybernetics: Systems, 51(6), 3832-3843. https://doi.org/10.1109/tsmc.2019.2931627

Most existing adaptive control designs for nonlinear pure-feedback systems have been derived based on backstepping or dynamic surface control (DSC) methods, requiring full system states to be measurable. The neural networks (NNs) or fuzzy logic syste... Read More about Unknown dynamics estimator-based output-feedback control for nonlinear pure-feedback systems.

Disturbance observer-based neural network control of cooperative multiple manipulators with input saturation (2019)
Journal Article
He, W., Sun, Y., Yan, Z., Yang, C., Li, Z., & Kaynak, O. (2020). Disturbance observer-based neural network control of cooperative multiple manipulators with input saturation. IEEE Transactions on Neural Networks and Learning Systems, 31(5), 1735-1746. https://doi.org/10.1109/tnnls.2019.2923241

In this paper, the complex problems of internal forces and position control are studied simultaneously and a disturbance observer-based radial basis function neural network (RBFNN) control scheme is proposed to: 1) estimate the unknown parameters acc... Read More about Disturbance observer-based neural network control of cooperative multiple manipulators with input saturation.

Neural adaptive global stability control for robot manipulators with time-varying output constraints (2019)
Journal Article
Fan, Y., Kang, T., Wang, W., & Yang, C. (2019). Neural adaptive global stability control for robot manipulators with time-varying output constraints. International Journal of Robust and Nonlinear Control, 29(16), 5765-5780. https://doi.org/10.1002/rnc.4690

© 2019 John Wiley & Sons, Ltd. In this paper, a novel adaptive control scheme is proposed based on radial basis function neural network (RBFNN). The considered system is deduced by the structure of RBFNN with nonzero time-varying parameter that ins... Read More about Neural adaptive global stability control for robot manipulators with time-varying output constraints.

Uncertainty and disturbance estimator-based control of a flapping-wing aerial vehicle withwith unknown backlash-like hysteresis (2019)
Journal Article
Yin, Z., He, W., Kaynak, O., Yang, C., Cheng, L., & Wang, Y. (2020). Uncertainty and disturbance estimator-based control of a flapping-wing aerial vehicle withwith unknown backlash-like hysteresis. IEEE Transactions on Industrial Electronics, 67(6), 4826-4835. https://doi.org/10.1109/tie.2019.2926055

Robust and accurate control of a flapping-wing aerial vehicle (FWAV) system is a challenging problem due to the existence of backlash-like hysteresis nonlinearity. This paper proposes uncertainty and disturbance estimator (UDE)-based control with out... Read More about Uncertainty and disturbance estimator-based control of a flapping-wing aerial vehicle withwith unknown backlash-like hysteresis.

Force Sensorless Admittance Control for Teleoperation of Uncertain Robot Manipulator Using Neural Networks (2019)
Journal Article
Yang, C., Peng, G., Cheng, L., Na, J., & Li, Z. (2019). Force Sensorless Admittance Control for Teleoperation of Uncertain Robot Manipulator Using Neural Networks. IEEE Transactions on Systems Man and Cybernetics: Systems, 51(5), 3282-3292. https://doi.org/10.1109/tsmc.2019.2920870

In this paper, a force sensorless control scheme based on neural networks (NNs) is developed for interaction between robot manipulators and human arms in physical collision. In this scheme, the trajectory is generated by using geometry vector method... Read More about Force Sensorless Admittance Control for Teleoperation of Uncertain Robot Manipulator Using Neural Networks.

New noise-tolerant neural algorithms for future dynamic nonlinear optimization with estimation on hessian matrix inversion (2019)
Journal Article
Wei, L., Jin, L., Yang, C., Chen, K., & Li, W. (2021). New noise-tolerant neural algorithms for future dynamic nonlinear optimization with estimation on hessian matrix inversion. IEEE Transactions on Systems Man and Cybernetics: Systems, 51(4), 2611 - 2623. https://doi.org/10.1109/TSMC.2019.2916892

Nonlinear optimization problems with dynamical parameters are widely arising in many practical scientific and engineering applications, and various computational models are presented for solving them under the hypothesis of short-time invariance. To... Read More about New noise-tolerant neural algorithms for future dynamic nonlinear optimization with estimation on hessian matrix inversion.

A robot learning method with physiological interface for teleoperation systems (2019)
Journal Article
Luo, J., Yang, C., Su, H., & Liu, C. (2019). A robot learning method with physiological interface for teleoperation systems. Applied Sciences, 9(10), https://doi.org/10.3390/app9102099

The human operator largely relies on the perception of remote environmental conditions to make timely and correct decisions in a prescribed task when the robot is teleoperated in a remote place. However, due to the unknown and dynamic working environ... Read More about A robot learning method with physiological interface for teleoperation systems.

Composite learning adaptive backstepping control using neural networks with compact supports (2019)
Journal Article
Pan, Y., Yang, C., Pratama, M., & Yu, H. (2019). Composite learning adaptive backstepping control using neural networks with compact supports. International Journal of Adaptive Control and Signal Processing, 33(12), 1726-1738. https://doi.org/10.1002/acs.3002

© 2019 John Wiley & Sons, Ltd. The ability to learn is crucial for neural network (NN) control as it is able to enhance the overall stability and robustness of control systems. In this study, a composite learning control strategy is proposed for a... Read More about Composite learning adaptive backstepping control using neural networks with compact supports.

Adaptive neural network control of underactuated surface vessels with guaranteed transient performance: Theory and experimental results (2019)
Journal Article
Chen, L., Cui, R., Yang, C., & Yan, W. (2020). Adaptive neural network control of underactuated surface vessels with guaranteed transient performance: Theory and experimental results. IEEE Transactions on Industrial Electronics, 67(5), 4024-4035. https://doi.org/10.1109/TIE.2019.2914631

In this paper, an adaptive trajectory tracking control algorithm for underactuated unmanned surface vessels (USVs) with guaranteed transient performance is proposed. To meet the realistic dynamical model of USVs, we consider that the mass and dam... Read More about Adaptive neural network control of underactuated surface vessels with guaranteed transient performance: Theory and experimental results.

Force Sensorless Admittance Control with Neural Learning for Robots with Actuator Saturation (2019)
Journal Article
Peng, G., Yang, C., He, W., & Chen, C. L. (2020). Force Sensorless Admittance Control with Neural Learning for Robots with Actuator Saturation. IEEE Transactions on Industrial Electronics, 67(4), 3138-3148. https://doi.org/10.1109/TIE.2019.2912781

© 1982-2012 IEEE. In this paper, we present a sensorless admittance control scheme for robotic manipulators to interact with unknown environments in the presence of actuator saturation. The external environment is defined as linear models with unknow... Read More about Force Sensorless Admittance Control with Neural Learning for Robots with Actuator Saturation.