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Neural-learning-based force sensorless admittance control for robots with input deadzone (2020)
Journal Article
Peng, G., Chen, C. L. P., He, W., & Yang, C. (in press). Neural-learning-based force sensorless admittance control for robots with input deadzone. IEEE Transactions on Industrial Electronics, 1-1. https://doi.org/10.1109/tie.2020.2991929

This paper presents a neural networks based admittance control scheme for robotic manipulators when interacting with the unknown environment in the presence of the actuator deadzone without needing force sensing. A compliant behaviour of robotic mani... Read More about Neural-learning-based force sensorless admittance control for robots with input deadzone.

Adaptive dynamic programming-based controller with admittance adaptation for robot–environment interaction (2020)
Journal Article
Zhan, H., Huang, D., Chen, Z., Wang, M., & Yang, C. (2020). Adaptive dynamic programming-based controller with admittance adaptation for robot–environment interaction. International Journal of Advanced Robotic Systems, 17(3), 172988142092461. https://doi.org/10.1177/1729881420924610

The problem of optimal tracking control for robot–environment interaction is studied in this article. The environment is regarded as a linear system and an admittance control with iterative linear quadratic regulator method is obtained to guarantee t... Read More about Adaptive dynamic programming-based controller with admittance adaptation for robot–environment interaction.

Multi-hierarchy interaction control of a redundant robot using impedance learning (2020)
Journal Article
Jiang, Y., Yang, C., Wang, Y., Ju, Z., Li, Y., & Su, C. (2020). Multi-hierarchy interaction control of a redundant robot using impedance learning. Mechatronics, 67, https://doi.org/10.1016/j.mechatronics.2020.102348

The control of robots with a compliant joint motion is important for reducing collision forces and improving safety during human robot interactions. In this paper, a multi-hierarchy control framework is proposed for the redundant robot to enable the... Read More about Multi-hierarchy interaction control of a redundant robot using impedance learning.

A method of motion recognition based on electromyographic signals (2020)
Journal Article
Luo, J., Liu, C., Feng, Y., & Yang, C. (in press). A method of motion recognition based on electromyographic signals. Advanced Robotics, 1-9. https://doi.org/10.1080/01691864.2020.1750480

In a robot-assisted surgery, a skillful surgeon can perform the operation excellently through flexible wrist motions and rich experience. However, there are little researches about the relationship between the wrist motion and electromyography (EMG)... Read More about A method of motion recognition based on electromyographic signals.

Hand gesture recognition in complex background based on convolutional pose machine and fuzzy gaussian mixture models (2020)
Journal Article
Zhang, T., Lin, H., Ju, Z., & Yang, C. (in press). Hand gesture recognition in complex background based on convolutional pose machine and fuzzy gaussian mixture models. International Journal of Fuzzy Systems, https://doi.org/10.1007/s40815-020-00825-w

© 2020, The Author(s). Hand gesture is one of the most intuitive and natural ways for human to communicate with computers, and it has been widely adopted in many human–computer interaction applications. However, it is still a challenging problem when... Read More about Hand gesture recognition in complex background based on convolutional pose machine and fuzzy gaussian mixture models.

Reinforcement learning control of a flexible two-link manipulator: an experimental investigation (2020)
Journal Article
He, W., Gao, H., Zhou, C., Yang, C., & Li, Z. (2020). Reinforcement learning control of a flexible two-link manipulator: an experimental investigation. IEEE Transactions on Systems Man and Cybernetics: Systems, 1-11. https://doi.org/10.1109/tsmc.2020.2975232

This article discusses the control design and experiment validation of a flexible two-link manipulator (FTLM) system represented by ordinary differential equations (ODEs). A reinforcement learning (RL) control strategy is developed that is based on a... Read More about Reinforcement learning control of a flexible two-link manipulator: an experimental investigation.

Information entropy-based intention prediction of aerial targets under uncertain and incomplete information (2020)
Journal Article
Zhou, T., Chen, M., Wang, Y., He, J., & Yang, C. (2020). Information entropy-based intention prediction of aerial targets under uncertain and incomplete information. Entropy, 22(3), https://doi.org/10.3390/e22030279

© 2020 by authors. To improve the effectiveness of air combat decision-making systems, target intention has been extensively studied. In general, aerial target intention is composed of attack, surveillance, penetration, feint, defense, reconnaissance... Read More about Information entropy-based intention prediction of aerial targets under uncertain and incomplete information.

Mixed reality enhanced user interactive path planning for omnidirectional mobile robot (2020)
Journal Article
Wu, M., Dai, S., & Yang, C. (2020). Mixed reality enhanced user interactive path planning for omnidirectional mobile robot. Applied Sciences, 10(3), https://doi.org/10.3390/app10031135

This paper proposes a novel control system for the path planning of an omnidirectional mobile robot based on mixed reality. Most research on mobile robots is carried out in a completely real environment or a completely virtual environment. However, a... Read More about Mixed reality enhanced user interactive path planning for omnidirectional mobile robot.

A sEMG-based shared control system with no-target obstacle avoidance for omnidirectional mobile robots (2020)
Journal Article
Kong, H., Yang, C., Li, G., & Dai, S. (2020). A sEMG-based shared control system with no-target obstacle avoidance for omnidirectional mobile robots. IEEE Access, 8, 26030-26040. https://doi.org/10.1109/access.2020.2970468

We propose a novel shared control strategy for mobile robots in a human-robot interaction manner based on surface eletromyography (sEMG) signals. For security reasons, an obstacle avoidance scheme is introduced to the shared control system as collisi... Read More about A sEMG-based shared control system with no-target obstacle avoidance for omnidirectional mobile robots.

Neural learning enhanced variable admittance control for human-robot collaboration (2020)
Journal Article
Chen, X., Wang, N., Cheng, H., & Yang, C. (2020). Neural learning enhanced variable admittance control for human-robot collaboration. IEEE Access, 8, 25727-25737. https://doi.org/10.1109/access.2020.2969085

© 2013 IEEE. In this paper, we propose a novel strategy for human-robot impedance mapping to realize an effective execution of human-robot collaboration. The endpoint stiffness of the human arm impedance is estimated according to the configurations o... Read More about Neural learning enhanced variable admittance control for human-robot collaboration.

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.

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.


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