Outputs (124)

Dynamic movement primitives-based human action prediction and shared control for bilateral robot teleoperation (2024)
Journal Article
Lu, Z., Si, W., Wang, N., & Yang, C. (online). Dynamic movement primitives-based human action prediction and shared control for bilateral robot teleoperation. IEEE Transactions on Industrial Electronics, https://doi.org/10.1109/tie.2024.3401185

This article presents a novel shared-control teleoperation framework that integrates imitation learning and bilateral control to achieve system stability based on a new dynamic movement primitives (DMPs) observer. First, a DMPs-based observer is firs... Read More about Dynamic movement primitives-based human action prediction and shared control for bilateral robot teleoperation.

A novel robust imitation learning framework for dual-arm object-moving tasks (2024)
Journal Article
Wang, W., Zeng, C., Lu, Z., & Yang, C. (in press). A novel robust imitation learning framework for dual-arm object-moving tasks. IEEE Transactions on Industrial Electronics, 1-9. https://doi.org/10.1109/tie.2024.3387098

Drawing inspiration from the mechanism of human skill acquisition, imitation learning has demonstrated remarkable performance. Over recent years, modelbased imitation learning combined with machine learning and control theory has been continuously de... Read More about A novel robust imitation learning framework for dual-arm object-moving tasks.

BioTacTip: A soft biomimetic optical tactile sensor for efficient 3D contact localization and 3D force estimation (2024)
Journal Article
Li, H., Nam, S., Lu, Z., Yang, C., Psomopoulou, E., & Lepora, N. F. (2024). BioTacTip: A soft biomimetic optical tactile sensor for efficient 3D contact localization and 3D force estimation. IEEE Robotics and Automation Letters, 9(6), 5314-5321. https://doi.org/10.1109/lra.2024.3387111

In this letter, we introduce a new soft biomimetic optical tactile sensor based on mimicking the interlocking structure of the epidermal-dermal boundary: the BioTacTip. The primary sensing unit comprises a sharp white tip surrounded by four black cov... Read More about BioTacTip: A soft biomimetic optical tactile sensor for efficient 3D contact localization and 3D force estimation.

A dynamic movement primitives-based tool use skill learning and transfer framework for robot manipulation (2024)
Journal Article
Lu, Z., Wang, N., & Yang, C. (online). A dynamic movement primitives-based tool use skill learning and transfer framework for robot manipulation. IEEE Transactions on Automation Science and Engineering, https://doi.org/10.1109/tase.2024.3370139

This paper presents a framework for learning and transferring robot tool-use skills based on Dynamic Movement Primitives (DMPs) for robot fine manipulation. DMPs and their enhanced methods are employed to acquire a specific tool-use skill applicable... Read More about A dynamic movement primitives-based tool use skill learning and transfer framework for robot manipulation.

MechTac: A multifunctional tendon-linked optical tactile sensor for in/out-the-field-of-view perception with deep learning (2023)
Presentation / Conference Contribution
Lu, Z., Yue, T., Zhao, Z., Si, W., Wang, N., & Yang, C. (2023, October). MechTac: A multifunctional tendon-linked optical tactile sensor for in/out-the-field-of-view perception with deep learning. Presented at IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society, Singapore

Tactile sensors can be used for motion detection and object perception in robot manipulation. The contact detection within the camera's visual inspection area has been well-developed, but perception outside the field of view of the camera is overlook... Read More about MechTac: A multifunctional tendon-linked optical tactile sensor for in/out-the-field-of-view perception with deep learning.

VERGNet: Visual enhancement guided robotic grasp detection under low-light condition (2023)
Journal Article
Niu, M., Lu, Z., Chen, L., Yang, J., & Yang, C. (2023). VERGNet: Visual enhancement guided robotic grasp detection under low-light condition. IEEE Robotics and Automation Letters, 8(12), 8541-8548. https://doi.org/10.1109/lra.2023.3330664

Although existing grasp detection methods have achieved encouraging performance under well-light conditions, repetitive experiments have found that the detection performance would deteriorate drastically under low-light conditions. Although supplemen... Read More about VERGNet: Visual enhancement guided robotic grasp detection under low-light condition.

Learning a flexible neural energy function with a unique minimum for globally stable and accurate demonstration learning (2023)
Journal Article
Jin, Z., Si, W., Liu, A., Zhang, W. A., Yu, L., & Yang, C. (in press). Learning a flexible neural energy function with a unique minimum for globally stable and accurate demonstration learning. IEEE Transactions on Robotics, https://doi.org/10.1109/tro.2023.3303011

Learning a stable autonomous dynamic system (ADS) encoding human motion rules has been shown as an effective way for demonstration learning. However, the stability guarantee may sacrifice the demonstration learning accuracy. This article solves the i... Read More about Learning a flexible neural energy function with a unique minimum for globally stable and accurate demonstration learning.

Distributed observer-based prescribed performance control for multi-robot deformable object cooperative teleoperation (2023)
Journal Article
Lu, Z., Wang, N., Si, W., & Yang, C. (in press). Distributed observer-based prescribed performance control for multi-robot deformable object cooperative teleoperation. IEEE Transactions on Automation Science and Engineering, https://doi.org/10.1109/tase.2023.3292553

In this paper, a distributed observer-based prescribed performance control method is proposed for using a multi-robot teleoperation system to manipulate a common deformable object. To achieve a stable position-tracking effect and realize the desired... Read More about Distributed observer-based prescribed performance control for multi-robot deformable object cooperative teleoperation.

Visual-tactile robot grasping based on human skill learning from demonstrations using a wearable parallel hand exoskeleton (2023)
Journal Article
Lu, Z., Chen, L., Dai, H., Li, H., Zhao, Z., Zheng, B., Lepora, N., & Yang, C. (2023). Visual-tactile robot grasping based on human skill learning from demonstrations using a wearable parallel hand exoskeleton. IEEE Robotics and Automation Letters, 8(9), 5384-5391. https://doi.org/10.1109/lra.2023.3295296

The soft fingers and strategic grasping skills enable the human hands to grasp objects in a stable manner. This letter is to model human grasping skills and transfer the learned skills to robots to improve grasping quality and success rate. First, we... Read More about Visual-tactile robot grasping based on human skill learning from demonstrations using a wearable parallel hand exoskeleton.

Two-stage grasp detection method for robotics using point clouds and deep hierarchical feature learning network (2023)
Journal Article
Liu, X., Huang, C., Li, J., Wan, W., & Yang, C. (2024). Two-stage grasp detection method for robotics using point clouds and deep hierarchical feature learning network. IEEE Transactions on Cognitive and Developmental Systems, 16(2), 720 - 731. https://doi.org/10.1109/tcds.2023.3289987

When human beings see different objects, they can quickly make correct grasping strategies through brain decisions. However, grasp, as the first step of most manipulation tasks, is still an open issue in robotics. Although many detection methods have... Read More about Two-stage grasp detection method for robotics using point clouds and deep hierarchical feature learning network.

Impedance learning for human-guided robots in contact with unknown environments (2023)
Journal Article
Xing, X., Burdet, E., Si, W., Yang, C., & Li, Y. (2023). Impedance learning for human-guided robots in contact with unknown environments. IEEE Transactions on Robotics, 39(5), 3705 - 3721. https://doi.org/10.1109/tro.2023.3281483

Previous works have developed impedance control to increase safety and improve performance in contact tasks, where the robot is in physical interaction with either an environment or a human user. This article investigates impedance learning for a rob... Read More about Impedance learning for human-guided robots in contact with unknown environments.

A novel human-robot skill transfer method for contact-rich manipulation task (2023)
Journal Article
Dong, J., Si, W., & Yang, C. (2023). A novel human-robot skill transfer method for contact-rich manipulation task. Robotic Intelligence and Automation, 43(3), https://doi.org/10.1108/RIA-01-2023-0002

Purpose: The purpose of this paper is to enhance the robot’s ability to complete multi-step contact tasks in unknown or dynamic environments, as well as the generalization ability of the same task in different environments. Design/methodology/approac... Read More about A novel human-robot skill transfer method for contact-rich manipulation task.

Dynamic motion primitives-based trajectory learning for physical human-robot interaction force control (2023)
Journal Article
Xing, X., Maqsood, K., Zeng, C., Yang, C., Yuan, S., & Li, Y. (2024). Dynamic motion primitives-based trajectory learning for physical human-robot interaction force control. IEEE Transactions on Industrial Informatics, 20(2), 1675 - 1686. https://doi.org/10.1109/TII.2023.3280320

One promising function of interactive robots is to provide a specific interaction force to human users. For example, rehabilitation robots are expected to promote patients' recovery by interacting with them with a prescribed force. However, motion un... Read More about Dynamic motion primitives-based trajectory learning for physical human-robot interaction force control.

A human-robot collaboration method for uncertain surface scanning (2023)
Journal Article
Zhao, G., Zeng, C., Si, W., & Yang, C. (in press). A human-robot collaboration method for uncertain surface scanning. CAAI Transactions on Intelligence Technology, https://doi.org/10.1049/cit2.12227

Robots are increasingly expected to replace humans in many repetitive and high-precision tasks, of which surface scanning is a typical example. However, it is usually difficult for a robot to independently deal with a surface scanning task with uncer... Read More about A human-robot collaboration method for uncertain surface scanning.

Recent advancements in multimodal human–robot interaction (2023)
Journal Article
Su, H., Qi, W., Chen, J., Yang, C., Sandoval, J., & Laribi, M. A. (2023). Recent advancements in multimodal human–robot interaction. Frontiers in Neurorobotics, 17, Article 1084000. https://doi.org/10.3389/fnbot.2023.1084000

Robotics have advanced significantly over the years, and human–robot interaction (HRI) is now playing an important role in delivering the best user experience, cutting down on laborious tasks, and raising public acceptance of robots. New HRI approach... Read More about Recent advancements in multimodal human–robot interaction.

Design and control of a novel bionic mantis shrimp robot (2023)
Journal Article
Chen, G., Xu, Y., Yang, C., Yang, X., Hu, H., Chai, X., & Wang, D. (2023). Design and control of a novel bionic mantis shrimp robot. IEEE/ASME Transactions on Mechatronics, 28(6), 3376 - 3385. https://doi.org/10.1109/TMECH.2023.3266778

This article presents the development of a novel bionic robot, which is inspired by agile and fast mantis shrimp in the ocean. The developed bionic mantis shrimp robot has ten rigid-flexible swimming feet (pleopods) for swimming propulsion and a rope... Read More about Design and control of a novel bionic mantis shrimp robot.

A constrained framework based on IBLF for robot learning with human supervision (2023)
Journal Article
Shi, D., Li, Q., Yang, C., & Lu, Z. (2023). A constrained framework based on IBLF for robot learning with human supervision. Robotica, 41(8), 2451-2463. https://doi.org/10.1017/S0263574723000462

Dynamical movement primitives (DMPs) method is a useful tool for efficient robotic skills learning from human demonstrations. However, the DMPs method should know the specified constraints of tasks in advance. One flexible solution is to introduce th... Read More about A constrained framework based on IBLF for robot learning with human supervision.

Distributed collaborative control of redundant robots under weight-unbalanced directed graphs (2023)
Journal Article
Zheng, X., Liu, M., Jin, L., & Yang, C. (2024). Distributed collaborative control of redundant robots under weight-unbalanced directed graphs. IEEE Transactions on Industrial Informatics, 20(1), 681 - 690. https://doi.org/10.1109/TII.2023.3268778

In consideration of the limitation of the communication and the possibility that redundant robots might deliver information at different power levels, cases under weight-unbalanced directed graphs from the network topology perspective are in larger a... Read More about Distributed collaborative control of redundant robots under weight-unbalanced directed graphs.

Handheld device design for robotic teleoperation based on multi-sensor fusion (2023)
Presentation / Conference Contribution
Xie, L., Huang, D., Lu, Z., Wang, N., & Yang, C. (2023, March). Handheld device design for robotic teleoperation based on multi-sensor fusion. Presented at Proceedings - 2023 IEEE International Conference on Mechatronics, ICM 2023, Loughborough, United Kingdom

Precise leader-follower control is critical for teleop- eration. This paper designs and implements a low-cost leader device for unilateral teleoperation scenario. Monocular vision based on fiducial markers and MEMS Inertial Measurement Unit (IMU) are... Read More about Handheld device design for robotic teleoperation based on multi-sensor fusion.

A multimodal teleoperation interface for human-robot collaboration (2023)
Presentation / Conference Contribution
Si, W., Zhong, T., Wang, N., & Yang, C. (2023, March). A multimodal teleoperation interface for human-robot collaboration. Presented at Proceedings - 2023 IEEE International Conference on Mechatronics, ICM 2023, Loughborough, United Kingdom

Human-robot collaboration provides an effective approach to combine human intelligence and the autonomy of robots, which can improve the safety and efficiency of the robot. However, developing an intuitive and immersive human-robot interface with mul... Read More about A multimodal teleoperation interface for human-robot collaboration.

The classification and new trends of shared control strategies in telerobotic systems: A survey (2023)
Journal Article
Li, G., Li, Q., Yang, C., Su, Y., Yuan, Z., & Wu, X. (2023). The classification and new trends of shared control strategies in telerobotic systems: A survey. IEEE Transactions on Haptics, 16(2), 118-133. https://doi.org/10.1109/TOH.2023.3253856

Shared control, which permits a human operator and an autonomous controller to share the control of a telerobotic system, can reduce the operator's workload and/or improve performances during the execution of tasks. Due to the great benefits of combi... Read More about The classification and new trends of shared control strategies in telerobotic systems: A survey.

Adaptive fuzzy prescribed-time connectivity-preserving consensus of stochastic nonstrict-feedback switched multiagent systems (2023)
Journal Article
Yi, J., Li, J., & Yang, C. (2023). Adaptive fuzzy prescribed-time connectivity-preserving consensus of stochastic nonstrict-feedback switched multiagent systems. IEEE Transactions on Fuzzy Systems, 31(10), 3346-3357. https://doi.org/10.1109/tfuzz.2023.3252601

An adaptive fuzzy prescribed-time connectivity-preserving consensus protocol is designed for a class of stochastic nonstrict-feedback multiagent systems, in which periodic disturbances, switched nonlinearities, input saturation, and limited communica... Read More about Adaptive fuzzy prescribed-time connectivity-preserving consensus of stochastic nonstrict-feedback switched multiagent systems.

Editorial: Neuromorphic engineering for robotics (2023)
Journal Article
Bing, Z., Yang, C., & Knoll, A. (2023). Editorial: Neuromorphic engineering for robotics. Frontiers in Neurorobotics, 17, Article 1158988. https://doi.org/10.3389/fnbot.2023.1158988

Neuromorphic engineering aims to apply insights from neurobiology to develop next-generation artificial intelligence for computation, sensing, and the control of robotic systems. There has been a rapid expansion of neuromorphic engineering technologi... Read More about Editorial: Neuromorphic engineering for robotics.

Editorial: Advanced learning control in physical interaction tasks (2023)
Journal Article
Zeng, C., Guo, J., Li, Q., & Yang, C. (2023). Editorial: Advanced learning control in physical interaction tasks. Frontiers in Robotics and AI, 10, 1166759. https://doi.org/10.3389/frobt.2023.1166759

Robotics are increasingly and urgently expected to acquire human-like dexterous manipulation skills in physical interaction environments. Due to this, the loop between the high-level action policy and the low-level motion execution needs to be closed... Read More about Editorial: Advanced learning control in physical interaction tasks.

Review on human-like robot manipulation using dexterous hands (2023)
Journal Article
Kadalagere Sampath, S., Wang, N., Wu, H., & Yang, C. (2023). Review on human-like robot manipulation using dexterous hands. Cognitive Computation and Systems, 5(1), 14-29. https://doi.org/10.1049/ccs2.12073

In recent years, human hand-based robotic hands or dexterous hands have gained attention due to their enormous capabilities of handling soft materials compared to traditional grippers. Back in the earlier days, the development of a hand model close t... Read More about Review on human-like robot manipulation using dexterous hands.

A comprehensive review of scab disease detection on Rosaceae family fruits via UAV imagery (2023)
Journal Article
Ali, Z. A., Yang, C., Israr, A., & Zhu, Q. (2023). A comprehensive review of scab disease detection on Rosaceae family fruits via UAV imagery. Drones, 7(2), 97. https://doi.org/10.3390/drones7020097

Disease detection in plants is essential for food security and economic stability. Unmanned aerial vehicle (UAV) imagery and artificial intelligence (AI) are valuable tools for it. The purpose of this review is to gather several methods used by our p... Read More about A comprehensive review of scab disease detection on Rosaceae family fruits via UAV imagery.

A gripper-like exoskeleton design for robot grasping demonstration (2023)
Journal Article
Dai, H., Lu, Z., He, M., & Yang, C. (2023). A gripper-like exoskeleton design for robot grasping demonstration. Actuators, 12(1), 39. https://doi.org/10.3390/act12010039

Learning from demonstration (LfD) is a practical method for transferring skill knowledge from a human demonstrator to a robot. Several studies have shown the effectiveness of LfD in robotic grasping tasks to improve the success rate of grasping and t... Read More about A gripper-like exoskeleton design for robot grasping demonstration.

Deformation-aware contact-rich manipulation skills learning and compliant control (2023)
Presentation / Conference Contribution
Si, W., Guo, C., Dong, J., Lu, Z., & Yang, C. (2022, September). Deformation-aware contact-rich manipulation skills learning and compliant control. Presented at 15th International Workshop on Human-Friendly Robotics, The Netherlands

In this paper, we study a vision-based reactive adaptation method for contact-rich manipulation tasks, based on the compliant control and learning from demonstration. For contact-rich tasks, the compliant control method is essential, especially when... Read More about Deformation-aware contact-rich manipulation skills learning and compliant control.

Human-in-the-loop Learning and Control for Robot Teleoperation (2023)
Book
Yang, C., Luo, J., & Wang, N. (2023). Human-in-the-loop Learning and Control for Robot Teleoperation. Elsevier. https://doi.org/10.1016/C2021-0-02620-1

Human-in-the-loop Learning and Control for Robot Teleoperation presents recent, research progress on teleoperation and robots, including human-robot interaction, learning and control for teleoperation with many extensions on intelligent learning tech... Read More about Human-in-the-loop Learning and Control for Robot Teleoperation.

Multi-fingered tactile servoing for grasping adjustment under partial observation (2022)
Presentation / Conference Contribution
Liu, H., Huang, B., Li, Q., Zheng, Y., Ling, Y., Lee, W., Liu, Y., Tsai, Y.-Y., & Yang, C. (2022, October). Multi-fingered tactile servoing for grasping adjustment under partial observation. Presented at 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan

Grasping of objects using multi-fingered robotic hands often fails due to small uncertainties in the hand motion control and the object's pose estimation. To tackle this problem, we propose a grasping adjustment strategy based on tactile seroving. Ou... Read More about Multi-fingered tactile servoing for grasping adjustment under partial observation.

One-shot domain-adaptive imitation learning via progressive learning applied to robotic pouring (2022)
Journal Article
Zhang, D., Fan, W., Lloyd, J., Yang, C., & Lepora, N. F. (2024). One-shot domain-adaptive imitation learning via progressive learning applied to robotic pouring. IEEE Transactions on Automation Science and Engineering, 21(1), 541 - 554. https://doi.org/10.1109/TASE.2022.3220728

Traditional deep learning-based visual imitation learning techniques require a large amount of demonstration data for model training, and the pre-trained models are difficult to adapt to new scenarios. To address these limitations, we propose a unifi... Read More about One-shot domain-adaptive imitation learning via progressive learning applied to robotic pouring.

A trajectory and force dual-incremental robot skill learning and generalization framework using improved dynamical movement primitives and adaptive neural network control (2022)
Journal Article
Lu, Z., Wang, N., Li, Q., & Yang, C. (2023). A trajectory and force dual-incremental robot skill learning and generalization framework using improved dynamical movement primitives and adaptive neural network control. Neurocomputing, 521, 146-159. https://doi.org/10.1016/j.neucom.2022.11.076

Due to changes in the environment and errors that occurred during skill initialization, the robot's operational skills should be modified to adapt to new tasks. As such, skills learned by the methods with fixed features, such as the classical Dynamic... Read More about A trajectory and force dual-incremental robot skill learning and generalization framework using improved dynamical movement primitives and adaptive neural network control.

A neural network based framework for variable impedance skills learning from demonstrations (2022)
Journal Article
Zhang, Y., Cheng, L., Cao, R., Li, H., & Yang, C. (2023). A neural network based framework for variable impedance skills learning from demonstrations. Robotics and Autonomous Systems, 160, 104312. https://doi.org/10.1016/j.robot.2022.104312

Robots are becoming standard collaborators not only in factories, hospitals, and offices, but also in people's homes, where they can play an important role in situations where a human cannot complete a task alone or needs the help of another person (... Read More about A neural network based framework for variable impedance skills learning from demonstrations.

A modified LSTM model for Chinese sign language recognition using leap motion (2022)
Presentation / Conference Contribution
Wu, B., Lu, Z., & Yang, C. (2022, October). A modified LSTM model for Chinese sign language recognition using leap motion. Presented at 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Prague, Czech Republic

At present, there are about 70 million deaf people using sign language in the world, but for most normal people, it is difficult to understand the meaning of the sign language expression. Therefore, it is of great importance to explore the ways of re... Read More about A modified LSTM model for Chinese sign language recognition using leap motion.

A novel robot skill learning framework based on bilateral teleoperation (2022)
Presentation / Conference Contribution
Si, W., Yue, T., Guan, Y., Wang, N., & Yang, C. (2022, August). A novel robot skill learning framework based on bilateral teleoperation. Presented at 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE), Mexico City, Mexico

In this paper, a bilateral teleoperation-based robot skill learning framework is developed to transfer multi-step and contact manipulation skills from humans to robots. Robot skill acquisition via bilateral teleoperation provides a solution for human... Read More about A novel robot skill learning framework based on bilateral teleoperation.

Fixed-time adaptive neural control for robot manipulators with input saturation and disturbance (2022)
Presentation / Conference Contribution
Huang, H., Lu, Z., Wang, N., & Yang, C. (2022, September). Fixed-time adaptive neural control for robot manipulators with input saturation and disturbance. Presented at 2022 27th International Conference on Automation and Computing: Smart Systems and Manufacturing, ICAC 2022, Bristol, United Kingdom

A fixed-time adaptive neural network control scheme is designed for an unknown model manipulator system with input saturation and external environment disturbance, so that the system convergence time can be parameterized and not affected by the initi... Read More about Fixed-time adaptive neural control for robot manipulators with input saturation and disturbance.

Novel gripper-like exoskeleton design for robotic grasping based on learning from demonstration (2022)
Presentation / Conference Contribution
Dai, H., Lu, Z., He, M., & Yang, C. (2022, September). Novel gripper-like exoskeleton design for robotic grasping based on learning from demonstration. Presented at 2022 27th International Conference on Automation and Computing: Smart Systems and Manufacturing, ICAC 2022, Bristol, United Kingdom

Learning from demonstration (LfD) has been developed and proved to be a promising method for transferring skill knowledge from human to robot. It is desired to have a demonstration device that can effectively map demonstrations to the robot's motion... Read More about Novel gripper-like exoskeleton design for robotic grasping based on learning from demonstration.

Non-singular fixed-time sliding mode control for unknown-dynamics manipulators interacting with environment (2022)
Presentation / Conference Contribution
Kong, H., Li, G., & Yang, C. (2022, September). Non-singular fixed-time sliding mode control for unknown-dynamics manipulators interacting with environment. Presented at 2022 27th International Conference on Automation and Computing: Smart Systems and Manufacturing, ICAC 2022, Bristol, UK

In this paper, a robust fixed-time controller is de-signed for manipulators with unknown dynamics while interacting with environment. To realize compliance of the manipulator to the environment, an admittance model is adopted in the system. In the co... Read More about Non-singular fixed-time sliding mode control for unknown-dynamics manipulators interacting with environment.

A collaboration scheme for controlling multimanipulator system: A game-theoretic perspective (2022)
Journal Article
Zhang, J., Jin, L., Wang, Y., & Yang, C. (2023). A collaboration scheme for controlling multimanipulator system: A game-theoretic perspective. IEEE/ASME Transactions on Mechatronics, 28(1), 128-139. https://doi.org/10.1109/TMECH.2022.3193136

In some task-oriented multimanipulator applications, the system not only needs to complete the main assigned tasks, but also should optimize some subobjectives. In order to tap the redundancy potential of individual manipulators and improve the perfo... Read More about A collaboration scheme for controlling multimanipulator system: A game-theoretic perspective.

A novel dynamic movement primitives-based skill learning and transfer framework for multi-tool use (2022)
Presentation / Conference Contribution
Lu, Z., Wang, N., Li, M., & Yang, C. (2022). A novel dynamic movement primitives-based skill learning and transfer framework for multi-tool use. . https://doi.org/10.1109/ICCA54724.2022.9831826

Dynamic Movement Primitives (DMPs) is a general method for learning skills from demonstrations. Most previous research on DMP has focused on point to point skill learning and training, and the skills learned are usually generalized based on the same... Read More about A novel dynamic movement primitives-based skill learning and transfer framework for multi-tool use.

Dynamic surface control with a nonlinear disturbance observer for multi‐degree of freedom underactuated mechanical systems (2022)
Journal Article
Ding, F., Huang, J., Xu, W., Yang, C., Sun, C., & Ai, Y. (2022). Dynamic surface control with a nonlinear disturbance observer for multi‐degree of freedom underactuated mechanical systems. International Journal of Robust and Nonlinear Control, 32(14), https://doi.org/10.1002/rnc.6275

Underactuated systems are extensively utilized in practice while attracting a huge deal of attention in theoretical studies. There are few robust control strategies for general underactuated systems because of the variety of their dynamic models. A d... Read More about Dynamic surface control with a nonlinear disturbance observer for multi‐degree of freedom underactuated mechanical systems.

A review on the techniques used in prostate brachytherapy (2022)
Journal Article
Li, Y., Yang, C., Bahl, A., Persad, R., & Melhuish, C. (2022). A review on the techniques used in prostate brachytherapy. Cognitive Computation and Systems, 4(4), 317-328. https://doi.org/10.1049/ccs2.12067

Prostate brachytherapy is a validated treatment for prostate cancer. During the procedure, the accuracy of needle placement is critical to the treatment’s effectiveness. However, the inserted needle could deflect from the preset trajectory because of... Read More about A review on the techniques used in prostate brachytherapy.

From teleoperation to autonomous robot-assisted microsurgery: A survey (2022)
Journal Article
Zhang, D., Si, W., Fan, W., Guan, Y., & Yang, C. (2022). From teleoperation to autonomous robot-assisted microsurgery: A survey. Machine Intelligence Research, 19, 288–306. https://doi.org/10.1007/s11633-022-1332-5

Robot-assisted microsurgery (RAMS) has many benefits compared to traditional microsurgery. Microsurgical platforms with advanced control strategies, high-quality micro-imaging modalities and micro-sensing systems are worth developing to further enhan... Read More about From teleoperation to autonomous robot-assisted microsurgery: A survey.

E2EK: End-to-end regression network based on keypoint for 6d pose estimation (2022)
Journal Article
Lin, S., Wang, Z., Ling, Y., Tao, Y., & Yang, C. (2022). E2EK: End-to-end regression network based on keypoint for 6d pose estimation. IEEE Robotics and Automation Letters, 7(3), 6526-6533. https://doi.org/10.1109/LRA.2022.3174261

The methods based on deep learning are the mainstream of 6D object pose estimation, which mainly include direct regression and two-stage pipelines. The former are keen by many scholars at first due to their simplicity and differentiability to poses,... Read More about E2EK: End-to-end regression network based on keypoint for 6d pose estimation.

Multifingered robot hand compliant manipulation based on vision-based demonstration and adaptive force control (2022)
Journal Article
Zeng, C., Li, S., Chen, Z., Yang, C., Sun, F., & Zhang, J. (in press). Multifingered robot hand compliant manipulation based on vision-based demonstration and adaptive force control. IEEE Transactions on Neural Networks and Learning Systems, 1-12. https://doi.org/10.1109/tnnls.2022.3184258

Multifingered hand dexterous manipulation is quite challenging in the domain of robotics. One remaining issue is how to achieve compliant behaviors. In this work, we propose a human-in-the-loop learning-control approach for acquiring compliant graspi... Read More about Multifingered robot hand compliant manipulation based on vision-based demonstration and adaptive force control.

An observation based method for human robot writing skill transfer (2022)
Presentation / Conference Contribution
Li, X., Si, W., & Yang, C. (2022). An observation based method for human robot writing skill transfer. In 2022 IEEE 17th International Conference on Control & Automation (ICCA). https://doi.org/10.1109/ICCA54724.2022.9831836

This paper proposes a novel method of Chinese character stroke extraction and a framework for human robot skill transfer through vision-based observation. By analyzing the structure of Chinese characters, a direction vector update rule and a pixel fi... Read More about An observation based method for human robot writing skill transfer.

Robotic dexterous manipulation: From tele-operation to autonomous learning and adaptive control (2022)
Journal Article
Li, Q., Liu, C., Yang, C., Chen, F., & Ritter, H. (2022). Robotic dexterous manipulation: From tele-operation to autonomous learning and adaptive control. Complex and Intelligent Systems, 8(4), 2809-2811. https://doi.org/10.1007/s40747-022-00773-y

Manipulation is one essential capability that enables robots to interact with and change the world. It is very important to understand the principles on how dexterous manipulation can be achieved from theoretical and technical aspects. From this spec... Read More about Robotic dexterous manipulation: From tele-operation to autonomous learning and adaptive control.

Robust passivity-based dynamical systems for compliant motion adaptation (2022)
Journal Article
Huang, H., Guo, Y., Yang, G., Chu, J., Chen, X., Li, Z., & Yang, C. (2022). Robust passivity-based dynamical systems for compliant motion adaptation. IEEE/ASME Transactions on Mechatronics, 27(6), 4819-4828. https://doi.org/10.1109/tmech.2022.3166204

Motivated by human compliant behaviors during interacting with unknown environments and how motions and impedance to are adapted skilfully complete a task, this article develops a motion planning scheme that is capable of generating a compliant traje... Read More about Robust passivity-based dynamical systems for compliant motion adaptation.

A framework for composite layup skill learning and generalizing through teleoperation (2022)
Journal Article
Si, W., Wang, N., Li, Q., & Yang, C. (2022). A framework for composite layup skill learning and generalizing through teleoperation. Frontiers in Neurorobotics, 16, Article 840240. https://doi.org/10.3389/fnbot.2022.840240

In this article, an impedance control-based framework for human-robot composite layup skill transfer was developed, and the human-in-the-loop mechanism was investigated to achieve human-robot skill transfer. Although there are some works on human-rob... Read More about A framework for composite layup skill learning and generalizing through teleoperation.

A proactive controller for human-driven robots based on force/motion observer mechanisms (2022)
Journal Article
Li, Y., Yang, L., Huang, D., Yang, C., & Xia, J. (2022). A proactive controller for human-driven robots based on force/motion observer mechanisms. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(10), 6211-6221. https://doi.org/10.1109/tsmc.2022.3143892

This article investigates human-driven robots via physical interaction, which is enhanced by integrating the human partner's motion intention. A human motor control model is employed to estimate the human partner's motion intention. A system observer... Read More about A proactive controller for human-driven robots based on force/motion observer mechanisms.

Tactile Sensing, Skill Learning, and Robotic Dexterous Manipulation (2022)
Book
Li, Q., Luo, S., Chen, Z., Zhang, J., & Yang, C. (2022). Tactile Sensing, Skill Learning, and Robotic Dexterous Manipulation. https://doi.org/10.1016/C2020-0-02663-0

Tactile Sensing, Skill Learning and Robotic Dexterous Manipulation focuses on cross-disciplinary lines of research and groundbreaking research ideas in three research lines: tactile sensing, skill learning and dexterous control. The book introduces r... Read More about Tactile Sensing, Skill Learning, and Robotic Dexterous Manipulation.

Incremental motor skill learning and generalization from human dynamic reactions based on dynamic movement primitives and fuzzy logic system (2021)
Journal Article
Lu, Z., Wang, N., Li, M., & Yang, C. (2022). Incremental motor skill learning and generalization from human dynamic reactions based on dynamic movement primitives and fuzzy logic system. IEEE Transactions on Fuzzy Systems, 30(6), 1506-1515. https://doi.org/10.1109/tfuzz.2021.3136933

Different from previous work on single skill learning from human demonstrations, an incremental motor skill learning, generalization and control method based on dynamic movement primitives (DMP) and broad learning system (BLS) is proposed for extract... Read More about Incremental motor skill learning and generalization from human dynamic reactions based on dynamic movement primitives and fuzzy logic system.

Iterative learning-based robotic controller with prescribed human-robot interaction force (2021)
Journal Article
Xing, X., Maqsood, K., Huang, D., Yang, C., & Li, Y. (2022). Iterative learning-based robotic controller with prescribed human-robot interaction force. IEEE Transactions on Automation Science and Engineering, 19(4), 3395-3408. https://doi.org/10.1109/tase.2021.3119400

In this article, an iterative-learning-based robotic controller is developed, which aims at providing a prescribed assistance or resistance force to the human user. In the proposed controller, the characteristic parameter of the human upper limb move... Read More about Iterative learning-based robotic controller with prescribed human-robot interaction force.

A unified parametric representation for robotic compliant skills with adaptation of impedance and force (2021)
Journal Article
Zeng, C., Li, Y., Guo, J., Huang, Z., Wang, N., & Yang, C. (2022). A unified parametric representation for robotic compliant skills with adaptation of impedance and force. IEEE/ASME Transactions on Mechatronics, 27(2), 623-633. https://doi.org/10.1109/tmech.2021.3109160

Robotic compliant manipulation is a very challenging but urgent research spot in the domain of robotics. One difficulty lies in the lack of a unified representation for encoding and learning of compliant profiles. This article aims to introduce a nov... Read More about A unified parametric representation for robotic compliant skills with adaptation of impedance and force.

Trajectory online adaption based on human motion prediction for teleoperation (2021)
Journal Article
Luo, J., Huang, D., Li, Y., & Yang, C. (2022). Trajectory online adaption based on human motion prediction for teleoperation. IEEE Transactions on Automation Science and Engineering, 19(4), 3184-3191. https://doi.org/10.1109/tase.2021.3111678

In this work, a human motion intention prediction method based on an autoregressive (AR) model for teleoperation is developed. Based on this method, the robot's motion trajectory can be updated in real time through updating the parameters of the AR m... Read More about Trajectory online adaption based on human motion prediction for teleoperation.

An improvement of robot stiffness-adaptive skill primitive generalization using the surface electromyography in human–robot collaboration (2021)
Journal Article
Guan, Y., Wang, N., & Yang, C. (2021). An improvement of robot stiffness-adaptive skill primitive generalization using the surface electromyography in human–robot collaboration. Frontiers in Neuroscience, 15, Article 694914. https://doi.org/10.3389/fnins.2021.694914

Learning from Demonstration in robotics has proved its efficiency in robot skill learning. The generalization goals of most skill expression models in real scenarios are specified by humans or associated with other perceptual data. Our proposed frame... Read More about An improvement of robot stiffness-adaptive skill primitive generalization using the surface electromyography in human–robot collaboration.

Adaptive neural control of a class of uncertain state and input-delayed systems with input magnitude and rate constraints (2021)
Journal Article
Xing, X., Liu, J., & Yang, C. (2021). Adaptive neural control of a class of uncertain state and input-delayed systems with input magnitude and rate constraints. IEEE Transactions on Systems Man and Cybernetics: Systems, 1-15. https://doi.org/10.1109/tsmc.2021.3103276

This article aims at proposing an adaptive neural control strategy for a class of nonlinear time-delay systems with input delays and unknown control directions. Different from previous researches that investigated delays and constraints separately, t... Read More about Adaptive neural control of a class of uncertain state and input-delayed systems with input magnitude and rate constraints.

Robot Learning Human Skills and Intelligent Control Design (2021)
Book
Yang, C., Zeng, C., & Zhang, J. (2021). Robot Learning Human Skills and Intelligent Control Design. CRC Press. https://doi.org/10.1201/9781003119173

In the last decades robots are expected to be of increasing intelligence to deal with a large range of tasks. Especially, robots are supposed to be able to learn manipulation skills from humans. To this end, a number of learning algorithms and techni... Read More about Robot Learning Human Skills and Intelligent Control Design.

A novel iterative identification based on the optimised topology for common state monitoring in wireless sensor networks (2021)
Journal Article
Lu, Z., Wang, N., & Yang, C. (2022). A novel iterative identification based on the optimised topology for common state monitoring in wireless sensor networks. International Journal of Systems Science, 53(1), 25-39. https://doi.org/10.1080/00207721.2021.1936275

Power consumption and data redundancy of wireless sensor networks (WSN) are widely considered for a distributed state monitoring network. For reducing the energy consumption and data amount, we propose a topology optimisation and an iterative paramet... Read More about A novel iterative identification based on the optimised topology for common state monitoring in wireless sensor networks.

Review of the techniques used in motor‐cognitive human‐robot skill transfer (2021)
Journal Article
Guan, Y., Wang, N., & Yang, C. (2021). Review of the techniques used in motor‐cognitive human‐robot skill transfer. Cognitive Computation and Systems, 3(3), 229-252. https://doi.org/10.1049/ccs2.12025

A conventional robot programming method extensively limits the reusability of skills in the developmental aspect. Engineers programme a robot in a targeted manner for the realisation of predefined skills. The low reusability of general-purpose robot... Read More about Review of the techniques used in motor‐cognitive human‐robot skill transfer.

Robot learning system based on dynamic movement primitives and neural network (2021)
Journal Article
Zhang, Y., Li, M., & Yang, C. (2021). Robot learning system based on dynamic movement primitives and neural network. Neurocomputing, 451, 205-214. https://doi.org/10.1016/j.neucom.2021.04.034

In the process of Human-robot skill transfer, we require the robot to reproduce the trajectory of teacher and expect that the robot can generalize the learned trajectory. For the trajectory after generalization, we expect that the robot arm can accur... Read More about Robot learning system based on dynamic movement primitives and neural network.

A review on manipulation skill acquisition through teleoperation‐based learning from demonstration (2021)
Journal Article
Si, W., Wang, N., & Yang, C. (2021). A review on manipulation skill acquisition through teleoperation‐based learning from demonstration. Cognitive Computation and Systems, 3(1), 1-16. https://doi.org/10.1049/ccs2.12005

Manipulation skill learning and generalisation have gained increasing attention due to the wide applications of robot manipulators and the spurt of robot learning techniques. Especially, the learning from demonstration method has been exploited widel... Read More about A review on manipulation skill acquisition through teleoperation‐based learning from demonstration.

Distributed cooperative kinematic control of multiple robotic manipulators with improved communication efficiency (2021)
Journal Article
Zhang, J., Jin, L., & Yang, C. (2022). Distributed cooperative kinematic control of multiple robotic manipulators with improved communication efficiency. IEEE/ASME Transactions on Mechatronics, 27(1), 149 - 158. https://doi.org/10.1109/tmech.2021.3059441

An efficiency-oriented solution is theoretically a preferred choice to support the efficient operation of a system. Although some studies on the multi-manipulator system share the load of the control center by transforming the network topology, the w... Read More about Distributed cooperative kinematic control of multiple robotic manipulators with improved communication efficiency.

Composite dynamic movement primitives based on neural networks for human–robot skill transfer (2021)
Journal Article
Si, W., Wang, N., & Yang, C. (in press). Composite dynamic movement primitives based on neural networks for human–robot skill transfer. Neural Computing and Applications, 35, 23283–23293. https://doi.org/10.1007/s00521-021-05747-8

In this paper, composite dynamic movement primitives (DMPs) based on radial basis function neural networks (RBFNNs) are investigated for robots’ skill learning from human demonstrations. The composite DMPs could encode the position and orientation ma... Read More about Composite dynamic movement primitives based on neural networks for human–robot skill transfer.

A constrained DMPs framework for robot skills learning and generalization from human demonstrations (2021)
Journal Article
Lu, Z., Wang, N., & Yang, C. (2021). A constrained DMPs framework for robot skills learning and generalization from human demonstrations. IEEE/ASME Transactions on Mechatronics, 26(6), 3265 - 3275. https://doi.org/10.1109/TMECH.2021.3057022

Dynamical movement primitives (DMPs) model is a useful tool for efficiently robotic learning manipulation skills from human demonstrations and then generalizing these skills to fulfill new tasks. It is improved and applied for the cases with multiple... Read More about A constrained DMPs framework for robot skills learning and generalization from human demonstrations.

Learning compliant robotic movements based on biomimetic motor adaptation (2020)
Journal Article
Zeng, C., Chen, X., Wang, N., & Yang, C. (2021). Learning compliant robotic movements based on biomimetic motor adaptation. Robotics and Autonomous Systems, 135, Article 103668. https://doi.org/10.1016/j.robot.2020.103668

It is one of the great challenges for a robot to learn compliant movements in interaction tasks. The robot can easily acquire motion skills from a human tutor by kinematics demonstration, however, this becomes much more difficult when it comes to the... Read More about Learning compliant robotic movements based on biomimetic motor adaptation.

Robust neurooptimal control for a robot via adaptive dynamic programming (2020)
Journal Article
Kong, L., He, W., Yang, C., & Sun, C. (2021). Robust neurooptimal control for a robot via adaptive dynamic programming. IEEE Transactions on Neural Networks and Learning Systems, 32(6), 2584-2594. https://doi.org/10.1109/tnnls.2020.3006850

We aim at the optimization of the tracking control of a robot to improve the robustness, under the effect of unknown nonlinear perturbations. First, an auxiliary system is introduced, and optimal control of the auxiliary system can be seen as an appr... Read More about Robust neurooptimal control for a robot via adaptive dynamic programming.

Recent advances in robot-assisted echography: Combining perception, control and cognition (2020)
Journal Article
Lu, Z., Li, M., Annamalai, A., & Yang, C. (2020). Recent advances in robot-assisted echography: Combining perception, control and cognition. Cognitive Computation and Systems, 2(3), 85-92. https://doi.org/10.1049/ccs.2020.0015

Echography imaging is an important technique frequently used in medical diagnostics due to low-cost, non-ionising characteristics, and pragmatic convenience. Due to the shortage of skilful technicians and injuries of physicians sustained from diagnos... Read More about Recent advances in robot-assisted echography: Combining perception, control and cognition.

An incremental learning framework to enhance teaching by demonstration based on multimodal sensor fusion (2020)
Journal Article
Li, J., Zhong, J., Yang, J., & Yang, C. (2020). An incremental learning framework to enhance teaching by demonstration based on multimodal sensor fusion. Frontiers in Neurorobotics, 14(55), https://doi.org/10.3389/fnbot.2020.00055

Though a robot can reproduce the demonstration trajectory from a human demonstrator by teleoperation, there is a certain error between the reproduced trajectory and the desired trajectory. To minimize this error, we propose a multimodal incremental l... Read More about An incremental learning framework to enhance teaching by demonstration based on multimodal sensor fusion.

Disturbance observer enhanced variable gain controller for robot teleoperation with motion capture using wearable armbands (2020)
Journal Article
Huang, D., Yang, C., Ju, Z., & Dai, S. (2020). Disturbance observer enhanced variable gain controller for robot teleoperation with motion capture using wearable armbands. Autonomous Robots, 44(7), 1217-1231. https://doi.org/10.1007/s10514-020-09928-7

Disturbance observer (DOB) based controller performs well in estimating and compensating for perturbation when the external or internal unknown disturbance is slowly time varying. However, to some extent, robot manipulators usually work in complex en... Read More about Disturbance observer enhanced variable gain controller for robot teleoperation with motion capture using wearable armbands.

Novel joint-drift-free scheme at acceleration level for robotic redundancy resolution with tracking error theoretically eliminated (2020)
Journal Article
Jin, L., Xie, Z., Liu, M., Chen, K., Li, C., & Yang, C. (2021). Novel joint-drift-free scheme at acceleration level for robotic redundancy resolution with tracking error theoretically eliminated. IEEE/ASME Transactions on Mechatronics, 26(1), 90-101. https://doi.org/10.1109/tmech.2020.3001624

In this article, three acceleration-level joint-drift-free (ALJDF) schemes for kinematic control of redundant manipulators are proposed and analyzed from perspectives of dynamics and kinematics with the corresponding tracking error analyses. First, t... Read More about Novel joint-drift-free scheme at acceleration level for robotic redundancy resolution with tracking error theoretically eliminated.

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. (2021). Neural-learning-based force sensorless admittance control for robots with input deadzone. IEEE Transactions on Industrial Electronics, 68(6), 5184-5196. 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.

Disturbance observer-based fault-tolerant control for robotic systems with guaranteed prescribed performance (2020)
Journal Article
Huang, H., He, W., Li, J., Xu, B., Yang, C., & Zhang, W. (2022). Disturbance observer-based fault-tolerant control for robotic systems with guaranteed prescribed performance. IEEE Transactions on Cybernetics, 52(2), 772-783. https://doi.org/10.1109/tcyb.2019.2921254

The actuator failure compensation control problem of robotic systems possessing dynamic uncertainties has been investigated in this paper. Control design against partial loss of effectiveness (PLOE) and total loss of effectiveness (TLOE) of the actua... Read More about Disturbance observer-based fault-tolerant control for robotic systems with guaranteed prescribed performance.

Biologically inspired deadbeat control of robotic leg prostheses (2020)
Journal Article
Pi, M., Li, Z., Li, Q., Kan, Z., Xu, C., Kang, Y., …Yang, C. (2020). Biologically inspired deadbeat control of robotic leg prostheses. IEEE/ASME Transactions on Mechatronics, 25(6), 2733-2742. https://doi.org/10.1109/tmech.2020.2990406

Recent advances in robotics technology provide great support for robotic leg prostheses to realize full biomechanical functionalities of the contralateral leg. In order to reproduce the biomechanical behaviors of the contralateral leg, this article a... Read More about Biologically inspired deadbeat control of robotic leg prostheses.

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, Article 102348. 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.

Admittance-based controller design for physical human-robot interaction in the constrained task space (2020)
Journal Article
He, W., Xue, C., Yu, X., Li, Z., & Yang, C. (2020). Admittance-based controller design for physical human-robot interaction in the constrained task space. IEEE Transactions on Automation Science and Engineering, 17(4), 1937-1949. https://doi.org/10.1109/tase.2020.2983225

In this article, an admittance-based controller for physical human-robot interaction (pHRI) is presented to perform the coordinated operation in the constrained task space. An admittance model and a soft saturation function are employed to generate a... Read More about Admittance-based controller design for physical human-robot interaction in the constrained task space.

A method of motion recognition based on electromyographic signals (2020)
Journal Article
Luo, J., Liu, C., Feng, Y., & Yang, C. (2020). A method of motion recognition based on electromyographic signals. Advanced Robotics, 34(15), 976-984. 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. (2020). Hand gesture recognition in complex background based on convolutional pose machine and fuzzy gaussian mixture models. International Journal of Fuzzy Systems, 22, 1330-1341. 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. (2021). Reinforcement learning control of a flexible two-link manipulator: an experimental investigation. IEEE Transactions on Systems Man and Cybernetics: Systems, 51(12), 7326-7336. 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), Article 279. 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), Article 1135. 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.

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.

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.

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), Article 3731. 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.

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, Article 106309. 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.

Bio-inspired approach for long-range underwater navigation using model predictive control (2019)
Journal Article
Zhang, Y., Liu, X., Luo, M., & Yang, C. (2021). Bio-inspired approach for long-range underwater navigation using model predictive control. IEEE Transactions on Cybernetics, 51(8), 4286-4297. https://doi.org/10.1109/TCYB.2019.2933397

Lots of evidence has indicated that many kinds of animals can achieve goal-oriented navigation by spatial cognition and dead reckoning. The geomagnetic field (GF) is a ubiquitous cue for navigation by these animals. Inspired by the goal-oriented navi... Read More about Bio-inspired approach for long-range underwater navigation using model predictive control.

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), Article 2099. 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.

Asymmetric bounded neural control for an uncertain robot by state feedback and output feedback (2019)
Journal Article
Kong, L., He, W., Dong, Y., Cheng, L., Yang, C., & Li, Z. (2021). Asymmetric bounded neural control for an uncertain robot by state feedback and output feedback. IEEE Transactions on Systems Man and Cybernetics: Systems, 51(3), 1735-1746. https://doi.org/10.1109/TSMC.2019.2901277

In this paper, an adaptive neural bounded control scheme is proposed for an n-link rigid robotic manipulator with unknown dynamics. With the combination of the neural approximation and backstepping technique, an adaptive neural network control policy... Read More about Asymmetric bounded neural control for an uncertain robot by state feedback and output feedback.

A task learning mechanism for the telerobots (2019)
Journal Article
Luo, J., Yang, C., Li, Q., & Wang, M. (2019). A task learning mechanism for the telerobots. International Journal of Humanoid Robotics, 16(2), 1950009. https://doi.org/10.1142/S0219843619500099

Telerobotic systems have attracted growing attention because of their superiority in the dangerous or unknown interaction tasks. It is very challengeable to exploit such systems to implement complex tasks in an autonomous way. In this paper, we propo... Read More about A task learning mechanism for the telerobots.

Efficient 3D object recognition via geometric information preservation (2019)
Journal Article
Liu, H., Cong, Y., Yang, C., & Tang, Y. (2019). Efficient 3D object recognition via geometric information preservation. Pattern Recognition, 92, 135-145. https://doi.org/10.1016/j.patcog.2019.03.025

© 2019 Elsevier Ltd Accurate 3D object recognition and 6-DOF pose estimation have been pervasively applied to a variety of applications, such as unmanned warehouse, cooperative robots, and manufacturing industry. How to extract a robust and represent... Read More about Efficient 3D object recognition via geometric information preservation.

Adaptive fuzzy control for coordinated multiple robots with constraint using impedance learning (2019)
Journal Article
Kong, L., He, W., Yang, C., Li, Z., & Sun, C. (2019). Adaptive fuzzy control for coordinated multiple robots with constraint using impedance learning. IEEE Transactions on Cybernetics, 49(8), 3052-3063. https://doi.org/10.1109/TCYB.2018.2838573

In this paper, we investigate fuzzy neural network (FNN) control using impedance learning for coordinated multiple constrained robots carrying a common object in the presence of the unknown robotic dynamics and the unknown environment with which the... Read More about Adaptive fuzzy control for coordinated multiple robots with constraint using impedance learning.

Admittance-based adaptive cooperative control for multiple manipulators with output constraints (2019)
Journal Article
Li, Y., Yang, C., Yan, W., Cui, R., & Annamalai, A. (2019). Admittance-based adaptive cooperative control for multiple manipulators with output constraints. IEEE Transactions on Neural Networks and Learning Systems, 30(12), 3621-3632. https://doi.org/10.1109/TNNLS.2019.2897847

This paper proposes a novel adaptive control methodology based on the admittance model for multiple manipulators transporting a rigid object cooperatively along a predefined desired trajectory. First, an admittance model is creatively applied to gene... Read More about Admittance-based adaptive cooperative control for multiple manipulators with output constraints.

Adaptive fuzzy Gaussian mixture models for shape approximation in Robot Grasping (2019)
Journal Article
Lin, H., Zhang, T., Chen, Z., Song, H., & Yang, C. (2019). Adaptive fuzzy Gaussian mixture models for shape approximation in Robot Grasping. International Journal of Fuzzy Systems, 21(4), 1026-1037. https://doi.org/10.1007/s40815-018-00604-8

Robotic grasping has always been a challenging task for both service and industrial robots. The ability of grasp planning for novel objects is necessary for a robot to autonomously perform grasps under unknown environments. In this work, we consider... Read More about Adaptive fuzzy Gaussian mixture models for shape approximation in Robot Grasping.

Improved human-robot collaborative control of redundant robot for teleoperated minimally invasive surgery (2019)
Journal Article
Su, H., Yang, C., Ferrigno, G., & De Momi, E. (2019). Improved human-robot collaborative control of redundant robot for teleoperated minimally invasive surgery. IEEE Robotics and Automation Letters, 4(2), 1447-1453. https://doi.org/10.1109/LRA.2019.2897145

© 2016 IEEE. An improved human-robot collaborative control scheme is proposed in a teleoperated minimally invasive surgery scenario, based on a hierarchical operational space formulation of a seven-degree-of-freedom redundant robot. Redundancy is exp... Read More about Improved human-robot collaborative control of redundant robot for teleoperated minimally invasive surgery.

An improved adaptive online neural control for robot manipulator systems using integral Barrier Lyapunov functions (2019)
Journal Article
Xia, J., Zhang, Y., Yang, C., Wang, M., & Annamalai, A. (2019). An improved adaptive online neural control for robot manipulator systems using integral Barrier Lyapunov functions. International Journal of Systems Science, 50(3), 638-651. https://doi.org/10.1080/00207721.2019.1567863

Conventional Neural Network (NN) control for robots uses radial basis function (RBF) and for n-link robot with online control, the number of nodes and weighting matrix increases exponentially, which requires a number of calculations to be performed w... Read More about An improved adaptive online neural control for robot manipulator systems using integral Barrier Lyapunov functions.

Enhanced teleoperation performance using hybrid control and virtual fixture (2019)
Journal Article
Luo, J., Yang, C., Wang, N., & Wang, M. (2019). Enhanced teleoperation performance using hybrid control and virtual fixture. International Journal of Systems Science, 50(3), 451-462. https://doi.org/10.1080/00207721.2018.1562128

To develop secure, natural and effective teleoperation, the perception of the slave plays a key role for the interaction of a human operator with the environment. By sensing slave information, the human operator can choose the correct operation in a... Read More about Enhanced teleoperation performance using hybrid control and virtual fixture.

Encoding Multiple Sensor Data for Robotic Learning Skills from Multimodal Demonstration (2019)
Journal Article
Zeng, C., Yang, C., Zhong, J., & Zhang, J. (2019). Encoding Multiple Sensor Data for Robotic Learning Skills from Multimodal Demonstration. IEEE Access, 7, 145604-145613. https://doi.org/10.1109/ACCESS.2019.2945484

© 2013 IEEE. Learning a task such as pushing something, where the constraints of both position and force have to be satisfied, is usually difficult for a collaborative robot. In this work, we propose a multimodal teaching-by-demonstration system whic... Read More about Encoding Multiple Sensor Data for Robotic Learning Skills from Multimodal Demonstration.

A Wave Variable Approach with Multiple Channel Architecture for Teleoperated System (2019)
Journal Article
Luo, J., Liu, C., Wang, N., & Yang, C. (2019). A Wave Variable Approach with Multiple Channel Architecture for Teleoperated System. IEEE Access, 7, 143912-143920. https://doi.org/10.1109/ACCESS.2019.2945674

© 2013 IEEE. Performance of teleoperation can be greatly influenced by time delay in the process of tele-manipulation with respect to accuracy and transparency. Wave variable is an effective algorithm to achieve a good stable capability. However, som... Read More about A Wave Variable Approach with Multiple Channel Architecture for Teleoperated System.

Estimation of EMG-Based force using a neural-network-based approach (2019)
Journal Article
Luo, J., Liu, C., & Yang, C. (2019). Estimation of EMG-Based force using a neural-network-based approach. IEEE Access, 7, 64856-64865. https://doi.org/10.1109/ACCESS.2019.2917300

© 2013 IEEE. The dynamics of human arms has a high impact on the humans' activities in daily life, especially when a human operates a tool such as interactions with a robot with the need for high dexterity. The dexterity of human arms depends largely... Read More about Estimation of EMG-Based force using a neural-network-based approach.

Head-raising of snake robots based on a predefined spiral curve method (2018)
Journal Article
Zhang, X., Liu, J., Ju, Z., & Yang, C. (2018). Head-raising of snake robots based on a predefined spiral curve method. Applied Sciences, 8(11), 2011. https://doi.org/10.3390/app8112011

© 2018 by the authors. A snake robot has to raise its head to acquire a wide visual space for planning complex tasks such as inspecting unknown environments, tracking a flying object and acting as a manipulator with its raising part. However, only a... Read More about Head-raising of snake robots based on a predefined spiral curve method.

A neural-network-based controller for piezoelectric-actuated stick-slip devices (2017)
Journal Article
Cheng, L., Liu, W., Yang, C., Huang, T., Hou, Z. G., & Tan, M. (2018). A neural-network-based controller for piezoelectric-actuated stick-slip devices. IEEE Transactions on Industrial Electronics, 65(3), 2598-2607. https://doi.org/10.1109/TIE.2017.2740826

© 1982-2012 IEEE. Piezoelectric-actuated stick-slip device (PASSD) is a highly promising equipment that is composed of one end-effector, one piezoelectric actuator (PEA) and one driving object adhered to the PEA. Since the end-effector can slip on th... Read More about A neural-network-based controller for piezoelectric-actuated stick-slip devices.

Extended state observer-based integral sliding mode control for an underwater robot with unknown disturbances and uncertain nonlinearities (2017)
Journal Article
Cui, R., Chen, L., Yang, C., & Chen, M. (2017). Extended state observer-based integral sliding mode control for an underwater robot with unknown disturbances and uncertain nonlinearities. IEEE Transactions on Industrial Electronics, 64(8), 6785-6795. https://doi.org/10.1109/TIE.2017.2694410

© 2017 IEEE. This paper develops a novel integral sliding mode controller (ISMC) for a general type of underwater robots based on multiple-input and multiple-output extended-state-observer (MIMO-ESO). The difficulties associated with the unmeasured v... Read More about Extended state observer-based integral sliding mode control for an underwater robot with unknown disturbances and uncertain nonlinearities.

Development of a dynamics model for the Baxter robot (2016)
Presentation / Conference Contribution
Smith, A., Yang, C., Li, C., Ma, H., & Zhao, L. (2016). Development of a dynamics model for the Baxter robot. . https://doi.org/10.1109/ICMA.2016.7558740

© 2016 IEEE. The dynamics model of a robot is important to find the relation between the joint actuator torques and the resulting motion. There are two common methods to do this: The Lagrange formulation, which gives a closed form of the dynamics equ... Read More about Development of a dynamics model for the Baxter robot.

Neural-Learning-Based Telerobot Control with Guaranteed Performance (2016)
Journal Article
Yang, C., Wang, X., Cheng, L., & Ma, H. (2017). Neural-Learning-Based Telerobot Control with Guaranteed Performance. IEEE Transactions on Cybernetics, 47(10), 3148-3159. https://doi.org/10.1109/TCYB.2016.2573837

© 2013 IEEE. In this paper, a neural networks (NNs) enhanced telerobot control system is designed and tested on a Baxter robot. Guaranteed performance of the telerobot control system is achieved at both kinematic and dynamic levels. At kinematic leve... Read More about Neural-Learning-Based Telerobot Control with Guaranteed Performance.

Novel hybrid adaptive controller for manipulation in complex perturbation environments (2015)
Journal Article
Smith, A. M. C., Yang, C., Ma, H., Culverhouse, P., Cangelosi, A., & Burdet, E. (2015). Novel hybrid adaptive controller for manipulation in complex perturbation environments. PLoS ONE, 10(6), e0129281. https://doi.org/10.1371/journal.pone.0129281

© 2015 Smith et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cred... Read More about Novel hybrid adaptive controller for manipulation in complex perturbation environments.

Neural network-based motion control of an underactuated wheeled inverted pendulum model (2014)
Journal Article
Yang, C., Li, Z., Cui, R., & Xu, B. (2014). Neural network-based motion control of an underactuated wheeled inverted pendulum model. IEEE Transactions on Neural Networks and Learning Systems, 25(11), 2004-2016. https://doi.org/10.1109/TNNLS.2014.2302475

In this paper, automatic motion control is investigated for one of wheeled inverted pendulum (WIP) models, which have been widely applied for modeling of a large range of two wheeled modern vehicles. First, the underactuated WIP model is decomposed i... Read More about Neural network-based motion control of an underactuated wheeled inverted pendulum model.

Dual adaptive control of bimanual manipulation with online fuzzy parameter tuning (2014)
Presentation / Conference Contribution
Smith, A., Yang, C., Ma, H., Culverhouse, P., Cangelosi, A., & Burdet, E. (2014). Dual adaptive control of bimanual manipulation with online fuzzy parameter tuning. . https://doi.org/10.1109/ISIC.2014.6967605

© 2014 IEEE. A biomimetic controller with online adaptation of impedance and force is applied to a full kinematic and dynamic model of the Baxter bimanual robot. A set of fuzzy logic engines are proposed to infer the values of tuning gains which affe... Read More about Dual adaptive control of bimanual manipulation with online fuzzy parameter tuning.