All Outputs (3)

Grasping detection of dual manipulators based on Markov decision process with neural network (2023)
Journal Article
Yun, J., Jiang, D., Huang, L., Tao, B., Liao, S., Liu, Y., …Chen, B. (2024). Grasping detection of dual manipulators based on Markov decision process with neural network. Neural Networks, 169, 778-792. https://doi.org/10.1016/j.neunet.2023.09.016

With the development of artificial intelligence, robots are widely used in various fields, grasping detection has been the focus of intelligent robot research. A dual manipulator grasping detection model based on Markov decision process is proposed t... Read More about Grasping detection of dual manipulators based on Markov decision process with neural network.

A novel curved gaussian mixture model and its application in motion skill encoding (2021)
Conference Proceeding
Chen, D., Li, G., Zhou, D., & Ju, Z. (2021). A novel curved gaussian mixture model and its application in motion skill encoding. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (7813-7818). https://doi.org/10.1109/IROS51168.2021.9636121

The purpose of this paper is to present a novel curved Gaussian Mixture Model (CGMM) and to study the application of it in motion skill encoding. Primarily, Gaussian mixture model (GMM) has been widely applied on many occasions when a probability den... Read More about A novel curved gaussian mixture model and its application in motion skill encoding.

Grasping force prediction based on sEMG signals (2020)
Journal Article
Ma, R., Zhang, L., Li, G., Jiang, D., Xu, S., & Chen, D. (2020). Grasping force prediction based on sEMG signals. Alexandria Engineering Journal, 59(3), 1135-1147. https://doi.org/10.1016/j.aej.2020.01.007

In order to realize the force control, when the prosthetic hand grasps the object, the forearm electromyography signal is collected by the multi-channel surface electromyography (sEMG) acquisition system. The grasping force information of the human h... Read More about Grasping force prediction based on sEMG signals.