| Dynamic neural networks for motion-force control of redundant manipulators: An optimization perspective Z Xu, S Li, X Zhou, S Zhou, T Cheng, Y Guan IEEE transactions on industrial electronics 68 (2), 1525-1536, 2020 | 85 | 2020 |
| Dynamic neural networks based kinematic control for redundant manipulators with model uncertainties Z Xu, S Li, X Zhou, W Yan, T Cheng, D Huang Neurocomputing 329, 255-266, 2019 | 63 | 2019 |
| Motion planning of manipulators for simultaneous obstacle avoidance and target tracking: An RNN approach with guaranteed performance Z Xu, X Zhou, H Wu, X Li, S Li IEEE Transactions on Industrial Electronics 69 (4), 3887-3897, 2021 | 46 | 2021 |
| Dynamic Neural Networks Based Adaptive Admittance Control for Redundant Manipulators with Model Uncertainties Z Xu, S Li, X Zhou, T Cheng Neurocomputing, 2019 | 42 | 2019 |
| Simultaneous obstacle avoidance and target tracking of multiple wheeled mobile robots with certified safety X Li, Z Xu, S Li, Z Su, X Zhou IEEE transactions on cybernetics 52 (11), 11859-11873, 2021 | 31 | 2021 |
| Cooperative kinematic control for multiple redundant manipulators under partially known information using recurrent neural network X Li, Z Xu, S Li, H Wu, X Zhou IEEE Access 8, 40029-40038, 2020 | 30 | 2020 |
| Fast object pose estimation using adaptive threshold for bin-picking W Yan, Z Xu, X Zhou, Q Su, S Li, H Wu IEEE Access 8, 63055-63064, 2020 | 28 | 2020 |
| Deep recurrent neural networks based obstacle avoidance control for redundant manipulators Z Xu, X Zhou, S Li Frontiers in neurorobotics 13, 47, 2019 | 27 | 2019 |
| MOPSO based multi-objective trajectory planning for robot manipulators Z Xu, S Li, Q Chen, B Hou 2015 2nd International Conference on Information Science and Control …, 2015 | 20 | 2015 |
| Review of wheeled mobile robot collision avoidance under unknown environment Y Wang, X Li, J Zhang, S Li, Z Xu, X Zhou Science Progress 104 (3), 00368504211037771, 2021 | 16 | 2021 |
| Incremental learning introspective movement primitives from multimodal unstructured demonstrations H Wu, Z Xu, W Yan, Q Su, S Li, T Cheng, X Zhou IEEE Access 7, 159022-159036, 2019 | 14 | 2019 |
| Collaboration of multiple SCARA robots with guaranteed safety using recurrent neural networks Y He, X Li, Z Xu, X Zhou, S Li Neurocomputing 456, 1-10, 2021 | 13 | 2021 |
| A vary-parameter convergence-accelerated recurrent neural network for online solving dynamic matrix pseudoinverse and its robot application X Li, S Li, Z Xu, X Zhou Neural Processing Letters 53 (2), 1287-1304, 2021 | 10 | 2021 |
| Recurrent neural networks-based collision-free motion planning for dual manipulators under multiple constraints J Liang, Z Xu, X Zhou, S Li, G Ye IEEE Access 8, 54225-54236, 2020 | 10 | 2020 |
| Collision-free compliance control for redundant manipulators: an optimization case X Zhou, Z Xu, S Li Frontiers in neurorobotics 13, 50, 2019 | 10 | 2019 |
| A framework of robot skill learning from complex and long-horizon tasks H Wu, W Yan, Z Xu, T Cheng, X Zhou IEEE Transactions on Automation Science and Engineering 19 (4), 3628-3638, 2021 | 9 | 2021 |
| Learning robot anomaly recovery skills from multiple time-driven demonstrations H Wu, W Yan, Z Xu, S Li, X Zhou Neurocomputing 464, 522-532, 2021 | 9 | 2021 |
| Dynamic neural networks based adaptive optimal impedance control for redundant manipulators under physical constraints Z Xu, X Li, S Li, H Wu, X Zhou Neurocomputing 471, 149-160, 2022 | 8 | 2022 |
| AI based robot safe learning and control X Zhou, Z Xu, S Li, H Wu, T Cheng, X Lv Springer Nature, 2020 | 7 | 2020 |
| Incremental learning robot task representation and identification X Zhou, H Wu, J Rojas, Z Xu, S Li, X Zhou, H Wu, J Rojas, Z Xu, S Li Nonparametric Bayesian Learning for Collaborative Robot Multimodal …, 2020 | 6 | 2020 |
