Runqi Chai
Design and Experimental Validation of Deep Reinforcement Learning-Based Fast Trajectory Planning and Control for Mobile Robot in Unknown Environment
Chai, Runqi; Niu, Hanlin; Carrasco, Joaquin; Arvin, Farshad; Yin, Hujun; Lennox, Barry
Authors
Hanlin Niu
Joaquin Carrasco
Professor Farshad Arvin farshad.arvin@durham.ac.uk
Professor
Hujun Yin
Barry Lennox
Citation
Chai, R., Niu, H., Carrasco, J., Arvin, F., Yin, H., & Lennox, B. (2024). Design and Experimental Validation of Deep Reinforcement Learning-Based Fast Trajectory Planning and Control for Mobile Robot in Unknown Environment. IEEE Transactions on Neural Networks and Learning Systems, 35(4), 5778-5792. https://doi.org/10.1109/tnnls.2022.3209154
Journal Article Type | Article |
---|---|
Acceptance Date | Sep 18, 2022 |
Online Publication Date | Oct 10, 2022 |
Publication Date | 2024-04 |
Deposit Date | Oct 12, 2022 |
Journal | IEEE Transactions on Neural Networks and Learning Systems |
Print ISSN | 2162-237X |
Electronic ISSN | 2162-2388 |
Publisher | Institute of Electrical and Electronics Engineers |
Peer Reviewed | Peer Reviewed |
Volume | 35 |
Issue | 4 |
Pages | 5778-5792 |
DOI | https://doi.org/10.1109/tnnls.2022.3209154 |
Public URL | https://durham-repository.worktribe.com/output/1189344 |
Related Public URLs | https://research.manchester.ac.uk/en/publications/design-and-experimental-validation-of-deep-reinforcement-learning |
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