A journal of IEEE and CAA , publishes
high-quality papers in English on original
theoretical/experimental research
and development in all areas of automation
Volume 11
Issue 5
IEEE/CAA Journal of Automatica Sinica
| Citation: | K. Chen, R. Chai, R. Zhang, Z. Xing, Y. Xia, and G. Liu, “A data-driven real-time trajectory planning and control methodology for UGVs using LSTMRDNN,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 5, pp. 1292–1294, May 2024. doi: 10.1109/JAS.2024.124269
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R. Chai, A. Tsourdos, A. Savvaris, S. Chai, Y. Xia, and C. Chen, “Six-DoF spacecraft optimal trajectory planning and real-time attitude control: A deep neural network-based approach,” IEEE Trans. Neural Networks and Learning Systems, vol. 31, no. 11, pp. 5005–5013, 2019.
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| [2] |
L. Chen, Y. Shan, W. Tian, B. Li, and D. Cao, “A fast and efficient double-tree RRT*-like sampling-based planner applying on mobile robotic systems,” IEEE/ASME Trans. Mechatronics, vol. 23, no. 6, pp. 2568–2578, 2018. doi: 10.1109/TMECH.2018.2821767
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| [3] |
Y. Guo, D. Yao, B. Li, Z. He, H. Gao, and L. Li, “Trajectory planning for an autonomous vehicle in spatially constrained environments,” IEEE Trans. Intelligent Transportation Systems, vol. 23, no. 10, pp. 18326–18336, 2022. doi: 10.1109/TITS.2022.3164548
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| [4] |
B. Li, L. Li, T. Acarman, Z. Shao, and M. Yue, “Optimization-based maneuver planning for a tractor-trailer vehicle in a curvy tunnel: A weak reliance on sampling and search,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 706–713, 2021.
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| [5] |
G. P. Kontoudis and K. G. Vamvoudakis, “Kinodynamic motion planning with continuous-time q-learning: An online, model-free, and safe navigation framework,” IEEE Trans. Neural Networks and Learning Systems, vol. 30, no. 12, pp. 3803–3817, 2019. doi: 10.1109/TNNLS.2019.2899311
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J. Fan, X. Chen, and X. Liang, “UAV trajectory planning based on bidirectional APF-RRT* algorithm with goal-biased,” Expert Systems With Applications, vol. 213, p. 119137, 2023. doi: 10.1016/j.eswa.2022.119137
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J. Wang, J. Wang, and Q.-L. Han, “Receding-horizon trajectory planning for under-actuated autonomous vehicles based on collaborative neurodynamic optimization,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 11, pp. 1909–1923, 2022. doi: 10.1109/JAS.2022.105524
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Z. Wang, L. B. Freidovich, and H. Zhang, “Periodic motion planning and control for double rotary pendulum via virtual holonomic constraints,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 1, pp. 291–298, 2017.
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W. Hu, Z. Deng, D. Cao, B. Zhang, A. Khajepour, L. Zeng, and Y. Wu, “Probabilistic lane-change decision-making and planning for autonomous heavy vehicles,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 12, pp. 2161–2173, 2022. doi: 10.1109/JAS.2022.106049
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C. Sánchez-Sánchez and D. Izzo, “Real-time optimal control via deep neural networks: Study on landing problems,” J. Guidance,Control,and Dynamics, vol. 41, no. 5, pp. 1122–1135, 2018. doi: 10.2514/1.G002357
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R. Chai, H. Niu, J. Carrasco, F. Arvin, H. Yin, and B. Lennox, “Design and experimental validation of deep reinforcement learning-based fast trajectory planning and control for mobile robot in unknown environment,” IEEE Trans. Neural Networks and Learning Systems, 2022. DOI: 10.1109/TNNLS.2022.3209154
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K. J. Åström and B. Wittenmark, Adaptive Control. New York, USA: Dover Publications, 2008.
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K. Vamvoudakis, F. Lewis, and S. S. Ge, “Neural networks in feedback control systems,” Mechanical Engineers’Handbook, pp. 1–52, 2014.
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D. Nodland, H. Zargarzadeh, and S. Jagannathan, “Neural network-based optimal adaptive output feedback control of a helicopter UAV,” IEEE Trans. Neural Networks and Learning Systems, vol. 24, no. 7, pp. 1061–1073, 2013. doi: 10.1109/TNNLS.2013.2251747
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R. Chai, A. Tsourdos, S. Chai, Y. Xia, A. Savvaris, and C. Chen, “Multiphase overtaking maneuver planning for autonomous ground vehicles via a desensitized trajectory optimization approach,” IEEE Trans. Industrial Informatics, vol. 19, no. 1, pp. 74–87, 2022.
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