Receding-Horizon Trajectory Planning for Under-Actuated  Autonomous Vehicles Based on Collaborative Neurodynamic Optimization

Jiasen Wang; Jun Wang; Qing-Long Han

doi:10.1109/JAS.2022.105524

Volume 9 Issue 11

Nov. 2022

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 11.8, Top 4% (SCI Q1)

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2022 > 9(11): 1909-1923

J. S. 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, Nov. 2022. doi: 10.1109/JAS.2022.105524

Citation:

J. S. 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, Nov. 2022. doi: 10.1109/JAS.2022.105524

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Receding-Horizon Trajectory Planning for Under-Actuated Autonomous Vehicles Based on Collaborative Neurodynamic Optimization

doi: 10.1109/JAS.2022.105524

Jiasen Wang^1
,,
Jun Wang^{2
,
,},
Qing-Long Han^3
,

1.
Future Network Research Center, Purple Mountain Laboratories, Nanjing 211111, China
2.
Department of Computer Science, the School of Data Science, City University of Hong Kong, Hong Kong, China
3.
School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne VIC 3122, Australia

Funds: This work was supported in part by the Research Grants Council of the Hong Kong Special Administrative Region of China (11202318, 11203721), and the Australian Research Council (DP200100700)

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Author Bio:
Jiasen Wang (Member, IEEE) received the B.E. degree in automation from Wuhan University of Technology in 2011, the M.E. degree in control theory and control engineering from Zhejiang University in 2014, and the Ph.D. degree in computer science from City University of Hong Kong, China in 2020. He is currently a Research Fellow at the Purple Mountain Laboratories, China. His research interests are optimization and its applications in control and communication

Jun Wang (Life Fellow, IEEE) received the B.S. degree in electrical engineering and the M.S. degree in systems engineering from Dalian University of Technology, and the Ph.D. degree in systems engineering from Case Western Reserve University Cleveland, USA. He held various academic positions at Dalian University of Technology, China; Case Western Reserve University, Cleveland, USA; University of North Dakota, Grand Forks, USA; and the Chinese University of Hong Kong, China. He also held various short-term or part-time visiting positions at the USAF Armstrong Laboratory, Dayton, USA; RIKEN Brain Science Institute, Japan; Huazhong University of Science and Technology; Shanghai Jiao Tong University; and Dalian University of Technology. He is currently a Chair Professor in the Department of Computer Science and School of Data Science at City University of Hong Kong. His research interests include optimization and control theories and applications. He was the Editor-in-Chief of the IEEE Transactions on Cybernetics in 2014−2019. He also served in the Editorial Board or Editorial Advisory Board of the IEEE Transactions on Neural Networks, IEEE Transactions on Systems, Man, and Cybernetics-part C, Neural Networks, and International Journal of Neural Systems. He was a Guest Editor of special issues of several journals such as the European Journal of Operational Research, and International Journal of Neural Systems. He served as the General Chair of the IEEE World Congress on Computational Intelligence in 2008, 13th/25th International Conference on Neural Information Processing in 2006/2018. He is a Fellow of the International Association for Pattern Recognition and a Foreign Member of Academia Europaea. He was a recipient of the Research Excellence Award from The Chinese University of Hong Kong for 2008−2009, the Outstanding Achievement Award from Asia Pacific Neural Network Assembly, IEEE Transactions on Neural Networks Outstanding Paper Award in 2011, Neural Networks Pioneer Award from the IEEE Computational Intelligence Society in 2014, Norbert Wiener Award from the IEEE Systems, Man and Cybernetics Society in 2019, among other distinctions

Qing-Long Han (Fellow, IEEE) received the B.Sc. degree in mathematics from Shandong Normal University in 1983, and the M.Sc. and Ph.D. degrees in control engineering from East China University of Science and Technology, in 1992 and 1997, respectively. He is Pro Vice-Chancellor (Research Quality) and a Distinguished Professor at Swinburne University of Technology, Australia. He held various academic and management positions at Griffith University and Central Queensland University, Australia. His research interests include networked control systems, multiagent systems, time-delay systems, smart grids, unmanned surface vehicles, and neural networks. Professor Han was the recipient of The 2021 Norbert Wiener Award (the Highest Award in systems science and engineering, and cybernetics), The 2021 M. A. Sargent Medal (the Highest Award of the Electrical College Board of Engineers Australia), The 2021 IEEE/CAA Journal of Automatica Sinica Norbert Wiener Review Award, The 2020 IEEE Systems, Man, and Cybernetics (SMC) Society Andrew P. Sage Best Transactions Paper Award, The 2020 IEEE Transactions on Industrial Informatics Outstanding Paper Award, and The 2019 IEEE SMC Society Andrew P. Sage Best Transactions Paper Award. He is a Member of the Academia Europaea (The Academy of Europe) and a Fellow of The Institution of Engineers Australia. He is a Highly Cited Researcher in both Engineering and Computer Science (Clarivate Analytics, 2019−2021). He has served as an AdCom Member of IEEE Industrial Electronics Society (IES), a Member of IEEE IES Fellow Committee, and Chair of IEEE IES Technical Committee on Networked Control Systems. He is Co-Editor-in-Chief of IEEE Transactions on Industrial Informatics, Deputy Editor-in-Chief of IEEE/CAA Journal of Automatica Sinica, Co-Editor of Australian Journal of Electrical and Electronic Engineering, an Associate Editor for 12 international journals, including the IEEE Transactions on Cybernetics, IEEE Industrial Electronics Magazine, Control Engineering Practice, and Information Sciences, and a Guest Editor for 13 Special Issues
Corresponding author: Jun Wang, e-mail: jwang.cs@cityu.edu.hk
Received Date: 2021-11-16
Revised Date: 2022-01-15
Accepted Date: 2022-02-25

Available Online: 2022-04-11

Abstract

Abstract

This paper addresses a major issue in planning the trajectories of under-actuated autonomous vehicles based on neurodynamic optimization. A receding-horizon vehicle trajectory planning task is formulated as a sequential global optimization problem with weighted quadratic navigation functions and obstacle avoidance constraints based on given vehicle goal configurations. The feasibility of the formulated optimization problem is guaranteed under derived conditions. The optimization problem is sequentially solved via collaborative neurodynamic optimization in a neurodynamics-driven trajectory planning method/procedure. Simulation results with under-actuated unmanned wheeled vehicles and autonomous surface vehicles are elaborated to substantiate the efficacy of the neurodynamics-driven trajectory planning method.
- Collaborative neurodynamic optimization,
- receding-horizon planning,
- trajectory planning,
- under-actuated vehicles

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References

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The receding-horizon trajectory planning of under-actuated vehicles is formulated as a sequential optimization problem with kinetic, kinematic, collision-avoidance constraints
Conditions are derived for ensuring the feasibility of the sequential optimization problem
Conditions are derived for the global convergence of the neurodynamics-driven trajectory planning method

Receding-Horizon Trajectory Planning for Under-Actuated Autonomous Vehicles Based on Collaborative Neurodynamic Optimization

doi: 10.1109/JAS.2022.105524

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