A Scalable Adaptive Approach to Multi-Vehicle Formation Control with Obstacle Avoidance

Xiaohua Ge; Qing-Long Han; Jun Wang; Xian-Ming Zhang

doi:10.1109/JAS.2021.1004263

Volume 9 Issue 6

Jun. 2022

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2022 > 9(6): 990-1004

X. Ge, Q.-L. Han, J. Wang, and X.-M. Zhang, “A scalable adaptive approach to multi-vehicle formation control with obstacle avoidance,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 6, pp. 990–1004, Jun. 2022. doi: 10.1109/JAS.2021.1004263

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X. Ge, Q.-L. Han, J. Wang, and X.-M. Zhang, “A scalable adaptive approach to multi-vehicle formation control with obstacle avoidance,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 6, pp. 990–1004, Jun. 2022. doi: 10.1109/JAS.2021.1004263

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A Scalable Adaptive Approach to Multi-Vehicle Formation Control with Obstacle Avoidance

doi: 10.1109/JAS.2021.1004263

1.
School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC 3122, Australia
2.
Department of Computer Science and School of Data Science, City University of Hong Kong, China

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Author Bio:
Xiaohua Ge (Senior Member, IEEE) received the B.Eng. degree in electronics and information engineering from Nanchang Hangkong University, Nanchang, China, in 2008, the M.Eng. degree in control theory and control engineering from Hangzhou Dianzi University, Hangzhou, China, in 2011, and the Ph.D. degree in computer engineering from Central Queensland University, Rockhampton, QLD, Australia, in 2014.From 2011 to 2013, he was a Research Assistant with the Centre for Intelligent and Networked Systems, Central Queensland University, where he was a Research Fellow in 2014. From 2015 to 2017, he was a Research Fellow with the Griffith School of Engineering, Griffith University, Gold Coast, QLD, Australia. He is currently a Senior Lecturer with the School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, VIC, Australia. His research interests include networked, event-triggered, secure, and intelligent control and estimation, and their applications in autonomous vehicles and connected vehicles.Dr. Ge received The 2019 IEEE Systems, Man, and Cybernetics Society Andrew P. Sage Best Transactions Paper Award, The 2020 IEEE/CAA Journal of Automatica Sinica Outstanding Reviewer Award, and The 2017 IEEE Transactions on Cybernetics Outstanding Reviewer Award. He is an Associate Editor of the IEEE TRANSACTION ON SYSTEMS, MAN, AND CYBERNETICS: SYSTEMS and a Guest Editor for several Special Issues

Qing-Long Han (Fellow, IEEE) received the B.Sc. degree in Mathematics from Shandong Normal University, Jinan, China, in 1983, and the M.Sc. and Ph.D. degrees in Control Engineering from East China University of Science and Technology, Shanghai, China, in 1992 and 1997, respectively.Professor Han is Pro Vice-Chancellor (Research Quality) and a Distinguished Professor at Swinburne University of Technology, Melbourne, Australia. He held various academic and management positions at Griffith University and Central Queensland University, Australia. His research interests include networked control systems, multi-agent systems, time-delay systems, smart grids, unmanned surface vehicles, and neural networks.Professor Han was a Highly Cited Researcher in both Engineering and Computer Science (Clarivate Analytics, 2019-2020). He was one of Australia's Top 5 Lifetime Achievers (Research Superstars) in Engineering and Computer Science (The Australian's Research Magazine, 2019-2020). He was the recipient of The 2021 M. A. Sargent Medal (the Highest Award of the Electrical College Board of Engineers Australia), 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.Professor Han is a Member of the Academia Europaea (The Academy of Europe) and a Fellow of The Institution of Engineers Australia. 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, Co-Editor of Australian Journal of Electrical and Electronic Engineering, an Associate Editor for 12 international journals, including the IEEE TRANSACTIONS ON CYBERNETICS, the IEEE INDUSTRIAL ELECTRONICS MAGAZINE, the IEEE/CAA JOURNAL OF AUTOMATICA SINICA, Control Engineering Practice, and Information Sciences, and a Guest Editor for 13 Special Issues

Jun Wang (Fellow, IEEE) received the B.S. degree in electrical engineering and the M.S. degree in systems engineering from Dalian University of Technology, Dalian, China, in 1982 and 1985, respectively, and the Ph.D. degree in systems engineering from Case Western Reserve University, Cleveland, OH, USA, in 1991.He held various academic positions with Dalian University of Technology; Case Western Reserve University; University of North Dakota, Grand Forks, ND, USA; and the Chinese University of Hong Kong, China. He also held various short-term or part-time visiting positions with the U.S. Air Force Armstrong Laboratory, Dayton, OH, USA; RIKEN Brain Science Institute, Wako, Japan; Chinese Academy of Sciences, Beijing, China; Huazhong University of Science and Technology, Wuhan, China; Shanghai Jiao Tong University, Shanghai, China, as a Cheung Kong Chair Professor; and Dalian University of Technology. He is currently the Chair Professor of Computational Intelligence with the Department of Computer Science and School of Data Science, City University of Hong Kong, Hong Kong, China.Dr. Wang was a recipient of several awards, such as the Research Excellence Award from the Chinese University of Hong Kong from 2008 to 2009, the Outstanding Achievement Award from AsiaPacific Neural Network Assembly in 2011, the IEEE TRANSACTIONS ON NEURAL NETWORKS Outstanding Paper Award in 2011, the Neural Networks Pioneer Award from the IEEE Computational Intelligence Society in 2014, the Wu Wenjun AI Achievement Award from Chinese Association of AI in 2016, and the Norbert Wiener Award from the IEEE Systems, Man and Cybernetics Society in 2019. He served as the President of the AsiaPacific Neural Network Assembly in 2006, the General Chair of the 13th/25th International Conference on Neural Information Processing in 2006 and 2018, the IEEE World Congress on Computational Intelligence in 2008, and the Program Chair of the IEEE International Conference on Systems, Man, and Cybernetics in 2012. In addition, he has served on many committees, such as the IEEE Fellow Committee. He is a Distinguished Lecturer of the IEEE Systems, Man, and Cybernetics Society from 2017 to 2021, a Distinguished Lecturer of the IEEE Computational Intelligence Society from 2010 to 2012 and from 2014 to 2016. He was the Editor-in-Chief of the IEEE TRANSACTIONS ON CYBERNETICS from 2014 to 2019

Xian-Ming Zhang (Senior Member, IEEE) received the M.Sc. degree in Applied Mathematics and the Ph.D. degree in Control Theory and Control Engineering from Central South University, Changsha, China, in 1992 and 2006, respectively.In 1992, he joined Central South University, where he was an Associate Professor with the School of Mathematics and Statistics. From 2007 to 2014, he was a Post-Doctoral Research Fellow and a Lecturer with the School of Engineering and Technology, Central Queensland University, Rockhampton, QLD, Australia. From 2014 to 2016, he was a Lecturer with the Griffith School of Engineering, Griffith University, Gold Coast, QLD, Australia. In 2016, he joined the Swinburne University of Technology, Melbourne, VIC, Australia, where he is currently an Associate Professor with the School of Software and Electrical Engineering. His current research interests include H-infinity filtering, event-triggered control systems, networked control systems, neural networks, distributed systems, and time-delay systems.Associate Professor Zhang was a recipient of second National Natural Science Award in China in 2013, and first Hunan Provincial Natural Science Award in Hunan Province in China in 2011, both jointly with Prof. M. Wu and Prof. Y. He. He was also the recipient of The 2020 IEEE Transactions on Industrial Informatics Outstanding Paper Award, The 2019 IEEE Systems, Man, and Cybernetics Society Andrew P. Sage Best Transactions Paper Award, and The 2016 IET Control Theory and Applications Premium Award. He is an Associate Editor of the IEEE Transactions on Cybernetics, Journal of the Franklin Institute, International Journal of Control, Automation, and Systems, Neurocomputing, and Neural Processing Letters
Corresponding author: Qing-Long Han, e-mail: qhan@swin.edu.au
Received Date: 2021-07-24
Accepted Date: 2021-08-09

Available Online: 2021-08-20

Abstract

Abstract

This paper deals with the problem of distributed formation tracking control and obstacle avoidance of multi-vehicle systems (MVSs) in complex obstacle-laden environments. The MVS under consideration consists of a leader vehicle with an unknown control input and a group of follower vehicles, connected via a directed interaction topology, subject to simultaneous unknown heterogeneous nonlinearities and external disturbances. The central aim is to achieve effective and collision-free formation tracking control for the nonlinear and uncertain MVS with obstacles encountered in formation maneuvering, while not demanding global information of the interaction topology. Toward this goal, a radial basis function neural network is used to model the unknown nonlinearity of vehicle dynamics in each vehicle and repulsive potentials are employed for obstacle avoidance. Furthermore, a scalable distributed adaptive formation tracking control protocol with a built-in obstacle avoidance mechanism is developed. It is proved that, with the proposed protocol, the resulting formation tracking errors are uniformly ultimately bounded and obstacle collision avoidance is guaranteed. Comprehensive simulation results are elaborated to substantiate the effectiveness and the promising collision avoidance performance of the proposed scalable adaptive formation control approach.
- Adaptive control,
- collision avoidance,
- distributed formation control,
- multi-vehicle systems,
- neural networks,
- obstacle avoidance,
- repulsive potential

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References(47)

References

[1]	Y. Cao, W. Yu, W. Ren, and G. Chen, “An overview of recent progress in the study of distributed multi-agent coordination,” IEEE Trans. Ind. Informat., vol. 9, no. 1, pp. 427–438, Feb. 2013. doi: 10.1109/TII.2012.2219061
[2]	K.-K. Oh, M.-C. Park, and H.-S. Ahn, “A survey of multi-agent formation control,” Automatica, vol. 53, pp. 424–440, Mar. 2015. doi: 10.1016/j.automatica.2014.10.022
[3]	X. Ge, Q.-L. Han, L. Ding, Y.-L. Wang, and X.-M. Zhang, “Dynamic event-triggered distributed coordination control and its applications: A survey of trends and techniques,” IEEE Trans. Syst. Man Cybern.,Syst., vol. 50, no. 9, pp. 3112–3125, Sep. 2020. doi: 10.1109/TSMC.2020.3010825
[4]	Z. Peng, J. Wang, D. Wang, and Q.-L. Han, “An overview of recent advances in coordinated control of multiple autonomous surface vehicles,” IEEE Trans. Ind. Informat., vol. 17, no. 2, pp. 732–745, Feb. 2021. doi: 10.1109/TII.2020.3004343
[5]	S. Coogan and M. Arcak, “Scaling the size of a formation using relative position feedback,” Automatica, vol. 48, no. 10, pp. 2677–2685, Oct. 2012. doi: 10.1016/j.automatica.2012.06.083
[6]	S.-M. Kang and H.-S. Ahn, “Design and realization of distributed adaptive formation control law for multi-agent systems with moving leader,” IEEE Trans. Ind. Electron., vol. 63, no. 2, pp. 1268–1279, Feb. 2016. doi: 10.1109/TIE.2015.2504041
[7]	X. Ge and Q.-L. Han, “Distributed formation control of networked multi-agent systems using a dynamic event-triggered communication mechanism,” IEEE Trans. Ind. Electron., vol. 64, no. 10, pp. 8118–8127, Oct. 2017. doi: 10.1109/TIE.2017.2701778
[8]	S. Zhao, “Affine formation maneuver control of multiagent systems,” IEEE Trans. Autom. Control, vol. 63, no. 12, pp. 4140–4155, Dec. 2018. doi: 10.1109/TAC.2018.2798805
[9]	Z. Gao and G. Guo, “Fixed-time sliding mode formation control of AUVs based on a disturbance observer,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 539–545, Mar. 2020. doi: 10.1109/JAS.2020.1003057
[10]	X. Ge, Q.-L. Han, and X.-M. Zhang, “Achieving cluster formation of multi-agent systems under aperiodic sampling and communication delays,” IEEE Trans. Ind. Electron., vol. 65, no. 4, pp. 3417–3426, Apr. 2018. doi: 10.1109/TIE.2017.2752148
[11]	M. Porfiri, D. Roberson, and D. Stilwell, “Tracking and formation control of multiple autonomous agents: A two-level consensus approach,” Automatica, vol. 43, no. 8, pp. 1318–1328, Aug. 2007. doi: 10.1016/j.automatica.2007.01.004
[12]	J. Lü, F. Chen, and G. Chen, “Nonsmooth leader-following formation control of nonidentical multi-agent systems with directed communication topologies,” Automatica, vol. 64, pp. 112–120, Feb. 2016. doi: 10.1016/j.automatica.2015.11.004
[13]	J. Hu, P. Bhowmick, and A. Lanzon, “Distributed adaptive time-varying group formation tracking for multiagent systems with multiple leaders on directed graphs,” IEEE Trans. Control Network Syst., vol. 7, no. 1, pp. 140–150, Mar. 2020. doi: 10.1109/TCNS.2019.2913619
[14]	G. Wen, C. Chen, and Y.-J. Liu, “Formation control with obstacle avoidance for a class of stochastic multiagent systems,” IEEE Trans. Ind. Electron., vol. 65, no. 7, pp. 5847–5855, Jul. 2018. doi: 10.1109/TIE.2017.2782229
[15]	Y. Yang, X. Si, D. Yue, and J. Tan, “Time-varying formation tracking of uncertain non-affine nonlinear multi-agent systems with communication delays,” IEEE Trans. Ind. Electron., vol. 68, no. 3, pp. 2501–2509, Mar. 2021. doi: 10.1109/TIE.2020.2975463
[16]	X. Dong, Y. Li, C. Lu, G. Hu, Q. Li, and Z. Ren, “Time-varying formation tracking for UAV swarm systems with switching directed topologies,” IEEE Trans. Neural Netw. Learn. Syst., vol. 30, no. 12, pp. 3674–3685, Dec. 2019. doi: 10.1109/TNNLS.2018.2873063
[17]	Y. Chen, R. Yu, Y. Zhang, and C. Lin, “Circular formation flight control for unmanned aerial vehicles with directed network and external disturbance,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 505–516, Mar. 2020. doi: 10.1109/JAS.2019.1911669
[18]	X. Ge, S. Xiao, Q.-L. Han, X.-M. Zhang, and D. Ding, “Dynamic event-triggered scheduling and platooning control co-design for automated vehicles over vehicular ad-hoc networks,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 1, pp. 31–46, Jan. 2022.
[19]	S. Xiao, X. Ge, Q.-L. Han, and Y. Zhang, “Secure distributed adaptive platooning control for automated vehicles over vehicular ad-hoc networks under DoS attacks,” IEEE Trans. Cybern., 2021. DOI: 10.1109/TCYB.2021.3074318
[20]	Y. Wang, Y. Song, and W. Ren, “Distributed adaptive finite-time approach for formation-containment control of networked nonlinear systems under directed topology,” IEEE Trans. Neural Netw. Learn. Syst., vol. 29, no. 7, pp. 3164–3175, Jul. 2018.
[21]	A. Amini, A. Asif, and A. Mohammadi, “Formation-containment control using dynamic event-triggering mechanism for multi-agent systems,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 5, pp. 1235–1248, Sep. 2020.
[22]	B.-B. Hu, H.-T. Zhang, and J. Wang, “Multiple-target surrounding and collision avoidance with second-order nonlinear multi-agent systems,” IEEE Trans. Ind. Electron., vol. 68, no. 8, pp. 7454–7463, Aug. 2021. doi: 10.1109/TIE.2020.3000092
[23]	S. Zuo and D. Yue, “Resilient output formation containment of heterogeneous multigroup systems against unbounded attacks,” IEEE Trans. Cybern., vol. 52, no. 3, pp. 1902–1910, Mar. 2022.
[24]	H. Zhang and F. Lewis, “Adaptive cooperative tracking control of higher-order nonlinear systems with unknown dynamics,” Automatica, vol. 48, no. 7, pp. 1432–1439, Jul. 2012. doi: 10.1016/j.automatica.2012.05.008
[25]	Z. Peng, D. Wang, H. Zhang, and G. Sun, “Distributed neural network control for adaptive synchronization of uncertain dynamical multiagent systems,” IEEE Trans. Neural Netw. Learn. Syst., vol. 25, no. 4, pp. 1508–1519, Aug. 2014.
[26]	F. Gao, W. Chen, Z. Li, J. Li, and B. Xu, “Neural network-based distributed cooperative learning control for multiagent systems via event-triggered communication,” IEEE Trans. Neural Netw. Learn. Syst., vol. 31, no. 2, pp. 407–419, Feb. 2020. doi: 10.1109/TNNLS.2019.2904253
[27]	H. Liang, G. Liu, H. Zhang, and T. Huang, “Neural-network-based event-triggered adaptive control of nonaffine nonlinear multiagent systems with dynamic uncertainties,” IEEE Trans. Neural Netw. Learn. Syst., vol. 32, no. 5, pp. 2239–2250, May 2021. doi: 10.1109/TNNLS.2020.3003950
[28]	Y. Liu, D. Yao, H. Li, and R. Lu, “Distributed cooperative compound tracking control for a platoon of vehicles with adaptive NN,” IEEE Trans. Cybern., 2020. DOI: 10.1109/TCYB.2020.3044883
[29]	G. Dong, H. Li, H. Ma, and R. Lu, “Finite-time consensus tracking neural network FTC of multi-agent systems,” IEEE Trans. Neural Netw. Learn. Syst., vol. 32, no. 2, pp. 653–662, Feb. 2021. doi: 10.1109/TNNLS.2020.2978898
[30]	Z. Li, L. Gao, W. Chen, and Y. Xu, “Distributed adaptive cooperative tracking of uncertain nonlinear fractional-order multi-agent systems,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 292–300, Jan. 2020. doi: 10.1109/JAS.2019.1911858
[31]	L. Wang, A. Ames, and M. Egerstedt, “Safety barrier certificates for collisions-free multirobot systems,” IEEE Trans. Robotics, vol. 33, no. 3, pp. 661–674, Jun. 2017. doi: 10.1109/TRO.2017.2659727
[32]	J. Fu, G. Wen, X. Yu, and Z.-G. Wu, “Distributed formation navigation of constrained second-order multiagent systems with collision avoidance and connectivity maintenance,” IEEE Trans. Cybern., vol. 52, no. 4, pp. 2149–2162, Apr. 2022.
[33]	W. Dunbar and R. Murray, “Distributed receding horizon control for multi-vehicle formation stabilization,” Automatica, vol. 42, no. 4, pp. 549–558, Apr. 2006. doi: 10.1016/j.automatica.2005.12.008
[34]	P. Wang and B. Ding, “Distributed RHC for tracking and formation of nonholonomic multi-vehicle systems,” IEEE Trans. Autom. Control, vol. 59, no. 6, pp. 1439–1453, Jun. 2014. doi: 10.1109/TAC.2014.2304175
[35]	E. Hernandez-Martinez and E. Aranda-Bricaire, “Convergence and collision avoidance in formation control: A survey of the artificial potential functions approach, ” in Multi-Agent Systems: Modeling, Control, Programming, Simulations and Applications, F. Alkhateeb, E. A. Maghayreh, and I. A. Doush, Eds. Princeton, NJ: InTech, 2011, ser. Robotics. Multiagent Systems, pp. 103–126, ch. 6.
[36]	H. Rezaee and F. Abdollahi, “A decentralized cooperative control scheme with obstacle avoidance for a team of mobile robots,” IEEE Trans. Ind. Electron., vol. 61, no. 1, pp. 347–354, Jan. 2014. doi: 10.1109/TIE.2013.2245612
[37]	S. Ajwad, E. Moulay, M. Defoort, T. Ménard, and P. Coirault, “Collision-free formation tracking of multi-agent systems under communication constraints,” IEEE Control Syst. Lett., vol. 5, no. 4, pp. 1345–1350, Oct. 2021. doi: 10.1109/LCSYS.2020.3036809
[38]	Z.-H. Pang, C.-B. Zheng, J. Sun, Q.-L. Han, and G.-P. Liu, “Distance- and velocity-based collision avoidance for time-varying formation control of second-order multi-agent systems,” IEEE Trans. Circuits Syst. II,Exp. Briefs, vol. 68, no. 4, pp. 1253–1257, Apr. 2021. doi: 10.1109/TCSII.2020.3022371
[39]	S. Huang, R. Teo, and K. Tan, “Collision avoidance of multi unmanned aerial vehicles: A review,” Annu. Rev. Control, vol. 48, pp. 147–164, Oct. 2019. doi: 10.1016/j.arcontrol.2019.10.001
[40]	Z. Li, G. Wen, Z. Duan, and W. Ren, “Designing fully distributed consensus protocols for linear multi-agent systems with directed graphs,” IEEE Trans. Autom. Control, vol. 60, no. 4, pp. 1152–1157, Apr. 2015. doi: 10.1109/TAC.2014.2350391
[41]	Z. Peng, D. Wang, and J. Wang, “Predictor-based neural dynamic surface control for uncertain nonlinear systems in strict-feedback form,” IEEE Trans. Neural Netw. Learn. Syst., vol. 28, no. 9, pp. 2156–2167, Sep. 2017.
[42]	P. Du, Y. Pan, H. Li, and H.-K. Lam, “Nonsingular finite-time event-triggered fuzzy control for large-scale nonlinear systems,” IEEE Trans. Fuzzy Syst., vol. 29, no. 8, pp. 2088–2099, Aug. 2021.
[43]	C. Wang, X. Wang, and H. Ji, “Leader-following consensus for an integrator-type nonlinear multi-agent systems using distributed adaptive protocol, ” in Proc. 10th IEEE Int. Conf. Control and Automation, 2013, pp. 1166–1171.
[44]	F. Lewis, S. Jagannathan, and A. Yesildirak, Neural Network Control of Robot Manipulators and Nonlinear Systems, Philadelphia, PA, USA: CRC Press, 1998.
[45]	C. Nowzari, E. Garcia, and J. Cortés, “Event-triggered communication and control of networked systems for multi-agent consensus,” Automatica, vol. 105, pp. 1–27, 2019. doi: 10.1016/j.automatica.2019.03.009
[46]	X.-M. Zhang, Q.-L. Han, X. Ge, D. Ding, L. Ding, D. Yue, and C. Peng, “Networked control systems: A survey of trends and techniques,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 1–17, Jan. 2020. doi: 10.1109/JAS.2019.1911861
[47]	X. Ge, Q.-L. Han, X.-M. Zhang, and D. Ding, “Dynamic event-triggered control and estimation: A survey,” Int. J. Autom. Comput., vol. 18, no. 6, pp. 857–886, Dec. 2021.

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A scalable and collision-free adaptive formation tracking control protocol
An efficient built-in collision avoidance mechanism
A design algorithm of the desired adaptive formation tracking control laws
A multi-vehicle formation tracking case study in an obstacle-laden 2D plane
A collision-free vehicle platooning case study in a longitudinal plane

A Scalable Adaptive Approach to Multi-Vehicle Formation Control with Obstacle Avoidance

doi: 10.1109/JAS.2021.1004263

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