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Volume 9 Issue 11
Nov.  2022

IEEE/CAA Journal of Automatica Sinica

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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

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

doi: 10.1109/JAS.2022.105524
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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  • 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.


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  • [1]
    Y. Ma, Z. Wang, H. Yang, and L. Yang, “Artificial intelligence applications in the development of autonomous vehicles: A survey,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 315–329, 2020. doi: 10.1109/JAS.2020.1003021
    Y. Wang, M. Hou, K. N. Plataniotis, S. Kwong, H. Leung, E. Tunstel, I. J. Rudas, and L. Trajkovic, “Towards a theoretical framework of autonomous systems underpinned by intelligence and systems sciences,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 1, pp. 52–63, 2021.
    H.-L. Choi, A. K. Whitten, and J. P. How, “Decentralized task allocation for heterogeneous teams with cooperation constraints,” in Proc. American Control Conf., 2010, pp. 3057−3062.
    A. Matveev, M. Hoy, and A. Savkin, “A method for reactive navigation of nonholonomic under-actuated robots in maze-like environments,” Automatica, vol. 49, no. 5, pp. 1268–1274, 2013. doi: 10.1016/j.automatica.2013.01.046
    W. Zhao, Q. Meng, and P. W. Chung, “A heuristic distributed task allocation method for multivehicle multitask problems and its application to search and rescue scenario,” IEEE Trans. Cybern., vol. 46, no. 4, pp. 902–915, 2016. doi: 10.1109/TCYB.2015.2418052
    K. Dorling, J. Heinrichs, G. G. Messier, and S. Magierowski, “Vehicle routing problems for drone delivery,” IEEE Trans. Syst. Man Cybern.: Syst., vol. 47, no. 1, pp. 70–85, 2017. doi: 10.1109/TSMC.2016.2582745
    A. Vasilijević, D. Nađ, F. Mandić, N. Mišković, and Z. Vukić, “Coordinated navigation of surface and underwater marine robotic vehicles for ocean sampling and environmental monitoring,” IEEE/ASME Trans. Mechatronics, vol. 22, no. 3, pp. 1174–1184, 2017. doi: 10.1109/TMECH.2017.2684423
    G. Schouten and J. Steckel, “A biomimetic radar system for autonomous navigation,” IEEE Trans. on Robotics, vol. 35, no. 3, pp. 539–548, 2019. doi: 10.1109/TRO.2018.2889577
    B. Shang, L. Liu, J. Ma, and P. Fan, “Unmanned aerial vehicle meets vehicle-to-everything in secure communications,” IEEE Communications Magazine, vol. 57, no. 10, pp. 98–103, 2019. doi: 10.1109/MCOM.001.1900170
    X. Bai, W. Yan, and S. S. Ge, “Efficient task assignment for multiple vehicles with partially unreachable target locations,” IEEE Internet of Things Journal, vol. 8, no. 5, pp. 3730–3742, 2021. doi: 10.1109/JIOT.2020.3025797
    C. Zu, C. Yang, J. Wang, W. Gao, D. Cao, and F.-Y. Wang, “Simulation and field testing of multiple vehicles collision avoidance algorithms,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1045–1063, 2020. doi: 10.1109/JAS.2020.1003246
    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, 2022. doi: 10.1109/JAS.2021.1004060
    X. Ge, Q.-L. Han, J. Wang, and X. Zhang, “Scalable and resilient platooning control of cooperative automated vehicles,” IEEE Trans. Vehicular Technology, pp. 1–14, 2022. DOI: 10.1109/TVT.2022.3147371
    K. D. Do, Z. P. Jiang, and J. Pan, “Simultaneous tracking and stabilization of mobile robots: An adaptive approach,” IEEE Trans. Auto. Cont., vol. 49, no. 7, pp. 1147–1151, 2004. doi: 10.1109/TAC.2004.831139
    E. Mousavinejad, X. Ge, Q.-L. Han, T. J. Lim, and L. Vlacic, “An ellipsoidal set-membership approach to distributed joint state and sensor fault estimation of autonomous ground vehicles,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 6, pp. 1107–1118, 2021. doi: 10.1109/JAS.2021.1004015
    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, 2022. DOI: 10.1109/JAS.2021.1004263
    K. D. Do, Z.-P. Jiang, and J. Pan, “Underactuated ship global tracking under relaxed conditions,” IEEE Trans. on Auto. Cont., vol. 47, no. 9, pp. 1529–1536, 2002. doi: 10.1109/TAC.2002.802755
    H. Niu, Y. Lu, A. Savvaris, and A. Tsourdos, “An energy-efficient path planning algorithm for unmanned surface vehicles,” Ocean Engineering, vol. 161, pp. 308–321, 2018. doi: 10.1016/j.oceaneng.2018.01.025
    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. Industrial Informatics, vol. 17, no. 2, pp. 732–745, 2021. doi: 10.1109/TII.2020.3004343
    Q. Zhang, W. Pan, and V. Reppa, “Model-reference reinforcement learning for collision-free tracking control of autonomous surface vehicles,” IEEE Trans. Intelligent Transportation Systems, 2021. DOI: 10.1109/TITS.2021.3086033
    Y.-L. Wang and Q.-L. Han, “Network-based modelling and dynamic output feedback control for unmanned marine vehicles in network environments,” Automatica, vol. 91, pp. 43–53, 2018. doi: 10.1016/j.automatica.2018.01.026
    Y. Wang, Q.-L. Han, M. Fei, and C. Peng, “Network-based T-S fuzzy dynamic positioning controller design for unmanned marine vehicles,” IEEE Trans. Cybern., vol. 48, no. 9, pp. 2750–2763, 2018. doi: 10.1109/TCYB.2018.2829730
    Z. Peng and J. Wang, “Output-feedback path-following control of autonomous underwater vehicles ased on an extended state observer and projection neural networks,” IEEE Trans. Systems,Man,and Cybernetics: Systems, vol. 48, no. 4, pp. 535–544, April. 2018. doi: 10.1109/TSMC.2017.2697447
    Z. Peng, J. Wang, and Q.-L. Han, “Path-following control of autonomous underwater vehicles subject to velocity and input constraints via neurodynamic optimization,” IEEE Trans. Industrial Electronics, vol. 66, no. 11, pp. 8724–8732, Nov. 2019. doi: 10.1109/TIE.2018.2885726
    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, 2020. doi: 10.1109/JAS.2020.1003057
    J.-C. Latombe, Robot Motion Planning. New York, USA: Springer Science & Business Media, LLC, 1991.
    D. Morgan, G. P. Subramanian, S.-J. Chung, and F. Y. Hadaegh, “Swarm assignment and trajectory optimization using variable-swarm, distributed auction assignment and sequential convex programming,” Int. J. of Rob. Res., vol. 35, no. 10, pp. 1261–1285, 2016. doi: 10.1177/0278364916632065
    J. Li, X. Meng, M. Zhou, and X. Dai, “A two-stage approach to path planning and collision avoidance of multibridge machining systems,” IEEE Trans. Systems,Man,and Cybernetics: Systems, vol. 47, no. 7, pp. 1039–1049, 2017. doi: 10.1109/TSMC.2016.2531648
    Y. Zhao, Y. Wang, M. Zhou, and J. Wu, “Energy-optimal collision-free motion planning for multiaxis motion systems: An alternating quadratic programming approach,” IEEE Trans. Autom. Science and Engineering, vol. 16, no. 1, pp. 327–338, 2019. doi: 10.1109/TASE.2018.2864773
    B. Hu, Z. Cao, and M. Zhou, “An efficient RRT-based framework for planning short and smooth wheeled robot motion under kinodynamic constraints,” IEEE Trans. Industrial Electronics, vol. 68, no. 4, pp. 3292–3302, 2021. doi: 10.1109/TIE.2020.2978701
    M. Zhao, T. Anzai, K. Okada, K. Kawasaki, and M. Inaba, “Singularity-free aerial deformation by two-dimensional multilinked aerial robot with 1-DoF vectorable propeller,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 1367–1374, 2021. doi: 10.1109/LRA.2021.3056027
    J. Wang, J. Wang, and Q.-L. Han, “Multi-vehicle task assignment based on collaborative neurodynamic optimization with discrete Hopfield networks,” IEEE Trans. Neural Networks and Learning Systems, vol. 32, no. 12, pp. 5274–5286, Dec. 2021. doi: 10.1109/TNNLS.2021.3082528
    D. E. Koditschek and E. Rimon, “Robot navigation functions on manifolds with boundary,” Advances in Applied Mathematics, vol. 11, no. 4, pp. 412–442, 1990. doi: 10.1016/0196-8858(90)90017-S
    E. Rimon and D. E. Koditschek, “Exact robot navigation using artificial potential functions,” IEEE Trans. Robotics and Automation, vol. 8, no. 5, pp. 501–518, 1992. doi: 10.1109/70.163777
    D. V. Dimarogonas, S. G. Loizou, K. J. Kyriakopoulos, and M. M. Zavlanos, “A feedback stabilization and collision avoidance scheme for multiple independent non-point agents,” Automatica, vol. 42, no. 2, pp. 229–243, 2006. doi: 10.1016/j.automatica.2005.09.019
    S. G. Loizou and K. J. Kyriakopoulos, “Navigation of multiple kinematically constrained robots,” IEEE Trans. Robotics, vol. 24, no. 1, pp. 221–231, 2008. doi: 10.1109/TRO.2007.912092
    A. Widyotriatmo and K. Hong, “Navigation function-based control of multiple wheeled vehicles,” IEEE Trans. Industrial Electronics, vol. 58, no. 5, pp. 1896–1906, 2011. doi: 10.1109/TIE.2010.2051394
    Z. Kan, A. P. Dani, J. M. Shea, and W. E. Dixon, “Network connectivity preserving formation stabilization and obstacle avoidance via a decentralized controller,” IEEE Trans. Automatic Control, vol. 57, no. 7, pp. 1827–1832, 2012. doi: 10.1109/TAC.2011.2178883
    Y. Wang, D. Wang, and S. Zhu, “A new navigation function based decentralized control of multi-vehicle systems in unknown environments,” J Intell Robot Syst, vol. 87, pp. 363–377, 2017. doi: 10.1007/s10846-016-0450-0
    P. Ögren and N. E. Leonard, “A convergent dynamic window approach to obstacle avoidance,” IEEE Trans. Robotics, vol. 21, no. 2, pp. 188–195, 2005. doi: 10.1109/TRO.2004.838008
    K. Weekly, A. Tinka, L. Anderson, and A. M. Bayen, “Autonomous river navigation using the Hamilton-Jacobi framework for underactuated vehicles,” IEEE Trans. Robotics, vol. 30, no. 5, pp. 1250–1255, 2014. doi: 10.1109/TRO.2014.2327288
    C. Juang, M. Lai, and W. Zeng, “Evolutionary fuzzy control and navigation for two wheeled robots cooperatively carrying an object in unknown environments,” IEEE Trans. Cybernetics, vol. 45, no. 9, pp. 1731–1743, 2015. doi: 10.1109/TCYB.2014.2359966
    Z. Wang, G. Li, H. Jiang, Q. Chen, and H. Zhang, “Collision-free navigation of autonomous vehicles using convex quadratic programming-based model predictive control,” IEEE/ASME Trans. Mechatronics, vol. 23, no. 3, pp. 1103–1113, 2018. doi: 10.1109/TMECH.2018.2816963
    Y. Zhang, X. Liu, M. Luo, and C. Yang, “Bio-inspired approach for long-range underwater navigation using model predictive control,” IEEE Trans. Cybernetics, vol. 51, no. 8, pp. 4286–4297, 2021. doi: 10.1109/TCYB.2019.2933397
    C. Zhong, S. Liu, Q. Lu, B. Zhang, and S. X. Yang, “An efficient fine-to-coarse wayfinding strategy for robot navigation in regionalized environments,” IEEE Trans. Cybernetics, vol. 46, no. 12, pp. 3157–3170, 2016. doi: 10.1109/TCYB.2015.2498760
    A. Banino, C. Barry, B. Uria, C. Blundell, T. Lillicrap, P. Mirowski, A. Pritzel, M. J. Chadwick, T. Degris, J. Modayil, G. Wayne, H. Soyer, F. Viola, B. Zhang, R. Goroshin, N. Rabinowitz, R. Pascanu, C. Beattie, S. Petersen, A. Sadik, S. Gaffney, H. King, K. Kavukcuoglu, D. Hassabis, R. Hadsell, and D. Kumaran, “Vector-based navigation using grid-like representations in artificial agents,” Nature, vol. 557, pp. 429–433, 2018. doi: 10.1038/s41586-018-0102-6
    W. Yuan, Z. Li, and C. Su, “Multisensor-based navigation and control of a mobile service robot,” IEEE Trans. Systems,Man,and Cybernetics: Systems, vol. 51, no. 4, pp. 2624–2634, 2021. doi: 10.1109/TSMC.2019.2916932
    Y. Liu, Z. Li, T. Zhang, and S. Zhao, “Brain-robot interface-based navigation control of a mobile robot in corridor environments,” IEEE Trans. Systems,Man,and Cybernetics: Systems, vol. 50, no. 8, pp. 3047–3058, 2020. doi: 10.1109/TSMC.2018.2833857
    D. Fox, W. Burgard, and S. Thrun, “The dynamic window approach to collision avoidance,” IEEE Robotics Autom. Magazine, vol. 4, no. 1, pp. 23–33, 1997. doi: 10.1109/100.580977
    H. G. Tanner and J. L. Piovesan, “Randomized receding horizon navigation,” IEEE Trans. Automatic Control, vol. 55, no. 11, pp. 2640–2644, 2010. doi: 10.1109/TAC.2010.2063291
    A. Tahirovic and G. Magnani, “General framework for mobile robot navigation using passivity-based MPC,” IEEE Trans. Autom. Control, vol. 56, no. 1, pp. 184–190, 2011. doi: 10.1109/TAC.2010.2089654
    T. Howard, M. Pivtoraiko, R. A. Knepper, and A. Kelly, “Model-predictive motion planning: Several key developments for autonomous mobile robots,” IEEE Robotics Automa. Magazine, vol. 21, no. 1, pp. 64–73, 2014. doi: 10.1109/MRA.2013.2294914
    J. Liu, P. Jayakumar, J. L. Stein, and T. Ersal, “Combined speed and steering control in high-speed autonomous ground vehicles for obstacle avoidance using model predictive control,” IEEE Trans. Veh. Technol., vol. 66, no. 10, pp. 8746–8763, 2017. doi: 10.1109/TVT.2017.2707076
    S. X. Yang and M. Q.-H. Meng, “Real-time collision-free motion planning of a mobile robot using a neural dynamics-based approach,” IEEE Trans. Neural Networks, vol. 14, no. 6, pp. 1541–1552, 2003. doi: 10.1109/TNN.2003.820618
    J. Wang, J. Wang, and Q.-L. Han, “Neurodynamics-based model predictive control of continuous-time under-actuated mechatronic systems,” IEEE/ASME Trans. Mechatronics, vol. 26, no. 1, pp. 311–321, 2021.
    X. Le and J. Wang, “Robust pole assignment for synthesizing feedback control systems using recurrent neural networks,” IEEE Trans. Neural Networks and Learning Syst., vol. 25, no. 2, pp. 383–393, 2014. doi: 10.1109/TNNLS.2013.2275732
    Z. Peng, J. Wang, and D. Wang, “Distributed containment maneuvering of multiple marine vessels via neurodynamics-based output feedback,” IEEE Trans. Ind. Electronics, vol. 64, no. 5, pp. 3831–3839, 2017. doi: 10.1109/TIE.2017.2652346
    H. Che and J. Wang, “A collaborative neurodynamic approach to global and combinatorial optimization,” Neural Networks, vol. 114, pp. 15–27, 2019. doi: 10.1016/j.neunet.2019.02.002
    Z. Yan, J. Fan, and J. Wang, “A collective neurodynamic approach to constrained global optimization,” IEEE Trans. Neural Networks and Learning Syst., vol. 28, no. 5, pp. 1206–1215, 2017. doi: 10.1109/TNNLS.2016.2524619
    D. Ioan, I. Prodan, S. Olaru, F. Stoican, and S.-I. Niculescu, “Mixed-integer programming in motion planning,” Annual Reviews in Control, vol. 51, pp. 65–87, 2021. doi: 10.1016/j.arcontrol.2020.10.008
    J. Nocedal and S. J. Wright, Numerical Optimization. New York, USA: Springer, 2006.
    J.-J. E. Slotine and W. Li, Applied Nonlinear Control. NJ, USA: Prentice hall Englewood Cliffs, 1991.
    Z.-P. Jiang, “Controlling underactuated mechanical systems: A review and open problems,” in Advances in the Theory of Control, Signals and Systems with Physical Modeling. Berlin, Heidelberg, Germany: Springer, 2011, pp. 77−88.
    F. Fahimi, “Non-linear model predictive formation control for groups of autonomous surface vessels,” Int. J. Control, vol. 80, no. 8, pp. 1248–1259, 2007. doi: 10.1080/00207170701280911


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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


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