Zhaoyang Liu, Daqi Zhu, Chenxia Liu, and Simon X. Yang


  1. [1] B. Li, B. R. Page, J. Hoffman, B. Moridian, and N. Mah-moudian, Rendezvous planning for multiple AUVs with mobilecharging stations in dynamic currents, IEEE Robotics andAutomation Letters, 4(2), April 2019, 1653–1660.
  2. [2] J. Ni, L. Wu, P. Shi, and S.X. Yang, A dynamic bioinspiredneural network based real-time path planning method forautonomous underwater vehicles, Computational Intelligenceand Neuroscience, 2017, 2017.
  3. [3] Z. Wang, S. Yang, X. Xiang, A. Vasilijevi, N. Mikovi, and. Nað, Cloud-based mission control of USV fleet: Architec-ture, implementation and experiments, Control EngineeringPractice, 106, 2021, 104657.
  4. [4] X. Cao, H. Sun, and X. Xu, A novel cooperative huntingalgorithm for multi-AUV in underwater environments, Inter-national Journal of Robotics & Automation, 35, 2020, 425–435.
  5. [5] L. Wang, L. Liu, J. Qi, and W. Peng, Improved quantumparticle swarm optimization algorithm for offline path planningin AUVs, IEEE Access, 8, 2020, 143397–143411.
  6. [6] P.E. Hart, N.J. Nilsson, and B. Raphael, A formal basis forthe heuristic determination of minimum cost paths, IEEETransactions on Systems Science and Cybernetics, 4(2), July1968, 100–107.
  7. [7] B. Hao and Z. Yan, Recovery path planning for an agriculturalmobile robot by dubins-RRT algorithm, International Journalof Robotics & Automation, 33, 2018, 202–207.466
  8. [8] S. Shao, Y. Peng, C. He, and Y. Du, Efcient path plan-ning for UAV formation via comprehensively improved par-ticle swarm optimization, ISA Transactions, 97, Feb. 2020,415–430.
  9. [9] C. Xiong, D. Chen, D. Lu, Z. Zeng, and L. Lian, Path planningof multiple autonomous marine vehicles for adaptive samplingusing Voronoi based ant colony optimization, Robotics andAutonomous Systems, 115, May 2019, 90–103.
  10. [10] F.Y. Xie and X.P. Shi, A global path planning for mannedsubmersible based on improved ant colony algorithms, In-ternational Journal of Robotics & Automation, 36, 2021,204–210.
  11. [11] X. You, S. Liu, and C. Zhang, An improved ant colony systemalgorithm for robot path planning and performance analysis,International Journal of Robotics & Automation, 33, 2019,527–533.
  12. [12] W. Zhang, S. Wei, J. Zeng, and N. Wang, Multi-UUV pathplanning based on improved artificial potential field method,International Journal of Robotics & Automation, 36, 2021,231–239.
  13. [13] J. Ni, L. Yang, L. Wu, and X. Fan, An improved spinal neuralsystem-based approach for heterogeneous AUVs cooperativehunting, International Journal of Fuzzy Systems, 20, 2018,672–686.
  14. [14] Y. Cao, J. Gu, and Y. Zhang, Path planning-oriented ob-stacle avoiding workspace modelling for robot manipulator,International Journal of Robotics & Automation, 34, 2019,1–16.
  15. [15] X.B. Xiang, C.Y. Yu, and Q. Zhang, On intelligent risk analysisand critical decision of underwater robotic vehicles, OceanEngineering, 140, 2017, 453–465.
  16. [16] Y. Wang, Z. Liu, Z. Zuo, Z. Li, L. Wang, and X. Luo, Trajectoryplanning and safety assessment of autonomous vehicles basedon motion prediction and model predictive control, IEEETransactions on Vehicular Technology, 68(9), Sept. 2019,8546–8556.
  17. [17] Y. Rasekhipour, A. Khajepour, S. Chen, and B. Litkouhi, Apotential field-based model predictive path-planning controllerfor autonomous road vehicles, IEEE Transactions on IntelligentTransportation Systems, 18(5), May 2017, 1255–1267.
  18. [18] Y. Huang, et al., A motion planning and tracking frameworkfor autonomous vehicles based on artificial potential fieldelaborated resistance network approach, IEEE Transactionson Industrial Electronics, 67(2), Feb. 2020, 1376–1386.
  19. [19] C. Zhou, et al., The review unmanned surface vehicle path plan-ning: Based on multi-modality constraint, Ocean Engineering,200, 2020, 107043.
  20. [20] W. Gan, D. Zhu, Z. Hu, X. Shi, L. Yang, and Y. Chen, Modelpredictive adaptive constraint tracking control for underwatervehicles, IEEE Transactions on Industrial Electronics, 67(9),Sept. 2020, 7829–7840.

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