OPTIMAL MOTION PLANNING FOR A MULTI-UUV SYSTEM WITH A FOUR NEURON-BASED NN AND KM ALGORITHM UNDER OCEAN CURRENTS

Danjie Zhu, Ya-Jun Pan, and Simon X. Yang

References

  1. [1] D. An, Y. Mu, Y. Wang, B. Li, and Y. Wei, Intelligent pathplanning technologies of underwater vehicles: a review, Journalof Intelligent and Robotic Systems: Theory and Applications,107(2), 2023, doi: 10.1007/s10846-022-01794-y
  2. [2] Y. Cao, W. Yu, W. Ren, and G. Chen, An overview of recentprogress in the study of distributed multi-agent coordination,Transactions on Industrial Informatics, 9(1), 2013, 427–438,doi: 10.1109/TII.2012.2219061
  3. [3] W. Zhang, S. Wei, J. Zeng, and N. Wang, Multi-UUVpath planning based on improved artificial potential fieldmethod, International Journal of Robotics and Automation,36(4), 2021, 231–239, doi: http://dx.doi.org/10.2316/J.2021.206-0531
  4. [4] M. Panda, B. Das, B. Subudhi, and B. Pati, A comprehensivereview of path planning algorithms for autonomous underwatervehicles, International Journal of Robotics and Automation,17(3), 2020, 321–352, doi: https://doi.org/10.1007/s11633-019-1204-9
  5. [5] C. Wang, D. Mei, Y. Wang, X. Yu, W. Sun, and J.Chen, Task allocation for multi-AUV system: A review,Ocean Engineering, 266, 2022, doi: https://doi.org/10.1016/j.oceaneng.2022.112911
  6. [6] G. Bai, Y. Chen, X. Hu, Y. Shi, W. Jiang, and X.Zhang, Multi-AUV dynamic trajectory optimization andcollaborative search combined with task urgency and energyconsumption scheduling in 3-D underwater environment withrandom ocean currents and uncertain obstacles, Ocean Engi-neering, 275, 2023, doi: https://doi.org/10.1016/j.oceaneng.2023.113841
  7. [7] D. Zhu, C. Cheng, and B. Sun, An integrated AUV pathplanning algorithm with ocean current and dynamic obstacles,International Journal of Robotics and Automation, 31(5), 2016,382–389, doi: http://dx.doi.org/10.2316/Journal.206.2016.5.206-4570
  8. [8] Z. Zhou, J. Liu, and J. Yu, A survey of underwater multi-robotsystems, IEEE/CAA Journal of Automatica Sinica, 9(1), 2022,1–18, doi: 10.1109/JAS.2021.1004269
  9. [9] X. Wang, X. Yao, and L. Zhang, Path planning underconstraints and path following control of autonomousunderwater vehicle with dynamical uncertainties and wavedisturbances, Journal of Intelligent and Robotic Systems:Theory and Applications, 99(3-4), 2020, 891–908, doi:https://doi.org/10.1007/s10846-019-01146-3
  10. [10] X. Cao, C. Sun, and M. Chen, Path planning for autonomousunderwater vehicle in time-varying current, IEEE Transactionson Intelligent Transportation Systems, 13(8), 2019, 1265–1271,doi: https://doi.org/10.1049/iet-its.2018.53889
  11. [11] C.S. Kulkarni and P.F. Lermusiaux, Three-dimensional time-optimal path planning in the ocean, Ocean Modelling, 152,2020, doi: https://doi.org/10.1016/j.ocemod.2020.101644
  12. [12] E. Dijkstra, Communication with an automatic computer,Ph.D. dissertation, University of Amsterdam, Netherlands,1959.
  13. [13] E.H. Peter, J.N. Nils, and R. Bertram, A formal basis forthe heuristic determination of minimum cost paths, IEEETransactions on Systems Science and Cybernetics, SSC-4(2),1968, 100–107, doi: 10.1109/TSSC.1968.300136
  14. [14] Y. Zhang, J. Lyu, and L. Fu, Energy-efficient trajectorydesign for UAV-aided maritime data collection in wind, IEEETransactions on Wireless Communications, 21(12), 2022,10871–10886, doi: 10.1109/TWC.2022.3187954
  15. [15] B.H. Wang, D.B. Wang, Z. Ali, B.T. Ting, and H. Wang, Anoverview of various kinds of wind effects on unmanned aerialvehicle, Measurement and Control, 52(7-8), 2019, 731–739, doi:https://doi.org/10.1177/0020294019847688
  16. [16] H. Cao, H. Cheng, and W. Zhu, Investigation of wind and soundfield characteristics of multi-rotor unmanned aerial vehicle,Noise and Vibration Worldwide, 51(7-9), 2020, 158–163, doi:https://doi.org/10.1177/0957456520923124
  17. [17] Y. Wu, K. H. Low, and C. Lv, Cooperative path planningfor heterogeneous unmanned vehicles in a search-and-trackmission aiming at an underwater target, IEEE Transactionson Vehicular Technology, 69(6), 2020, 6782–6787, doi:10.1109/TVT.2020.2991983
  18. [18] Y. Singh, S. Sharma, R. Sutton, D. Hatton, and A.Khan, A constrained A approach towards optimal pathplanning for an unmanned surface vehicle in a mar-itime environment containing dynamic obstacles and oceancurrents, Ocean Engineering, 169, 2018, 187–201, doi:https://doi.org/10.1016/j.oceaneng.2018.09.016
  19. [19] H. Yu and T. Su, A destination driven navigator withdynamic obstacle motion prediction, in Proceedings of 2001IEEE International Conference on Robotics and Automation,3, Seoul, South Korea, May 2001, pp. 2692–2697, doi:10.1109/ROBOT.2001.933029
  20. [20] S. Mahmoudzadeh, D. Powers, and A. Atyabi, UUV’s hierar-chical de-based motion planning in a semi dynamic underwaterwireless sensor network, IEEE Transactions on Cybernetics,49(8), 2019, 2992–3005, doi: 10.1109/TCYB.2018.2837134
  21. [21] V. T. Huynh, M. Dunbabin, and R. N. Smith, Predictive motionplanning for AUVs subject to strong time-varying currents andforecasting uncertainties, in 2015 IEEE international conferenceon robotics and automation (ICRA), IEEE, 2015, pp. 1144–1151. doi: 10.1109/ICRA.2015.7139335
  22. [22] S. MahmoudZadeh, D. Powers, A. Yazdani, K. Sammut,and A. Atyabi, “Efficient AUV path planning in time-variant underwater environment using differential evolutionalgorithm,” Journal of Marine Science and Application, 17(4),2018, 585–591, doi: https://doi.org/10.1007/s11804-018-0034-4
  23. [23] G. Han, Z. Zhou, T. Zhang, H. Wang, L. Liu, Y. Peng,and M. Guizani, Ant-colony-based complete-coverage path-planning algorithm for underwater gliders in ocean areas withthermoclines, IEEE Transactions on Vehicular Technology,69(8), 2020, 8959–8971, doi: 10.1109/TVT.2020.2998137
  24. [24] P. Salgado and P. Afonso, Evolutionary genes algorithmto path planning problems, in Proceedings of 2020 10thInternational Conference on Soft Computing and PatternRecognition: Advances in Intelligent Systems and Comput-ing, Cham, Switzerland, 2020, pp. 217–225, doi: https://doi.org/10.1007/978-3-030-17065-3 22
  25. [25] Z. Chu, F. Wang, T. Lei, and C. Luo, ’Path planningbased on deep reinforcement learning for autonomousunderwater vehicles under ocean current disturbance’, IEEETransactions on Intelligent Vehicles, 8(1), 2022, 108-120, doi:10.1109/TIV.2022.3153352.
  26. [26] J. Wu, C. Song, J. Ma, J. Wu, and G. Han, Reinforcementlearning and particle swarm optimization supporting real-time rescue assignments for multiple autonomous underwatervehicles, IEEE Transactions on Intelligent TransportationSystems, 2021, doi: 10.1109/TITS.2021.3062500
  27. [27] D. Zhu, H. Huang, and S. Yang, “Dynamic task assign-ment and path planning of multi-AUV system based onan improved self-organizing map and velocity synthesismethod in three-dimensional underwater workspace,” IEEETransactions on Cybernetics, 43(2), 2013, 504–514, doi:10.1109/TSMCB.2012.2210212
  28. [28] D. Zhu, X. Cao, B. Sun, and C. Luo, Biologically inspiredself-organizing map applied to task assignment and pathplanning of an AUV system, IEEE Transactions on Cognitiveand Developmental Systems, 10(2), 2018, 304–313, doi:10.1109/TCDS.2017.2727678
  29. [29] N.T. Hung, F.F.C. Rego, and A.M. Pascoal, Cooperativedistributed estimation and control of multiple autonomousvehicles for range-based underwater target localization andpursuit, IEEE Transactions on Control Systems Technology,2021, doi: 10.1109/TCST.2021.3107346
  30. [30] 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, 9269742–9269758, Article id: 9269742,doi: https://doi.org/10.1155/2017/9269742
  31. [31] D. Zhu, B. Zhou, and S. Yang, A novel algorithm of multi-AUVs task assignment and path planning based on biologicallyinspired neural network map, IEEE Transactions on IntelligentVehicles, 6(2), 2021, 333–342, doi: 10.1109/TIV.2020.3029369
  32. [32] D. Zhu and S. Yang, “Bio-inspired neural network-basedoptimal path planning for UUVs under the effect of oceancurrents,” IEEE Transactions on Intelligent Vehicles, 7(2),2022, 231–239, doi: https://doi.org/10.1109/TIV.2021.3082151
  33. [33] D. Zhu and S.X. Yang, Current effect-eliminated optimal targetassignment and motion planning for a multi-UUV system,IEEE Transactions on Intelligent Transportation Systems,2023. Article in press, doi: 10.1109/TITS.2024.3351442.
  34. [34] H. Kuhn, The Hungarian method for the assignment problem,Naval Research Logistics Quarterly, 2, 2018, 83–97, doi:https://doi.org/10.1002/nav.3800020109
  35. [35] J. Munkres, Algorithms for the assignment andtransportation problems, Journal of the Society forIndustrial and Applied Mathematics, 5, 1957, 32–38, doi:https://doi.org/10.1137/0105003

Important Links:

Go Back