A NOVEL PATH PLANNING FOR AUV BASED ON DUNG BEETLE OPTIMISATION ALGORITHM WITH DEEP Q-NETWORK

Baogang Li, Hanbin Zhang, and Xianpeng Shi

References

  1. [1] A. Sahoo, S.K. Dwivedy, and P.S. Robi, Advancements in thefield of autonomous underwater vehicle, Ocean Engineering,181, 2019, 145–160.
  2. [2] D. Zhu, Y. Zhao, and M. Yan, A bio-inspired neurodynamicsbased backstepping path-following control of an AUVwith ocean current, International Journal of Robotics andAutomation, 27, 2012.
  3. [3] L. Paull, S. Saeedi, M. Seto, and H. Li, AUV navigation andlocalization: A review, IEEE Journal of Oceanic Engineering,39(1), 2014, 131–149.
  4. [4] C. Cheng, Q. Sha, B. He, and G. Li, Path planning and obstacleavoidance for AUV: A review, Ocean Engineering, 235, 2021,109355.
  5. [5] Y. Guo, H. Liu, X. Fan, and W. Lyu, Research progressof path planning methods for autonomous underwatervehicle, Mathematical Problems in Engineering, 2021, 2021,1–25.
  6. [6] H. Zhang and X. Shi, An improved quantum-behaved particleswarm optimization algorithm combined with reinforcementlearning for AUV path planning, Journal of Robotics, 2023,2023, 8821906:1–8821906:11.
  7. [7] S. McPhail, R. Templeton, M. Pebody, D. Roper, and R.Morrison, Autosub long range AUV missions under the filchnerand ronne ice shelves in the Weddell Sea, Antarctica - anengineering perspective, Proc. OCEANS 2019 - Marseille,Marseille, France, 2019, 1–8.
  8. [8] X. Cao, H. Sun, and X. Xu, A novel cooperative hunt-ing algorithm for multi-AUV in underwater environments,International Journal of Robotics and Automation, 35, 2020,425–435.
  9. [9] W. Pang, D. Zhu, and S.X. Yang, A novel time-varyingformation obstacle avoidance algorithm for multiple AUVs,International Journal of Robotics and Automation, 38, 2023,194–207.8
  10. [10] J. Zhao, J. Zhou, S.X. Yang, and W. Zhang, A dynamic velocityregulation approach to planar trajectory tracking control ofunderactuated AUVs, International Journal of Robotics andAutomation, 32, 2017.
  11. [11] C. Cheng, Q. Sha, B. He, and G. Li, Path planning and obstacleavoidance for AUV: A review, Ocean Engineering, 235, 2021,109355.
  12. [12] Y. Guo, H. Liu, X. Fan, and W. Lyu, Research progressof path planning methods for autonomous underwatervehicle, Mathematical Problems in Engineering, 2021, 2021,1–25.
  13. [13] R. Dechter and J. Pearl, Generalized best-first search strategiesand the optimality of A, Journal of the ACM, 32(3), 1985,505–536.
  14. [14] S. Arinaga, S. Nakajima, H. Okabe, A. Ono, and Y. Kanayama,A motion planning method for an AUV, Proc. of Symposium onAutonomous Underwater Vehicle Technology, Monterey, CA,1996, 477–484. DOI: 10.1109/AUV.1996.532450.
  15. [15] W. Hongjian, Z. Hexiong, and Y. Hongfei, Research onautonomous planning method based on improved quantumparticle swarm optimization for autonomous underwatervehicle, Proc. OCEANS 2016 MTS/IEEE Monterey, Monterey,CA, 2016, 1–7. DOI: 10.1109/oceans.2016.7761143.
  16. [16] A. Colorni, M. Dorigo, and V. Maniezzo, Distributedoptimization by ant colonies, Computer Science, Engineering,Mathematics, 1992, 6.
  17. [17] D.A. Linkens and H.O. Nyongesa, Genetic algorithms for fuzzycontrol.1. Offline system development and application, IEEProceedings - Control Theory and Applications, 142(3), 1995,161–176.
  18. [18] V. Pshikhopov, Y. Chernukhin, V. Guzik, M. Medvedev,and B. Gurenko, Implementation of intelligent control systemfor autonomous underwater vehicle, AMM, 701–702, 2014,704–710.
  19. [19] J. Xue and B. Shen, Dung beetle optimizer: A new meta-heuristic algorithm for global optimization, The Journal ofSupercomputing, 79(7), 2023, 7305–7336.
  20. [20] R. Zhang and Y. Zhu, Predicting the mechanical properties ofheat-treated woods using optimization-algorithm-based BPNN,Forests, 14(5), 2023, 935.
  21. [21] W. Zhang, S. Zhang, F. Wu, and Y. Wang, Path planningof UAV based on improved adaptive grey wolf optimizationalgorithm, IEEE Access, 9, 2021, 89400–89411.
  22. [22] Y. Jia, L. Qu, and X. Li, Automatic path planning of unmannedcombat aerial vehicle based on double-layer coding method withenhanced grey wolf optimizer, Artificial Intelligence Review,56, 2023, 12257–12314. DOI: 10.1007/s10462-023-10481-9.
  23. [23] H. Huang and C. Jin, A novel particle swarm opti-mization algorithm based on reinforcement learning mech-anism for AUV path planning, Complexity, 2021, 2021,1–13.

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