MULTI-UUV PATH PLANNING BASED ON IMPROVED ARTIFICIAL POTENTIAL FIELD METHOD

Wei Zhang,∗ Shilin Wei,∗ Jia Zeng,∗ and Naixin Wang∗

References

  1. [1] W. Zhang, S. Wei, Y. Teng, et al., Dynamic obstacle avoidance for unmanned underwater vehicles based on an improved velocity obstacle method, Sensors, 17(12), 2017, 2742.
  2. [2] W. Zhang, N. Wang, S. Wei, et al., Overview of unmanned underwater vehicle swarm development status and key technologies, Journal of Harbin Engineering University, 41(2), 2019, 289–297.
  3. [3] W. Zhang, Y. Teng, S. Wei, et al., Underactuated UUV tracking control of adaptive RBF neural network and backstepping method, Journal of Harbin Engineering University, 39(1), 2018, 93–99.
  4. [4] Z. Yan, P. Gong, W. Zhang, et al., AUV vision guided docking experiments based on L-shaped light array, IEEE Access, 7, 2019, 72567–72576.
  5. [5] W. Zhang, Y. Teng, S. Wei, S. Hu, and Z. Yan, Robust H-infinity auxiliary driving heading control for a UUV in low speed mode, International Journal of Robotics & Automation, 32(2), 2019, 176–182. 238
  6. [6] B.K. Sahu and B. Subudhi, Flocking control of multiple AUVs based on fuzzy potential functions, IEEE Transactions on Fuzzy Systems, 26(5), 2018, 2539–2551.
  7. [7] C. Lamini, S. Benhlima, and A. Elbekri, Genetic algorithm based approach for autonomous mobile robot path planning, Procedia Computer Science, 127, 2018, 180–189.
  8. [8] X. Pan, X.S. Wu, X.G. Hou, and Y. Feng, Global path planning based on genetic-ant hybrid algorithm for AUV, Journal of Huazhong University of Science and Technology (Natural Science Edition), 45(5), 2017, 45–49.
  9. [9] B. Sun, D. Zhu, and S.X. Yang, An optimized fuzzy control algorithm for three-dimensional AUV path planning, International Journal of Fuzzy Systems, 20(5), 2017, 1–14.
  10. [10] Z. Yan, B. Hao, W. Zhang, et al., Dubins-RRT path planning and heading-vector control guidance for a UUV recovery, International Journal of Robotics and Automation, 31(3), 2016, 251–262.
  11. [11] B. Hao and Z. Yan, Recovery path planning for an agricultural mobile robot by dubins-RRT algorithm, International Journal of Robotics and Automation, 33(2), 2018, 303–307.
  12. [12] W. Deng, R. Chen, B. He, et al., A novel two-stage hybrid swarm intelligence optimization algorithm and application, Soft Computing, 16(10), 2012, 1707–1722.
  13. [13] X. Yang, W. Yang, H. Zhang, et al., A new method for robot path planning based artificial potential field, 2016 IEEE 11th Conference on Industrial Electronics and Applications, Hefei, 2016, 1294–1299.
  14. [14] Y. Geva and A. Shapiro, A combined potential function and graph search approach for free gait generation of quadruped robots, IEEE International Conference on Robotics and Automation, Saint Paul, MN, 2012, 5371–5376.
  15. [15] S.S. Ge and Y.J. Cui, New potential functions for mobile robot path planning, IEEE Transactions on Robotics and Automation, 16(5), 2000, 615–620.
  16. [16] C. Ignacio, D.P. Mariano, W. Sen, et al., Adaptive lowlevel control of autonomous underwater vehicles using deep reinforcement learning, Robotics and Autonomous Systems, 107, 2018, 71–86.
  17. [17] F.J. Solari, A.F. Rozenfeld, V.A. Sebastián, et al., Artificial potential fields for the obstacles avoidance system of an AUV using a mechanical scanning sonar, 2016 3rd IEEE/OES South American International Symposium on Oceanic Engineering, Buenos Aires, 2016, 1–6.
  18. [18] N. Milad, K. Esmaeel, and D. Samira, Multi-objective multirobot path planning in continuous environment using an enhanced genetic algorithm, Expert Systems with Applications, 115, 2019, 106–120.
  19. [19] A. Azzabi and K. Nouri, An advanced potential field method proposed for mobile robot path planning, Transactions of the Institute of Measurement and Control, 41(4), 2019, 1–13.
  20. [20] X.X. Liang, C.Y. Liu, X.L. Song, et al., Research on improved artificial potential field approach in local path planning for mobile robot, Computer Simulation, 35(4), 2018, 291–294.
  21. [21] D.F. Li, K.W. Li, H.B. Deng, et al., The 2D aquatic obstacle avoidance control algorithm of the snake-like robot based on artificial potential field and IB-LBM, Robot, 40(3), 2018, 346– 359.
  22. [22] T. Weerakoon, K. Ishii, and A.A.F. Nassiraei, An artificial potential field based mobile robot navigation method to prevent from deadlock, Journal of Artificial Intelligence and Soft Computing Research, 5(3), 2015, 189–203.
  23. [23] S.M. Wang, M.C. Fang, and C.N. Huang, Vertical obstacle avoidance and navigation of autonomous underwater vehicles with H∞ controller and the artificial potential field method, Journal of Navigation, 72(1), 2018, 1–22.
  24. [24] J.B. Sun, G.L. Liu, G.H. Tian, et al., Smart obstacle avoidance using a danger index for a dynamic environment, Applied Sciences, 9(8), 2019, 1589.
  25. [25] H. Zhang, A discrete-time switched linear model of the particle swarm optimization algorithm, Swarm and Evolutionary Computation, 52, 2020, 100606.
  26. [26] X. Wang, V. Yadav, and S.N. Balakrishnan, Cooperative UAV formation flying with obstacle/collision avoidance, IEEE Transactions on Control Systems Technology, 15(4), 2007, 672–679.
  27. [27] A.D. Dang and J. Horn, Path planning for a formation of autonomous robots in an unknown environment using artificial force fields, System Theory, Control and Computing (ICSTCC), Sinaia, 2014 18th International Conference. IEEE, 2014, 773–778.
  28. [28] U. Yayan and A. Yazici, Reliability-based multi-robot route planning, International Journal of Robotics & Automation, 34(3), 2019, 266–2722.
  29. [29] X. Li, Z. Shao, and J. Qian, An optimizing method based on autonomous animats: Fish-swarm algorithm, Systems Engineering – Theory and Practice, 22(11), 2002, 32–38.

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