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 avoidancefor unmanned underwater vehicles based on an improvedvelocity obstacle method, Sensors, 17(12), 2017, 2742.
  2. [2] W. Zhang, N. Wang, S. Wei, et al., Overview of unmannedunderwater vehicle swarm development status and key tech-nologies, Journal of Harbin Engineering University, 41(2),2019, 289–297.
  3. [3] W. Zhang, Y. Teng, S. Wei, et al., Underactuated UUV trackingcontrol of adaptive RBF neural network and backsteppingmethod, Journal of Harbin Engineering University, 39(1),2018, 93–99.
  4. [4] Z. Yan, P. Gong, W. Zhang, et al., AUV vision guided dockingexperiments 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, RobustH-infinity auxiliary driving heading control for a UUV in lowspeed mode, International Journal of Robotics & Automation,32(2), 2019, 176–182.238
  6. [6] B.K. Sahu and B. Subudhi, Flocking control of multiple AUVsbased on fuzzy potential functions, IEEE Transactions onFuzzy Systems, 26(5), 2018, 2539–2551.
  7. [7] C. Lamini, S. Benhlima, and A. Elbekri, Genetic algorithmbased 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 pathplanning 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 controlalgorithm for three-dimensional AUV path planning, Interna-tional Journal of Fuzzy Systems, 20(5), 2017, 1–14.
  10. [10] Z. Yan, B. Hao, W. Zhang, et al., Dubins-RRT path planningand 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 agriculturalmobile robot by dubins-RRT algorithm, International Journalof Robotics and Automation, 33(2), 2018, 303–307.
  12. [12] W. Deng, R. Chen, B. He, et al., A novel two-stage hybridswarm 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 robotpath planning based artificial potential field, 2016 IEEE 11thConference on Industrial Electronics and Applications, Hefei,2016, 1294–1299.
  14. [14] Y. Geva and A. Shapiro, A combined potential function andgraph search approach for free gait generation of quadrupedrobots, IEEE International Conference on Robotics and Au-tomation, Saint Paul, MN, 2012, 5371–5376.
  15. [15] S.S. Ge and Y.J. Cui, New potential functions for mobilerobot path planning, IEEE Transactions on Robotics andAutomation, 16(5), 2000, 615–620.
  16. [16] C. Ignacio, D.P. Mariano, W. Sen, et al., Adaptive low-level control of autonomous underwater vehicles using deepreinforcement learning, Robotics and Autonomous Systems,107, 2018, 71–86.
  17. [17] F.J. Solari, A.F. Rozenfeld, V.A. Sebasti´an, et al., Artificialpotential fields for the obstacles avoidance system of an AUVusing a mechanical scanning sonar, 2016 3rd IEEE/OES SouthAmerican International Symposium on Oceanic Engineering,Buenos Aires, 2016, 1–6.
  18. [18] N. Milad, K. Esmaeel, and D. Samira, Multi-objective multi-robot path planning in continuous environment using an en-hanced genetic algorithm, Expert Systems with Applications,115, 2019, 106–120.
  19. [19] A. Azzabi and K. Nouri, An advanced potential field methodproposed for mobile robot path planning, Transactions of theInstitute of Measurement and Control, 41(4), 2019, 1–13.
  20. [20] X.X. Liang, C.Y. Liu, X.L. Song, et al., Research on improvedartificial potential field approach in local path planning formobile robot, Computer Simulation, 35(4), 2018, 291–294.
  21. [21] D.F. Li, K.W. Li, H.B. Deng, et al., The 2D aquatic obstacleavoidance control algorithm of the snake-like robot based onartificial potential field and IB-LBM, Robot, 40(3), 2018, 346–359.
  22. [22] T. Weerakoon, K. Ishii, and A.A.F. Nassiraei, An artificialpotential field based mobile robot navigation method to preventfrom deadlock, Journal of Artificial Intelligence and SoftComputing Research, 5(3), 2015, 189–203.
  23. [23] S.M. Wang, M.C. Fang, and C.N. Huang, Vertical obstacleavoidance and navigation of autonomous underwater vehicleswith 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 avoidanceusing a danger index for a dynamic environment, AppliedSciences, 9(8), 2019, 1589.
  25. [25] H. Zhang, A discrete-time switched linear model of the par-ticle swarm optimization algorithm, Swarm and EvolutionaryComputation, 52, 2020, 100606.
  26. [26] X. Wang, V. Yadav, and S.N. Balakrishnan, CooperativeUAV formation flying with obstacle/collision avoidance, IEEETransactions on Control Systems Technology, 15(4), 2007,672–679.
  27. [27] A.D. Dang and J. Horn, Path planning for a formation ofautonomous robots in an unknown environment using artificialforce 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 routeplanning, International Journal of Robotics & Automation,34(3), 2019, 266–2722.
  29. [29] X. Li, Z. Shao, and J. Qian, An optimizing method based onautonomous animats: Fish-swarm algorithm, Systems Engi-neering – Theory and Practice, 22(11), 2002, 32–38.

Important Links:

Go Back