A NOVEL COOPERATIVE HUNTING ALGORITHM FOR MULTI-AUV IN UNDERWATER ENVIRONMENTS, 425-435.

Xiang Cao, Hongbing Sun, and Xinyuan Xu

References

  1. [1] D. Zhu, X. Cao, B. Sun, and C. Luo, Biologically inspired self-organizing map applied to task assignment and path planning of an AUV system, IEEE Transactions on Cognitive and Developmental Systems, 10(2), 2018, 304–313.
  2. [2] H. Ge, G. Chen, and G. Xu, Multi-AUV cooperative target hunting based on improved potential field in a surface-water environment, Applied Science, 8, 2018, Art. no. 973, 1–12.
  3. [3] J. Liu, Y. Zhang, Y. Yu, et al., Fixed-time event-triggered consensus for nonlinear multiagent systems without continuous communications, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48, 2018, 1–9.
  4. [4] L. Cai, G. Zhou, and S. Zhang, Multi-AUV collaborative hunting method for the non-cooperative target in underwater environment, 3rd Int. Conf. Advanc. Robot. Mecha., 2018, 1–5.
  5. [5] M. Chen and D. Zhu, Multi-AUV cooperative hunting control with improved Glasius bio-inspired neural network, Journal of Navigation, 71, 2018, 1–18.
  6. [6] Z. Cao, C. Zhou, L. Cheng,Y. Yang, W. Zhang, and M. Tan, A distributed hunting approach for multiple autonomous robots, International Journal of Advanced Robotic Systems., 10(1), 2013, 217.
  7. [7] V. Yordanova, H. Griffiths, and S. Hailes, Rendezvous planning for multiple autonomous underwater vehicles using a Markov decision process, IET Radar, Sonar & Navigation, 11(12), 2017, 1762–1769.
  8. [8] Q. Han, S. Sun, and H. Lang, Leader-follower formation control of multi-robots based on bearing-only observations, International Journal of Robotics and Automation, 34(2), 2019, 120–129.
  9. [9] G. Zhang, J. Xu, Z. Yan, et al., Dynamic positioning control of UUV in the presence of disturbances caused by working manipulators, International Journal of Robotics and Automation, 32(5), 2017, 590–605.
  10. [10] A. Poorva and A. Himanshu, Adaptive algorithm design for cooperative hunting in multi-robots, International Journal of Intelligent Systems and Applications, 12, 2018, 47–55.
  11. [11] S. Zhang, M. Liu, X. Lei, et al., Multi-target trapping with swarm robots based on pattern formation, Robotics and Autonomous Systems, 106, 2018, 1–13.
  12. [12] Z. Cao, B. Zhang, S. Wang, and M. Tan, Cooperative hunting of multiple mobile robots in an unknown environment, Acta Mechanica Sinica, 29(4), 2003, 536–543.
  13. [13] Y. Song, Y. Li, C. Li, and X. Ma, Mathematical modeling and analysis of multirobot cooperative hunting behaviors, Journal of Robotics, 2015(11), 2015, Art. no. 184256.
  14. [14] J. Denzinger and M. Fuchs, Experiments in learning prototypical situations for variants of the pursuit game, Proc. Tech. Univ. Kaiserslautern, Fachbereich Inf., 1996, 48–55.
  15. [15] Y. Ma, Z. Cao, X. Dong, et al., A multi-robot coordinated hunting strategy with dynamic alliance, Proc. Chin. Control Decis. Conf., Shanghai, China, 2009, 2338–2342.
  16. [16] T. Chung, G. Hollinger, and V. Isler, Search and pursuitevasion in mobile robotics. Autonomous Robots, 31(4), 2011, Art. no. 229, 229–316.
  17. [17] R. Korf, A simple solution to pursuit games, Proc. Int. Workshop DAI, Geneva, Switzerland, 1992, 83–194.
  18. [18] T. Hazra, M. Nene, and C. Kumar, Modelling and analysis of information-based target searching using mobile sensors, International Journal of Systems, Control and Communications, 9(1), 2018, 53–74.
  19. [19] H. Yamaguchi, A cooperative hunting behavior by mobile-robot troops, The International Journal of Robotics Research, 18(8), 1999, 931–940.
  20. [20] H. Yamaguchui and T. Arai, Distributed and autonomous control method for generating shape of multiple mobile robot group, Proc. IEEE/RSJ Int. Conf. Int. Robots Syst., Munich, Germany, 1994, 800–807.
  21. [21] Y. Ishiwaka, T. Sato, and Y. Kakazu, An approach to the pursuit problem on a heterogeneous multiagent system using 434 reinforcement learning, Robotics and Autonomous Systems, 43(4), 2003, 245–256.
  22. [22] M. Sauter, D. Shi, and J. Kralik, Multi-agent reinforcement learning and chimpanzee hunting, Proc. IEEE Int. Conf. Robot. Biomimetics, Guilin, China, 2009, 622–626.
  23. [23] M. Wu, F. Huang, L. Wang, and J. Sun, A distributed multi-robot cooperative hunting algorithm based on limit-cycle, Proc. Int. Asia Conf. Informat. Control, Autom. Robot., Bangkok, Thailand, 2009, 156–160.
  24. [24] X. Cao and D. Zhu, A survey of cooperative hunting control algorithms for multi-AUV systems, Proc. 32nd Chin. Control Conf., Xi’an, China, 2013, 5791–5795.
  25. [25] X. Cao and C. Sun, A potential field-based PSO approach to multi-robot cooperation for target search and hunting, At Automatisierungstechnik, 65(12), 2017, 878–887.
  26. [26] Y. Rasekhipour, A. Khajepour, S. Chen, and B. Litkouhi, A potential field-based model predictive path-planning controller for autonomous road vehicles, IEEE Transactions on Intelligent Transportation, 18(5), 2017, 1255–1267.
  27. [27] J. Ni and S. Yang, Bioinspired neural network for real-time cooperative hunting by multirobots in unknown environments, IEEE Transactions on Neural Networks, 22(12), 2011, 2062– 2077.
  28. [28] J. Ni, L. Yang, L. Wu, and X. Fan, An improved spinal neural system-based approach for heterogeneous AUVs cooperative hunting, International Journal of Fuzzy Systems, 20(2), 2018, 672–686.
  29. [29] X. Cao, H. Sun, and G. Jan, Multi-AUV cooperative target search and tracking in unknown underwater environment, Ocean Engineering, 150, 2018, 1–11,
  30. [30] B. Nguyen and D. Hopkin, Modeling autonomous underwater vehicle (AUV) operations in mine hunting, Proc. OCEANS, Brest, France, 2005, 533–538.
  31. [31] D. Williams, On optimal AUV track-spacing for underwater mine detection, Proc. IEEE Int. Conf. Robot. Autom., Anchorage, AK, USA, 2010, 4755–4762.
  32. [32] Z. Huang, D. Zhu, and B. Sun, A multi-AUV cooperative hunting method in 3-D underwater environment with obstacle, Engineering Applications of Artificial Intelligence, 50, 2016, 192–200.
  33. [33] X. Cao, D. Zhu, and S. Yang, Multi-AUV target searching under ocean current based on PPSO and velocity synthesis algorithm, Underwater Technology, 33(1), 2015, 31–39.
  34. [34] X. Cao, Z. Huang, and D. Zhu, AUV cooperative hunting algorithm based on bio-inspired neural network for path conflict state, Proc. IEEE Int. Conf. Inf. Autom., Lijiang, China, 2015, 1821–1826.
  35. [35] J. Chen, W. Zha, Z. Peng, et al., Multi-player pursuit–evasion games with one superior evader, Automatica, 71, 2016, 24–32.
  36. [36] M. Chen and D. Zhu, Multi-AUV cooperative hunting control with improved Glasius bio-inspired neural network, Journal of Navigation, 71, 2018, 1–18.
  37. [37] R. Nair, L. Behera, and S. Kumar, Event-triggered finite-time integral sliding mode controller for consensus-based formation of multirobot systems with disturbances, IEEE Transactions on Control Systems Technology, 27(1), 2019, 39–47.
  38. [38] P. Lanillos, S. Gan, E. Besada-Portas, et al., Multi-UAV target search using decentralized gradient-based negotiation with expected observation, Information Sciences, 282, 2014, 92–110.
  39. [39] A. Marjovi and L. Marques, Multi-robot olfactory search in structured environments, Robotics and Autonomous Systems, 59(11), 2011, 867–881.
  40. [40] S. Jeong, O. Simeone, and J. Kang, Mobile edge computing via a UAV-mounted cloudlet: optimization of bit allocation and path planning, IEEE Transactions on Vehicular Technology, 67(3), 2018, 2049–2063.
  41. [41] Z. Zhou, et al., Efficient path planning algorithms in reach-avoid problems, Automatica, 89, 2018, 28–36.
  42. [42] Z. Cao, N. Gu, M. Tan, et al., Multi-robot hunting in dynamic environments, Intelligent Automation & Soft Computing, 14(1), 2008, 61–72.
  43. [43] A. Khan, B. Rinner, and A. Cavallaro, Cooperative robots to observe moving targets, IEEE Transactions on Cybernetics, 48(1), 2018, 187–198.
  44. [44] A. Kolling, A. Kleiner, and S. Carpin, Coordinated search with multiple robots arranged in line formations, IEEE Transactions on Robotics, 34(2), 2018, 459–473.
  45. [45] C. Lin, H. Wang, J. Yuan, et al., An online path planning method based on hybrid quantum ant colony optimization for AUV, International Journal of Robotics and Automation, 33(4), 2018, 435–444.
  46. [46] D. Zhu, H. Huang, and S. Yang, Dynamic task assignment and path planning of multi-AUV system based on an improved self-organizing map and velocity synthesis method in three-dimensional underwater workspace, IEEE Transactions on Cybernetics, 43(2), 2013, 504–514.

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