Abstract
Cooperative hunting by a multi-AUV system in unknown 3D underwater environment is not only a research hot spot but also a challenging task. To conduct this task, each AUV needs to move quickly without obstacle collisions and cooperate with other AUVs considering the overall interests. In this paper, the heterogeneous AUVs cooperative hunting problem is studied, including two main tasks, namely the search and pursuit of targets, and a novel spinal neural system-based approach is proposed. In the search stage, a partition and column parallel search strategy is used in this paper, and a search formation control algorithm based on an improved spinal neural system is proposed. The presented search algorithm not only accomplishes the search task but also maintains a stable formation without obstacle collisions. In the cooperative pursuit stage, a dynamic alliance method based on bidirectional negotiation strategy and a pursuit direction assignment method based on improved genetic algorithm are presented, which can realize the pursuit task efficiently. Finally, some simulations are conducted and the results show that the proposed approach is capable of guiding multi-AUVs to achieve the hunting tasks in unknown 3D underwater environment efficiently.













Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Abreu, N., Cruz, N., Matos, A.: Accounting for uncertainty in search operations using AUVs. In: 2017 IEEE OES International Symposium on Underwater Technology. Haeundae, Busan, Korea (2017)
Alaaeldeen, M.E.A., Duan, W.Y.: Overview on the development of autonomous underwater vehicles (AUVs). J. Ship Mech. 20(6), 768–787 (2016)
Belbachir, A., Ingrand, F., Lacroix, S.: A cooperative architecture for target localization using multiple AUVs. Intel. Serv. Robot. 5(2), 119–132 (2012)
Cao, X., Zhu, D., Yang, S.X.: Multi-AUV target search based on bioinspired neurodynamics model in 3-D underwater environments. In: IEEE Transactions on Neural Networks and Learning Systems PP(99) (2015). http://dx.doi.org/10.1109/TNNLS.2015.2482501
Cao, Z.Q., Zhang, B., Wang, S., Tan, M.: Cooperative hunting of multiple mobile robots in an unknown environment. Acta Autom. Sin. 29(4), 536–543 (2003)
Chen, W.J., Jhong, B.G., Chen, M.Y.: Design of path planning and obstacle avoidance for a wheeled mobile robot. Int. J. Fuzzy Syst. 18(6), 1080–1091 (2016)
Couillard, M., Fawcett, J., Davison, M.: Optimizing constrained search patterns for remote mine-hunting vehicles. IEEE J. Ocean. Eng. 37(1), 75–84 (2012)
Cui, R., Li, Y., Yan, W.: Mutual information-based multi-AUV path planning for scalar field sampling using multidimensional RRT\(^*\). IEEE Trans. Syst. Man Cybern. Syst. 46(7), 993–1004 (2016)
Dadgar, M., Jafari, S., Hamzeh, A.: A PSO-based multi-robot cooperation method for target searching in unknown environments. Neurocomputing 177, 62–74 (2016)
Das, B., Subudhi, B., Pati, B.B.: Co-operative control coordination of a team of underwater vehicles with communication constraints. Trans. Inst. Meas. Control 38(4), 463–481 (2016)
Eickstedt, D.P., Schmidt, H.: A low-frequency sonar for sensor-adaptive, multi-static, detection and classification of underwater targets with AUVs. In: Oceans Conference Record (IEEE), vol. 3, pp. 1440–1447. San Diego, CA, USA (2003)
Hao, L., Gu, H., Kang, F., Yang, H.: Virtual-leader based formation control with constant bearing guidance for underactuated AUVs. ICIC Express Lett. 11(1), 117–125 (2017)
Huang, H., Zhu, D., Ding, F.: Dynamic task assignment and path planning for multi-AUV system in variable ocean current environment. J. Intell. Robot. Syst. Theory Appl. 74(3–4), 999–1002 (2014)
Huang, Z., Zhu, D., Sun, B.: A multi-AUV cooperative hunting method in 3-D underwater environment with obstacle. Eng. Appl. Artif. Intell. 50, 192–200 (2016)
Jung, J., Li, J.H., Choi, H.T., Myung, H.: Localization of AUVs using visual information of underwater structures and artificial landmarks. Intel. Serv. Robot. 10(1), 67–76 (2017)
Liu, K., Liu, M., Zhang, X., Li, H.: A bio-inspired geomagnetic navigation model based on course constraint strategy under anomalies field disturbing for AUV. In: OCEANS 2016-Shanghai. Shanghai, China (2016)
Liu, M., Xu, B., Peng, X.: Cooperative path planning for multi-AUV in time-varying ocean flows. J. Syst. Eng. Electron. 27(3), 612–618 (2016)
Mataric, M.J.: Getting humanoids to move and imitate. IEEE Intell. Syst. 15(4), 18–24 (2000)
Mon, Y.J., Lin, C.M.: Supervisory recurrent fuzzy neural network guidance law design for autonomous underwater vehicle. Int. J. Fuzzy Syst. 14(1), 54–64 (2012)
Ni, J., Wu, L., Fan, X., Yang, S.X.: Bioinspired intelligent algorithm and its applications for mobile robot control: a survey. Comput. Intell. Neurosci. 2016 Article ID: 3810903 (2016)
Ni, J., Wu, L., Shi, P., Yang, S.X.: A dynamic bioinspired neural network based real-time path planning method for autonomous underwater vehicles. Computational Intelligence and Neuroscience 2017, Article ID: 9269742 (2017)
Ni, J., Yang, S.X.: Bioinspired neural network for real-time cooperative hunting by multirobots in unknown environments. IEEE Trans. Neural Netw. 22(12), 2062–2077 (2011)
Ni, J., Yang, S.X.: A fuzzy-logic based chaos GA for cooperative foraging of multi-robots in unknown environments. Int. J. Robot. Autom. 27(1), 15–30 (2012)
Phillips, A., Blake, J., Smith, B., Boyd, S., Griffiths, G.: Nature in engineering for monitoring the oceans: towards a bio-inspired flexible autonomous underwater vehicle operating in an unsteady flow. Proc. Inst. Mech. Eng. Part M J. Eng. Maritime Environ. 224(4), 267–278 (2010)
Qu, H., Xing, K., Alexander, T.: An improved genetic algorithm with co-evolutionary strategy for global path planning of multiple mobile robots. Neurocomputing 120, 509–517 (2013)
Rusu, P., Petriu, E.M., Whalen, T.E., Cornell, A.: Behavior-based neuro-fuzzy controller for mobile robot navigation. IEEE Trans. Instrum. Meas. 52(4), 1335–1340 (2003)
Siddique, N.H., Amavasai, B.P.: Bio-inspired behaviour-based control. Artif. Intell. Rev. 27(2–3 SPEC. ISS.), 131–147 (2007)
Song, Y., Li, Y., Li, C., Ma, X.: Mathematical modeling and analysis of multirobot cooperative hunting behaviors. J. Robot. 2015, Article ID: 184256 (2015)
Sun, B., Zhu, D., Yang, S.X.: A bio-inspired cascaded approach for three-dimensional tracking control of unmanned underwater vehicles. Int. J. Robot. Autom. 29(4), 349–358 (2014)
Tsalatsanis, A., Yalcin, A., Valavanis, K.P.: Dynamic task allocation in cooperative robot teams. Robotica 30(5), 721–730 (2012)
Wong, C.C., Cheng, C.T., Huang, K.H., Yang, Y.T.: Fuzzy control of humanoid robot for obstacle avoidance. Int. J. Fuzzy Syst. 10(1), 1–10 (2008)
Wu, F., Yang, R.J., Gao, Q.W.: Heuristic search for moving underwater targets based on Markov process. J. Electron. Inf. Technol. 32(5), 1088–1093 (2010)
Wu, X., Feng, Z., Zhu, J., Allen, R.: GA-based path planning for multiple AUVs. Int. J. Control 80(7), 1180–1185 (2007)
Xiang, X., Liu, C., Lapierre, L., Jouvencel, B.: Synchronized path following control of multiple homogenous underactuated AUVs. J. Syst. Sci. Complex. 25(1), 71–89 (2012)
Xiang, X., Yu, C., Niu, Z., Zhang, Q.: Subsea cable tracking by autonomous underwater vehicle with magnetic sensing guidance. Sensors (Switzerland) 16(8), Article number: 1335 (2016)
Xiang, X., Yu, C., Zhang, Q.: On intelligent risk analysis and critical decision of underwater robotic vehicle. Ocean Eng. 140, 453–465 (2017)
Xiang, X., Yu, C., Zhang, Q.: Robust fuzzy 3D path following for autonomous underwater vehicle subject to uncertainties. Comput. Oper. Res. 84, 165–177 (2017)
Xiao, G., Wang, B., Deng, Z., Fu, M., Ling, Y.: An acoustic communication time delays compensation approach for master-slave AUV cooperative navigation. IEEE Sens. J. 17(2), 504–513 (2017)
Xing, W., Zhao, Y., Karimi, H.R.: Convergence analysis on multi-AUV systems with leader-follower architecture. IEEE Access 5, 853–868 (2017)
Xu, M., Pan, Z., Lu, H., Ye, Y., Lv, P., Rhalibi, A.E.: Moving-target pursuit algorithm using improved tracking strategy. IEEE Trans. Comput. Intell. AI Games 2(1), 27–39 (2010)
Yi, X., Zhu, A., Yang, S.X., Luo, C.: A bio-inspired approach to task assignment of swarm robots in 3-D dynamic environments. IEEE Trans. Cybern. 47(4), 974–983 (2017)
Yoon, S., Qiao, C.: Cooperative search and survey using autonomous underwater vehicles (AUVs). IEEE Trans. Parallel Distrib. Syst. 22(3), 364–379 (2011)
Zhu, D., Huang, H., Yang, S.X.: 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 Trans. Cybern. 43(2), 504–514 (2013)
Zhu, D., Li, W., Yan, M., Yang, S.X.: The path planning of AUV based on D-S information fusion map building and bio-inspired neural network in unknown dynamic environment. Int. J. Adv. Robot. Syst. 11(1), 415–429 (2014)
Zhu, D., Lv, R., Cao, X., Yang, S.X.: Multi-AUV hunting algorithm based on bio-inspired neural network in unknown environments. Int. J. Adv. Robot. Syst. 12(11), 689–700 (2015)
Acknowledgements
The authors would like to thank the National Natural Science Foundation of China (61203365, 61573128), the Fundamental Research Funds for the Central Universities (2015B20114), the National Key Research Program of China (2016YFC0401606) and the Jiangsu Province Natural Science Foundation (BK2012149) for their support of this paper.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ni, J., Yang, L., Wu, L. et al. An Improved Spinal Neural System-Based Approach for Heterogeneous AUVs Cooperative Hunting. Int. J. Fuzzy Syst. 20, 672–686 (2018). https://doi.org/10.1007/s40815-017-0395-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40815-017-0395-x