Abstract
For the underwater target search problem of Autonomous Underwater Vehicle (AUV), this paper proposes a search path planning method for multiple underwater targets based on GBNN. Firstly, the underwater two-dimensional environment discrete grid map is constructed. Secondly, the corresponding two-dimensional GBNN model is constructed according to the grid map. Finally, the GBNN model is used to adaptively suppress the obstacle and adaptively attract the target search area. AUV can search and detect underwater targets in close proximity according to the activity output values of neural network neurons. The simulation results show that AUV can avoid obstacles autonomously and search and detect underwater targets.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Yang, Y.: Research Status and Future Prospect of Intelligent Underwater Vehicle Technology . Electron. Prod. (04), 24–25+55 (2019)
Song, J.H., Yun, Y., Luo, L.B., Wang, X.M.: Application of artificial intelligence in the field of deep sea robots. Electronic World (06), 185–186 (2019)
Li, Y.P., Li, S., Zhang, A.Q.: Research status of autonomous/remote control underwater vehicles. Sci. Technol. Innov. Prod. 8(02), 217–222 (2016)
Huang, S., Shu, Y., Tang, S.F., Tong, Z.Y., Song, B., Tong, M.M.: A review of path planning methods for autonomous mobile robots. Softw. Guide 17(10), 1–5 (2018)
Fu, X.W., Wei, G.W., Gao, X.G.: Multi-UAV collaborative area search in uncertain environment algorithm. Syst. Eng. Electron. 38(4), 821–827 (2016)
Yang, B., Ding, Y., Jin, Y.: Self-organized swarm robot for target search and trapping inspired by bacterial chemotaxis. Rob. Auton. Syst. 72, 83–92 (2015)
Renzagli, A., Doitsidis, L., Martinelli, A.: Cognitive-based adaptive control for cooperative multi-robot Coverage. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3314–3320 (2010)
Renzagli, A., Doitsidis, L., Martinelli, A.: Multi-robot three-dimensional coverage of unknown areas. Int. J. Robot. Res. 31(6), 738–752 (2012)
Glasius, R., Komoda, A., Gielen, S.: A biologically inspired neural net for trajectory formation and obstacle avoidance. Biol. Cybern. 6(84), 511–520 (1996)
Zhu, D.Q., Sun, B., Li, L.: An AUV’s 3-D autonomous path planning and secure obstacles avoidance algorithm based on biological heuristic model. Control Decisions 30(5), 798–806 (2015)
Zhu, D.Q., Liu, Y., Sun, B., Liu, Q.Y.: Autonomous Heuristic GBNN Path Planning Algorithm for Autonomous Underwater Vehicles [J/OL]. Control Theory Appl. 04, 1–9 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhu, T., Zhu, D., Yan, M. (2019). Multiple Underwater Target Search Path Planning Based on GBNN. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science(), vol 11742. Springer, Cham. https://doi.org/10.1007/978-3-030-27535-8_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-27535-8_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-27534-1
Online ISBN: 978-3-030-27535-8
eBook Packages: Computer ScienceComputer Science (R0)