Navigation situation assessment of autonomous surface vehicles in a cooperative hunting environment

• Autorzy: Zhang Wenjun , Wang Fuqiang , Gao Qiqiang , Qu Xingru

Identyfikatory

DOI

10.2478/pomr-2022-0013

• Języki publikacji: EN

Abstrakty

EN

This paper proposes a navigation situation assessment method for autonomous surface vehicles (ASVs) in a cooperative hunting environment. By virtue of the repulsion function expressed in the artificial potential field, the navigation situation of hunting ASVs and target ASVs is firstly described. And the hunting situation is also constructed to describe the cooperative hunting. Based on the navigation situation and the hunting situation, a navigation situation assessment method for cooperative hunting of multiple ASVs is designed, where the number of hunting vehicles and the hunting radius can be successfully computed. Simulation results show that this proposed situation assessment method can give an optimised formation pattern and provide an effective reference for cooperative hunting of ASVs.

Słowa kluczowe

EN

autonomous surface vehicles; navigation situation; situation assessment; cooperative hunting

• Wydawca: Gdańsk University of Technology, Faculty of Ocean Engineering & Ship Technology
• Czasopismo: Polish Maritime Research
• Rocznik: 2022
• Tom: nr 2
• Strony: 19--26
• Opis fizyczny: Bibliogr. 27 poz., rys., tab.

Twórcy

autor

Zhang Wenjun

• Navigation College Dalian Maritime University Dalian, 116026 China

autor

Wang Fuqiang

• School of Marine Electrical Engineering Dalian Maritime University Dalian, 116026 China

autor

Gao Qiqiang

• School of Marine Electrical Engineering Dalian Maritime University Dalian, 116026 China

autor

Qu Xingru

• College of Mechanical and Electronic Engineering Dalian Minzu University Dalian, 116600 China

Bibliografia

• 1. Z. Dong, Y. Liu, H. Wang, and T. Qin, ‘Method of cooperative formation control for underactuated USVS based on nonlinear backstepping and cascade system theory,’ Polish Maritime Research, vol. 28, no. 1, 2021, doi: 10.2478/pomr-2021-0014.
• 2. X. Liang, X. Qu, N. Wang, R. Zhang, and Y. Li, ‘Three-dimensional trajectory tracking of an underactuated AUV based on fuzzy dynamic surface control,’ IET Intelligent Transport Systems, vol. 14, no. 5, 2020, doi: 10.1049/ iet-its.2019.0347.
• 3. X. Liang, X. Qu, Y. Hou, Y. Li, and R. Zhang, ‘Distributed coordinated tracking control of multiple unmanned surface vehicles under complex marine environments,’ Ocean Engineering, vol. 205, 2020, doi: 10.1016/j. oceaneng.2020.107328.
• 4. K. Do, ‘Formation control of underactuated ships with elliptical shape approximation and limited communication ranges,’ Automatica, vol. 48, no. 7, 2012, doi: 10.1016/j. automatica.2011.11.013
• 5. X. Liang, X. Qu, Y. Hou, Y. Li, and R. Zhang, ‘Finite-time unknown observer based coordinated path-following control of unmanned underwater vehicles,’ Journal of the Franklin Institute, vol. 358, no. 5, 2021, doi: 10.1016/j. jfranklin.2021.01.028.
• 6. X. Liang, X. Qu, N. Wang, and Y. Li, ‘Swarm velocity guidance based distributed finite-time coordinated path-following for uncertain under-actuated autonomous surface vehicles,’ ISA Transactions, vol. 112, 2021, doi: 10.1016/j.isatra.2020.11.025.
• 7. L. Liu, D. Wang, Z. Peng, C. Chen, and T. Li, ‘Bounded neural network control for target tracking of underactuated autonomous surface vehicles in the presence of uncertain target dynamics,’ IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 4, 2019, doi: 10.1109/ TNNLS.2018.2868978.
• 8. J. Lewin, and J. Breakwell, ‘The surveillance-evasion game of degree,’ Journal of Optimization Theory and Applications, vol. 16, 1975, doi: 10.1007/BF01262940.
• 9. J. Chen, W. Zha, Z. Peng, and D. Gu, ‘Multi-player pursuitevasion games with one superior evader,’ Automatica, vol. 71, 2016, doi: 10.1016/j.automatica.2016.04.012.
• 10. W. Zha, J. Chen, Z. Peng, and D. Gu, ‘Construction of barrier in a fishing game with point capture,’ IEEE transactions on cybernetics, vol. 47, no. 6, 2017, doi: 10.1109/ TCYB.2016.2546381.
• 11. S. Jin, and Z. Qu, ‘Pursuit-evasion games with multipursuer vs. one fast evader,’ 2010 8th World Congress on Intelligent Control and Automation, Jinan, China, 6-9 July, 2010.
• 12. M. Awheda, and H. Schwartz, ‘A decentralized fuzzy learning algorithm for pursuit-evasion differential games with superior evaders,’ Journal of Intelligent & Robotic Systems, vol. 83, no. 1, 2016, doi: 10.1007/s10846-015-0315-y.
• 13. B. Steven, N. Wasif, and F. Stuart, ‘Improved APF strategies for dual-arm local motion planning,’ Transactions of the Institute of Measurement and Control, vol. 37, no. 1, 2015, doi: 10.1177/0142331214532002.
• 14. M. Wolf, and J. Burdick, ‘Artificial potential functions for highway driving with collision avoidance,’ 2008 IEEE International Conference on Robotics and Automation, Pasadena, CA, USA, 19-23 May, 2008.
• 15. L. Song, H. Chen, W. Xiong, Z. Dong, P. Mao, Z. Xiang, and K. Hu, ‘Method of emergency collision avoidance for unmanned surface vehicle (USV) based on motion ability database,’ Polish Maritime Research, vol. 26, no. 2, 2019, doi: 10.2478/pomr-2019-0025.
• 16. J. Wang, J. Wu, and Y. Li, ‘The driving safety field based on driver-vehicle-road interactions,’ IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 4, 2015, doi: 10.1109/TITS.2015.2401837.
• 17. J. Wang, J. Wu, X. Zheng, D. Ni, and K. Li, ‘Driving safety field theory modeling and its application in pre-collision warning system,’ Transportation Research Part C: Emerging Technologies, vol. 72, 2016, doi: 10.1016/j.trc.2016.10.003.
• 18. M. Li, X. Song, H. Cao, J. Wang, Y. Huang, C. Hu, and H. Wang, ‘Shared control with a novel dynamic authority allocation strategy based on game theory and driving safety field,’ Mechanical Systems and Signal Processing, vol. 124, 2019, doi: 10.1016/j.ymssp.2019.01.040.
• 19. H. Lyu, and Y. Yin, ‘COLREGS-constrained real-time path planning for autonomous ships using modified artificial potential fields,’ The Journal of Navigation, vol. 72, no. 3, 2019, doi: 10.1017/S0373463318000796.
• 20. K. Zheng, Y. Chen, Y. Jiang, and S. Qiao, ‘A SVM based ship collision risk assessment algorithm,’ Ocean Engineering, vol. 202, 2020, doi: 10.1016/j.oceaneng.2020.107062.
• 21. I. Kotenko, and E. Novikova, ‘Visualization of security metrics for cyber situation awareness,’ 2014 Ninth International Conference on Availability, Reliability and Security, Fribourg, Switzerland, 8-12 September, 2014.
• 22. L. Chen, M. Cao, and L. Tian, ‘Situation assessment approach based on a hierarchic multi-timescale Bayesian network,’ 2015 2nd International Conference on Information Science and Control Engineering, Shanghai, China, 24-26 April, 2015.
• 23. S. Ulbrich and M. Maurer, ‘Situation assessment in tactical lane change behaviour planning for automated vehicles,’ 2015 IEEE 18th International Conference on Intelligent Transportation Systems, Gran Canaria, Spain, 15-18 September, 2015.
• 24. D. Kong, H. Li, and H. Dong, ‘Research on network security situation assessment technology based on fuzzy evaluation method,’ Journal of Physics: Conference Series, vol.1883, 2021, doi: 10.1088/1742-6596/1883/1/012108.
• 25. L. Zhang, Y. Zhu, X. Shi, and X. Li, ‘A situation assessment method with an improved fuzzy deep neural network for multiple UAVs,’ Information (Switzerland), vol. 11, no. 4, 2020, doi: 10.3390/info11040194.
• 26. X. Song, H. Cao, and J. Huang, ‘Vehicle path planning in various driving situations based on the elastic band theory for highway collision avoidance,’ Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, vol. 227, no. 12, 2013, doi: 10.1177/0954407013481299.
• 27. J. Ji, A. Khajepour, W. Melek, and Y. Huang, ‘Path planning and tracking for vehicle collision avoidance based on model predictive control with multiconstraints,’ IEEE Transactions on Vehicular Technology, vol. 66, no. 2, 2017, doi: 10.1109/TVT.2016.2555853.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu „Społeczna odpowiedzialność nauki” - moduł: Popularyzacja nauki i promocja sportu (2022-2023).