Navigation Situation Assessment of Autonomous Surface Vehicles in a Cooperative Hunting Environment

Zhang, Wenjun; Wang, Fuqiang; Gao, Qiqiang; Qu, Xingru

doi:10.2478/pomr-2022-0013

Abstract

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.

References

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.
Open DOI Search in Google Scholar Back to article
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.
Open DOI Search in Google Scholar Back to article
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.
Open DOI Search in Google Scholar Back to article
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
Open DOI Search in Google Scholar Back to article
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.
Open DOI Search in Google Scholar Back to article
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.2020.11.025.33288222
Open DOI Search in Google Scholar Back to article
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.2018.2868978.30281490
Open DOI Search in Google Scholar Back to article
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.
Open DOI Search in Google Scholar Back to article
9. J. Chen, W. Zha, Z. Peng, and D. Gu, ‘Multi-player pursuit-evasion games with one superior evader,’ Automatica, vol. 71, 2016, doi: 10.1016/j.automatica.2016.04.012.
Open DOI Search in Google Scholar Back to article
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.2016.2546381.27071205
Open DOI Search in Google Scholar Back to article
11. S. Jin, and Z. Qu, ‘Pursuit-evasion games with multi-pursuer vs. one fast evader,’ 2010 8th World Congress on Intelligent Control and Automation, Jinan, China, 6-9 July, 2010.
Search in Google Scholar Back to article
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.
Open DOI Search in Google Scholar Back to article
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.
Open DOI Search in Google Scholar Back to article
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, 2008.10.1109/ROBOT.2008.4543783
Search in Google Scholar Back to article
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.
Open DOI Search in Google Scholar Back to article
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.
Open DOI Search in Google Scholar Back to article
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.
Open DOI Search in Google Scholar Back to article
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.
Open DOI Search in Google Scholar Back to article
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.
Open DOI Search in Google Scholar Back to article
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.
Open DOI Search in Google Scholar Back to article
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, 2014.10.1109/ARES.2014.75
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
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, 2015.10.1109/ITSC.2015.163
Search in Google Scholar Back to article
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.
Open DOI Search in Google Scholar Back to article
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.
Open DOI Search in Google Scholar Back to article
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.
Open DOI Search in Google Scholar Back to article
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.
Open DOI Search in Google Scholar Back to article

Navigation Situation Assessment of Autonomous Surface Vehicles in a Cooperative Hunting Environment

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