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A novel optimal route planning algorithm for searching on the sea

Published online by Cambridge University Press:  20 January 2021

Y. Yang Open the ORCID record for Y. Yang [Opens in a new window] ,
Y. Mao ,
R. Xie ,
Y. Hu  and
Y. Nan
Y. Yang
Affiliation:
State Key Laboratory of Air Traffic Management System and Technology NanjingChina
Y. Mao*
Affiliation:
State Key Laboratory of Air Traffic Management System and Technology NanjingChina
R. Xie
Affiliation:
State Key Laboratory of Air Traffic Management System and Technology NanjingChina
Y. Hu
Affiliation:
State Key Laboratory of Air Traffic Management System and Technology NanjingChina
Y. Nan
Affiliation:
Nanjing University of Aeronautics and Astronautics NanjingChina
*
[email protected]
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Abstract

Emergency search and rescue on the sea is an important part of national emergency response for marine perils. Optimal route planning for maritime search and rescue is the key capability to reduce the searching time, improve the rescue efficiency, as well as guarantee the rescue target’s safety of life and property. The main scope of the searching route planning is to optimise the searching time and voyage within the constraints of missing search rate and duplicate search rate. This paper proposes an optimal algorithm for searching routes of large amphibious aircraft corresponding to its flight characteristics and sea rescue capability. This algorithm transforms the search route planning problem into a discrete programming problem and applies the route traceback indexes to satisfy the duplicate search rate and missing search rate.

Keywords

route planningcontrol and navigation technology of aircraftdynamic programminghigh-seas search and rescuelarge amphibious aircraft
Type
Research Article
Information
The Aeronautical Journal , Volume 125 , Issue 1288 , June 2021 , pp. 1064 - 1082
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Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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References

REFERENCES

Li, J., Cai, Z. and Xie, H. Design of maritime search horizontal navigation requirements for large amphibious aircraft, Sci Technol, 2014, 1, (14), pp 24–25.Google Scholar
Enric, G. and Marc, C. A survey on coverage path planning for robotics, Robot Autonom Syst, 2013, 61, (12), pp 1258–1276.Google Scholar
Oksanen, T. and Visala, A. Coverage path planning algorithms for agricultural field machines, J Field Robot, 2009, 26, (8), pp 651–668.CrossRefGoogle Scholar
Ercan, U.A., Howie, C., Alfred, A.R., Prasad, N.A. and Douglas, H. Morse decompositions for coverage tasks, Int J Robot Res, 2002, 21, (4), pp 331–334.Google Scholar
Shivashankar, V., Jain, R., Kuter, U. and Nau, D. Real-time planning for covering an initially-unknown spatial environment, Proceedings of the Twenty-Fourth International Florida Artificial Intelligence Research Society Conference, 2011.Google Scholar
Lee, T.K., Baek, S.H., Choi, Y.H. and Oh, S.Y. Smooth coverage path planning and control of mobile robots based on high-resolution grid map representation, Robot Autonom Syst, 2011, 59, (10), pp 801–812.CrossRefGoogle Scholar
Luo, C. and Yang, S. A bioinspired neural network for real-time concurrent map building and complete coverage robot navigation in unknown environments, IEEE Trans Neural Netw, 2008, 19, (7), pp 1279–1298.CrossRefGoogle Scholar
Tobias, R.S., Shuaiby, M., Naoki, U. and Oliver, S. Coverage path planning for mobile robots using genetic algorithm with energy optimization, Electronics Symposium (IES) 2016 International, 2016, pp 99–104.Google Scholar
Zheping, Y., Jingwen, H. and Juan, L. Path planning method for multi-AUVs patrol in restricted multizone area, J Unmanned Undersea Syst, 2017, 25, (3), pp 237–242.Google Scholar
Bing, A. and Rui, Y. The algorithm of helicopter maritime search auxiliary route-planning, Electron Optics Cont, 2007, 15, (7), pp 1–5.Google Scholar
Li, P and Duan, H.B. Path planning of unmanned aerial vehicle based on improved gravitational search algorithm, Sci China Tech Sci, 2012, 55, pp 2712–2719.CrossRefGoogle Scholar
Mou, C., Jian, X. and Changsheng, J. Three- dimensional path planning of UAV with improved ant algorithm, J Jilin Univ (Eng Technol Edn), 2008, 38, (04), pp 991–995.Google Scholar
Ma, X., Chen, X. and Lei, Y. The data link based A* algorithm used for UCAV path planning, Electron Opt Cont, 2009, 16, (12), pp 15–17+21.Google Scholar
Liang, X., Wang, H., Meng, G. and Chen, X. Path planning for UAV under three-dimensional real terrain in rescue mission, J Beijing Univ Aeronaut Astronaut, 2015, 41, (07), pp 1183–1187.Google Scholar
Liang, X., Wang, H., Li, D. and Lv, W. Three-dimensional path planning for unmanned aerial vehicles based on principles of stream avoiding obstacles, Chin J Aeronaut, 2013, 34, (07), pp 1670–1681.Google Scholar
Wei, R., Xu, Z., Wang, S., Lv, M. Self-optimization A-star algorithm for UAV path planning based on Laguerre diagram, Syst Eng Electron, 2015, 37, (03), pp 577–582.Google Scholar
Liu, Z., Gao, X., Fu, X. and Xi, W. Three-dimensional path tracking guidance and control for unmanned aerial vehicle based on back-stepping and nonlinear dynamic inversion, Acta Armamentarii, 2014, 35, (12), pp 2030–2040.Google Scholar
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