@inproceedings{724d54f70f874e058a80b3985170cf27,
title = "An Improved Genetic Algorithm for Optimal Search Path of Unmanned Underwater Vehicles",
abstract = "To solve path planning problem of continuous space-time Markov moving targets for UUV search, an optimal path planning model is established. The search direction of the UUV is set as decision variables. An improved genetic algorithm is adopted to pursue an optimal path for underwater anti-submarine search. The algorithm utilizes an improved real number encoding method to describe the path. The target{\textquoteright}s motion is assumed as uniform distribution in direction and normal distribution in velocity around an initial speed. The results show that the search path planning is more reasonable through a certain number of genetic and cross mutation operations. The proposed method has the advantages of high search efficiency, good stability and short reaction period, and is suitable for solving underwater path-searching problems.",
keywords = "Improved genetic algorithm, Markov moving target, Optimal search path problem (OSPP), Unmanned underwater vehicle (UUV)",
author = "Zhaoyong Mao and Peiliang Liu and Wenjun Ding and Guo Hui",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 12th International Conference on Intelligent Robotics and Applications, ICIRA 2019 ; Conference date: 08-08-2019 Through 11-08-2019",
year = "2019",
doi = "10.1007/978-3-030-27532-7_43",
language = "英语",
isbn = "9783030275310",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "480--488",
editor = "Haibin Yu and Jinguo Liu and Lianqing Liu and Yuwang Liu and Zhaojie Ju and Dalin Zhou",
booktitle = "Intelligent Robotics and Applications - 12th International Conference, ICIRA 2019, Proceedings",
}