Optimal search path planning for unmanned surface vehicle based on an improved genetic algorithm. (October 2019)

• Record Type: Journal Article
• Authors: Guo, Hui; Mao, Zhaoyong; Ding, Wenjun; Liu, Peiliang
• Is Part Of: Computers & electrical engineering. Volume 79(2019)
• Publication Date: 2019-10
• Subjects: Unmanned surface vehicle (USV) -- Cumulative detection probability -- Improved genetic algorithm -- Adaptive mutation; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2019.106467
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: A planning model for simultaneously optimizing the unmanned surface vehicle's (USV's) direction and speed is established for searching submarines. The USV detection model is achieved through the underwater sonar search principle. An improved genetic algorithm is employed for maximizing cumulative detection probability (CDP), which uses three control factors to control the direction and amplitude of mutation adaptively and improve the convergence speed. In the simulation, the escaping target is assumed unknown direction, and many reasonable and efficient search paths are obtained. The analysis results of the evolutionary curve show that the proposed algorithm has the advantages of strong stability and fast convergence and is suitable for USV search problem.