Optimal search path planning for unmanned surface vehicle based on an improved genetic algorithm

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.