Unmanned surface vehicle path planning method based on adaptive Cuckoo search algorithm

Abstract

The present invention discloses an unmanned surface vehicle path planning method based on an adaptive Cuckoo search algorithm, belonging to the technical field of unmanned surface vehicle path planning. The method comprises the steps of: constructing a USV (Unmanned Surface Vehicle) planning path, and initializing parameters; performing iteration of each bird nest position in free grids, and selecting an initial global optimum bird nest position; performing updating of other bird nests one by one to obtain a new set of bird nest positions, employing an estimation function to test the path length of each bird nest one by one, and respectively computing probability of bird eggs of modern cuckoos of each bird nest being discovered by one nest host bird; selecting bird nest positions one by one, randomly generating random numbers following uniformed distribution, if the random numbers are smaller than the probabilities, remaining a current final bird nest position of the current bird nest,combining the current final bird nest position with the global optimum bird nest position to reselect a (t+1)th generation global optimum position, performing repeated iteration until an optimal pathis output. The unmanned surface vehicle path planning method based on an adaptive Cuckoo search algorithm can remain a high local fine research capacity, can improve the problem of slow rate of convergence and can meet the demand in the USV working process.

Description

technical field [0001] The invention belongs to the technical field of surface unmanned boat path planning, and in particular relates to a water surface unmanned boat path planning method based on an adaptive cuckoo search algorithm. Background technique [0002] Unmanned surface vehicle (Unmanned Surface Vehicle, USV) is recognized as one of the important means of performing complex military and civil missions by virtue of its advantages of modularization, unmanned, flexible maneuverability, small size and intelligence. Non-combat fields all play an important role. Intelligence is an important development direction of USV. Autonomous trajectory planning technology is the basis of its motion control and trajectory tracking, which reflects the level of intelligence of USV and has very important research significance. [0003] Path planning algorithms mainly include A* algorithm, artificial potential field method, fuzzy logic algorithm, neural network algorithm, genetic algor...

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Problems solved by technology

However, the cuckoo search algorithm, like some other swarm intelligence algorithms, also has the disadvantages of slow search speed and low precision in the later stage. When using it for USV path planning, it needs to be further improved for these problems; and there is no literature to describe the cuckoo search algorithm Applied in USV path planning

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