A Path Planning Method for Unmanned Surface Vehicle Based on Adaptive Cuckoo Search Algorithm

Abstract

The invention discloses a path planning method for a surface unmanned boat based on an adaptive cuckoo search algorithm, and belongs to the technical field of path planning for a water surface unmanned boat. First, construct the USV planning path and initialize the parameters; each bird nest position starts to iterate in the free grid, and selects the initial global optimal bird nest position. Update the rest of the nests one by one to get a new set of nest positions, test the path length of each nest one by one with the evaluation function, and calculate the probability that the eggs of contemporary cuckoos in each nest will be found by the nest owner bird; then Select the bird’s nest position one by one, randomly generate a random number that obeys the uniform distribution, if it is less than the discovery probability, keep the current bird’s nest as the final bird’s nest position, combine it with the initial global optimal bird’s nest position, and re-select the t+1th Generation of the global optimal bird nest position. Repeat iterations until the optimal path is output. The invention maintains a strong local fine search ability, improves the problem of slow convergence speed, and meets the requirements in the USV operation 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...

AI Technical Summary

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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