Method for collaboratively searching for multi-dynamic target in unknown sea area by unmanned aerial vehicle group based on enhanced learning

Abstract

The invention discloses a method for collaboratively searching for a multi-dynamic target in an unknown sea area by an unmanned aerial vehicle group based on enhanced learning. The method comprises the following steps: S1, dividing a searched sea area by using a grid method; to be specific, establishing a territory awareness information map based on the pheromone concentration of an unmanned aerial vehicle in a certain area; S2, according to unmanned aerial vehicle state information and a decision u(k), designing a Q value table; S3, according to a Q value of the current state of the unmannedaerial vehicle group, selecting flight routes of the unmanned aerial vehicles based on a Boltzmann distribution mechanism and performing the rotues; S4, according to a search performance function, designing a punishment function for evaluating the flight state of the unmanned aerial vehicle and updating a Q value of an arrival new state of the unmanned aerial vehicle group based on the punishmentfunction; and S5, updating the arrival new state of the unmanned aerial vehicle group to be a current state, making a flight route decision continuously, completing learning of the whole Q value table, and making a decision by the unmanned aerial vehicle group based on the trained Q table to complete the search task.

Description

technical field [0001] The invention relates to the technical field of unmanned aerial vehicle control, in particular to a method for cooperatively searching multi-dynamic targets in an unknown sea area by a group of unmanned aerial vehicles through reinforcement learning. Background technique [0002] With the rapid development of technologies such as sensors, wireless communication, and intelligent control, the functions of unmanned swarm systems are increasingly enhanced, and their application fields are expanding. Due to its scalability, strong collaboration and low loss, the unmanned swarm system has attracted more and more attention from academia, industry and national defense in its collaborative theory and application research, and the multi-UAV cooperative search system can effectively improve the search Efficiency, especially for the search of dynamic targets in complex sea conditions such as uncertainty and strong interference, has great advantages. Therefore, mul...

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Problems solved by technology

[0003] The traditional search method is to use coverage search, such as back-type search, traversal search, etc. This search method generally maximizes the coverage of the task area to find as many targets as possible. In recent years, a search graph model has been established in combination with the target existence probability , using distributed model predictive control to solve, effectively reducing the solution scale of the search decision problem, but only limited to the search of static targets

For dynamic targets, the Bayesian method is used to calculate the average detection time and average detection probability, but it is only applicable to the search for a single target at sea, and cannot meet the needs of multi-target search

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