Multi-unmanned aerial vehicle collaborative searching method based on improved pigeon flock optimization

A multi-UAV, search method technology, applied in artificial life, two-dimensional position/channel control, instruments, etc., can solve the problems of low static efficiency of search targets, repeated searches, etc.

Active Publication Date: 2019-08-20
NANJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

[0005] Purpose of the invention: In order to overcome the problems of repeated searches, static search targets and low efficiency in multi-UAV cooperative search in the prior art, the present invention provides a multi-UAV cooperative search method based on improved pigeon group optimization, which ...

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  • Multi-unmanned aerial vehicle collaborative searching method based on improved pigeon flock optimization
  • Multi-unmanned aerial vehicle collaborative searching method based on improved pigeon flock optimization
  • Multi-unmanned aerial vehicle collaborative searching method based on improved pigeon flock optimization

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Embodiment

[0095] first figure 2 is the modeling of the search environment, the search area L is divided into 10*10 grids, image 3 For the flight model of the UAV, due to the limitation of the minimum turning diameter, it can only reach the front, left and right directions of the flight direction, and the Markov model is used to establish the dynamic target.

[0096] Then start the multi-UAV search, set the total number of pigeons N to 50, and the number of iterations of the compass operator to be N c 1 is 15, the number of iterations of the landmark operator is N c 2 is 5. In the iterative stage of the compass operator, the initial individual position and velocity are calculated first, and the historical optimal position and the global optimal position of the individual are recorded. If it is close to K 1 = The value of the global optimal fitness function in 3 iterations is less than the threshold e 1 =0.1, then carry out Cauchy mutation, where Cauchy distribution probability dens...

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Abstract

The invention discloses a multi-unmanned aerial vehicle collaborative searching method based on improved pigeon flock optimization. The method comprises the following steps: firstly establishing a mapmodel, and establishing a target information map by using a Markov model, and then establishing a motion model and a digital pheromone map of the unmanned aerial vehicle; performing the collaborativesearching of multi-unmanned aerial vehicle by applying the pigeon flock optimization algorithm, and realizing by using chaotic and reverse strategies at an initial population location, thereby determining the randomness of the initial location; when performing the iteration of the map and the compass operator at the first stage, preventing from falling in local optimum by using Cauchy mutation; when performing the iteration of a landmark operator, representing the bad individual and Gaussian variation by using simulated annealing reservation part, thereby preventing premature from falling inlocal optimum. The chaotic and reverse strategies are used for initializing the population location, the variation and simulated annealing algorithm imported for preventing local optimum are combined,thereby solving the dynamic target and repeated searching problem in the searching process, and the searching efficiency is effectively improved at the same time.

Description

technical field [0001] The invention relates to a multi-unmanned aerial vehicle cooperative search method based on improved pigeon group optimization, which belongs to the fields of collaborative search and swarm intelligence optimization. Background technique [0002] Since the 1990s, algorithms obtained by simulating and revealing certain natural phenomena or processes have been developed, such as particle swarm algorithm, ant colony algorithm, and pigeon colony algorithm mentioned in this article. These algorithms have unique advantages and mechanisms, and provide new ideas and means for solving complex problems, and are widely used in many fields. These algorithms are called swarm intelligence optimization algorithms because of their intuition and natural mechanism. [0003] The group in Swarm Intelligence is a group of subjects (Agents) that can communicate directly or indirectly with each other, and these subjects can solve distributed problems through cooperation. S...

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Application Information

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IPC IPC(8): G05D1/02G06N3/00
CPCG05D1/0223G06N3/006G05D1/0221G05D1/0276
Inventor 陈志袁广进岳文静汪皓平狄小娟董阳
Owner NANJING UNIV OF POSTS & TELECOMM
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