Multi-robot smell source positioning method based on fruit fly optimization algorithm

A fruit fly optimization algorithm and multi-robot technology, applied in the directions of instruments, computing, computing models, etc., can solve problems such as discrete domain optimization problems that have not yet been directly applied

Inactive Publication Date: 2017-07-14
CHINA UNIV OF MINING & TECH
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Problems solved by technology

At present, the scope of application of the fruit fly optimization algorithm is mostly in the field of parameter optim

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  • Multi-robot smell source positioning method based on fruit fly optimization algorithm

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Embodiment Construction

[0018] 1. Initialization.

[0019] 1.1 Set the initial parameters: randomly select a position in the search space Pos As the initial position of the robot group, set the size of the population size , initial concentration threshold α and the initial step value L 0 .

[0020] 1.2 Set the algorithm termination condition: the concentration threshold of the search space C lim and the maximum number of iterations G max .

[0021] 2. Plume discovery and plume tracking: The fruit fly population iteratively updates the location of the odor source according to the step value of the adaptive change.

[0022] 2.1 Calculate the adaptive step value step : The size of the adaptive step value is determined by the current group optimal concentration value Smellbest with a pre-set maximum concentration value in the environment C max The size relationship between is adaptively adjusted, which is determined by formula (1):

[0023] (1)

[0024] in L 0 For a given fixed step...

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Abstract

The invention relates to a multi-robot smell source positioning method based on a fruit fly optimization algorithm. The method is characterized in that the method comprises following steps of 1), smoke plume discovering: randomly presetting a robot group initial position Pos, dispersing robots in a diamond region with adaptive step length Step*2 by taking the initial position as the center, and determining whether to enter a smoke plume tracking stage according to the fact whether the obtained biggest concentration value is larger than a set concentration threshold value alpha; 2) smoke plume tracking: selecting a robot with the biggest smell concentration as an optimal individual Bestsmell, and if the concentration of the individual is larger than the group optimum Smellbest, updating the position to be the group optimum; and 3) smell source confirmation: using a set concentration threshold value C<lim> as an iteration termination condition, and when the optimal solution continuously found for 3 times meets a termination condition within the biggest iteration times, confirming that the task of positioning the smell source is finished. According to the invention, the fixed step length in the original fruit fly algorithm is corrected, adaptive change is performed according to the concentration and smell source positioning efficiency is effectively improved.

Description

technical field [0001] The invention relates to the research of a multi-robot odor source positioning method based on a fruit fly optimization algorithm, and belongs to the field of active olfaction of robots. Background technique [0002] Due to the frequent occurrence of damage to life and property due to the leakage of dangerous gases, many creatures in nature can judge the existence of danger and avoid danger in time by virtue of their sensitive sense of smell. Inspired by the effective use of olfactory information by organisms, some scholars have applied robots equipped with gas sensors to the research of locating dangerous chemical sources, and defined such research as robotic active olfaction. In the field of active olfactory research of robots, the source of dangerous gas leakage is defined as the odor source. [0003] Hayes defined the general odor source localization problem as the process of finding a single odor source as quickly as possible in a two-dimensional...

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Application Information

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IPC IPC(8): G06N3/00
Inventor 缪燕子卜淑萍许瑞琪金鑫李晓东周笛金慧杰许红盛
Owner CHINA UNIV OF MINING & TECH
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