Multi-agent centralized region coverage method and system based on global environment prediction

A technology of global environment and regional coverage, applied in wireless communication, electrical components, network planning, etc., can solve problems such as no effective solutions.

Active Publication Date: 2021-09-14
SOUTHEAST UNIV
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Problems solved by technology

To solve this problem, the multi-agent system needs to quickly complete the task of predicting the unknown environment density functi...

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  • Multi-agent centralized region coverage method and system based on global environment prediction
  • Multi-agent centralized region coverage method and system based on global environment prediction
  • Multi-agent centralized region coverage method and system based on global environment prediction

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Embodiment Construction

[0066] The purpose, technical solution and advantages of the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0067] Existing multi-agent system task area coverage control methods mainly adopt the distributed coverage control method based on Voronoi partition. The current methods of this kind are almost all based on the assumption that the task environment density function is known, which is obviously unreasonable, so there are certain problems in the practicality of this kind of coverage control method.

[0068] Based on the above considerations, the embodiment of the present invention discloses a multi-agent centralized area coverage method based on global environment prediction. The base station receives the coordinates of the respective agents and the collected environmental data, and predicts the global environment density based on a sparse approximate Gaussian process regression method. ...

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Abstract

The invention discloses a multi-agent centralized region coverage method and system based on global environment prediction. In the method, base stations receive respective main body positions and environment data, predict a global environment density function based on a sparse approximate Gaussian process regression method, and give a global prediction variance; the base station designs an autologous control scheme decision model based on the global prediction variance, assigns tasks to respective subjects through the decision model, designs a finite step prediction priority controller based on the global prediction variance by taking a decision index as a predicted autologous, designing a finite step length coverage priority controller based on an optimal region coverage Lloyd algorithm; and realizing the optimal coverage control through continuous interaction of the self-agent and the base station. According to the method, the base station-multi-autologous system can autonomously capture the environment density function model with high accuracy and high performance. Meanwhile, the multi-autologous system can quickly realize ideal optimal region coverage under the limitation of finite step length.

Description

technical field [0001] The invention relates to a multi-agent centralized area coverage method and system based on global environment prediction, and belongs to the field of multi-agent cooperative coverage and the technical field of multi-agent intelligent task execution. Background technique [0002] Since the beginning of the new century, with the rapid development of networking communication and computer technology, multi-agent cluster technology represented by drone cluster technology has also been greatly developed. Compared with a single autonomous system, the multi-autonomous system has stronger adaptability to the environment, has better robustness, and can cooperate to complete more complex tasks. and other fields have a wide range of applications. Considering the development status of multi-agent systems comprehensively, information collection and environmental monitoring are one of the application fields where all kinds of multi-agent systems can give full play ...

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Application Information

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IPC IPC(8): H04W16/18
CPCH04W16/18
Inventor 付俊杰唐美祺温广辉
Owner SOUTHEAST UNIV
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