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

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 ...

AI Technical Summary

Problems solved by technology

To solve this problem, the multi-agent system needs to quickly complete the task of predicting the unknown environment density function and the task of covering the task area within a limited time, which is extremely challenging and no effective solution has been seen yet.

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