Cluster area coverage method based on Deep Q-Learning

A technology of area coverage and clustering, applied in neural learning methods, two-dimensional position/channel control, biological neural network models, etc., can solve the problems of repeated coverage reducing algorithm operation efficiency, lack of effective use of historical coverage information, etc., to improve Coverage efficiency, effect of ensuring stability

Pending Publication Date: 2022-04-12
YANGTZE DELTA REGION INST (QUZHOU) UNIV OF ELECTRONIC SCI & TECH OF CHINA
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Problems solved by technology

The current cluster area coverage method lacks the effective use of historical coverage information. The repeated coverage problem greatly reduces the operating efficiency of the algorithm. Therefore

Method used

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  • Cluster area coverage method based on Deep Q-Learning
  • Cluster area coverage method based on Deep Q-Learning
  • Cluster area coverage method based on Deep Q-Learning

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Embodiment Construction

[0132] The technical solution of the present invention will be further described in detail below in conjunction with the accompanying drawings, but the protection scope of the present invention is not limited to the following description.

[0133] Such as figure 1 As shown, a Deep Q-Learning-based cluster area coverage method includes the following steps:

[0134] A method for cluster area coverage based on Deep Q-Learning, comprising the following steps:

[0135] Step S1, establish the dynamic model of the cluster system, the cluster V contains n agents, v={1, 2...,n}, the i-th agent in the cluster is defined as agent i, its second-order dynamics The model is defined as follows:

[0136]

[0137] where p i is the position of agent i, v i is the speed of agent i, u i is the acceleration of agent i, n is the total number of agents in the cluster, and means p i , v i Derivation relative to time;

[0138] Step S2. Determine the neighbor set of the agent in the clus...

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Abstract

The invention discloses a Deep Q-Learning-based cluster area coverage method. The method comprises the following steps of establishing a dynamic model of a cluster system; determining a neighbor set of agents in the cluster; establishing a motion control model of the cluster system; constructing an information map, and encoding the information map; defining a state space, a behavior space and a return function required by reinforcement learning according to the information map; a network model required by a Deep Q-Learning algorithm is designed; a Deep Q-Learning area coverage algorithm under the free area is designed; and adjusting the obtained points as required to obtain a Deep Q-Learning area coverage algorithm under the obstacle area. According to the method, training and learning of a cluster area coverage control algorithm are realized by means of a Deep Q-Learning technology, cluster area coverage in a free area and an obstacle area is realized, the cluster area coverage efficiency is effectively improved, and meanwhile, the stability of the algorithm in a weak communication environment can be ensured.

Description

technical field [0001] The patent of the present invention belongs to the field of multi-agent cluster and Q-Learning, and in particular relates to a cluster area coverage method based on Deep Q-Learning. Background technique [0002] People's idea of ​​multi-agent swarms comes from the observation and research of animal swarm movement in nature. For example, sharks can drive fish to the near sea surface for predation, and geese maintain a specific formation during migration to reduce air pollution. The resistance, etc., is a kind of bionics research. With the rise of artificial intelligence technology in recent years, the intelligent control of robots, drones, unmanned vehicles, etc. has become a hot research field, and significant progress has been made. [0003] Cluster area coverage has very important application and scientific research value, such as exploration of unknown areas, monitoring of target areas, etc. The current cluster area coverage method lacks the effec...

Claims

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Application Information

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IPC IPC(8): G05D1/02G06N3/04G06N3/08
CPCY02D30/70
Inventor 袁国慧王卓然肖剑何劲辉
Owner YANGTZE DELTA REGION INST (QUZHOU) UNIV OF ELECTRONIC SCI & TECH OF CHINA
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