Cluster area coverage method based on Deep Q-Learning

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...

AI Technical Summary

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, improving the efficiency of the area coverage algorithm and maximizing the search area in the shortest time is multi-intelligence. An important research direction of population swarm search control

Images