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Multi-AUV collaborative navigation method based on reinforcement learning

A collaborative navigation and reinforcement learning technology, applied in navigation, surveying and mapping and navigation, navigation computing tools, etc., can solve problems such as difficult trajectories and difficult to find solutions, and achieve the effect of reducing positioning errors

Inactive Publication Date: 2021-06-25
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

However, since the multi-AUV cooperative navigation system is a complex nonlinear system, it is difficult to obtain a reasonable trajectory through theoretical calculations. For simple navigation scenarios, the trajectory of the master-slave AUV can be obtained through manual planning, but for complex scenarios, such as multi-slave AUV system, traditional methods are difficult to find effective solutions

Method used

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  • Multi-AUV collaborative navigation method based on reinforcement learning
  • Multi-AUV collaborative navigation method based on reinforcement learning
  • Multi-AUV collaborative navigation method based on reinforcement learning

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specific Embodiment

[0094] This embodiment is a single-master-three-slave cooperative navigation system. The three slave AUVs start from points (-150,0), (0,0) and (150,0) to move in a straight line at a uniform speed, and the initial heading angles are π / 2. The sailing speed is 1.5m / s. The simulation time is 4000s, and the navigation speed of the main AUV is 2.5m / s.

[0095] The state quantities in the selected state set are relative azimuth and relative distance, and then the state quantities are discretized to form a state set, as shown in the following table:

[0096] Table 1 Discretization parameters of state quantity

[0097]

[0098] The action set selects the heading angular velocity of the main AUV, and sets the maximum heading angular velocity of the main AUV to 0.08rad / s. The action set is selected as follows:

[0099] A=[-0.08,-0.05,-0.03,0.00,-0.03,-0.05,0.08]

[0100] The maximum number of learning times is set to 1000, and the relevant parameters of the training and learning p...

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Abstract

The invention provides a multi-AUV (Autonomous Underwater Vehicle) collaborative navigation method based on reinforcement learning. According to the method, a collaborative navigation process is divided into two main processes: firstly, a trajectory planning process: a trajectory of a slave AUV in a cluster is obtained through manual planning according to a task to be executed, and a trajectory of a master AUV is obtained through a trajectory planning method based on hierarchical Q learning; and then, a navigation calculation process: a proper nonlinear filtering algorithm is selected to carry out actual navigation calculation. Test verification shows that after the master AUV sails according to the trajectory planned by the method and then the slave AUV performs navigation calculation by using nonlinear filtering, the positioning error of the slave AUV can be obviously reduced.

Description

technical field [0001] The invention relates to a multi-AUV cooperative navigation method based on reinforcement learning, which belongs to the technical field of underwater vehicle navigation. Background technique [0002] Autonomous Underwater Vehicle (AUV) is the most widely used form of underwater mobile robot. Underwater autonomous navigation carrier for mine hunting and combat. The necessary condition for it to function underwater and perform tasks is to be able to accurately determine its own position, so navigation is one of the key technologies of AUV. Different from land and air navigation methods, due to the lack of GPS support underwater, dead reckoning, inertial navigation, Doppler velocimetry, acoustic positioning and geophysical navigation are widely used in AUV navigation. However, these navigation methods have their own limitations. [0003] Multi-AUV cooperative navigation technology, especially the master-slave cooperative navigation technology is a cur...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01C21/00G01C21/16G01C21/20G06F17/15
CPCG01C21/005G01C21/165G01C21/20G06F17/15
Inventor 张立川武东伟任染臻邢润发
Owner NORTHWESTERN POLYTECHNICAL UNIV
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