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Method for cooperative search and dynamic task allocation of unmanned aerial vehicle teams under uncertain environment

An unmanned aerial vehicle and dynamic task technology, applied in the field of flight formation guidance and control, can solve problems such as increasing the number of unmanned aerial vehicles to be discovered, and achieve the effect of shortening computing time, reducing computing load, and ensuring real-time performance.

Active Publication Date: 2013-01-30
BEIHANG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that in order to achieve optimality, multiple rounds of communication between UAVs representing each Agent are required. Frequent communication in enemy areas will increase the probability of UAVs being discovered.

Method used

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  • Method for cooperative search and dynamic task allocation of unmanned aerial vehicle teams under uncertain environment
  • Method for cooperative search and dynamic task allocation of unmanned aerial vehicle teams under uncertain environment
  • Method for cooperative search and dynamic task allocation of unmanned aerial vehicle teams under uncertain environment

Examples

Experimental program
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Effect test

Embodiment 1

[0096] The initial state of 5 drones performing 6 tasks is as follows: Figure 5 As shown, among them, the detection radius of each UAV is 800 meters, and the communication radius is 1600 meters. For clarity, it is not marked in the figure. When the UAV is in the search mode, the guidance law guides the UAV to make the center of the circle at ( 2100, 2025), a circular motion with a radius of 800 meters, which is a typical situation where the number of drones performing missions is smaller than the number of missions. The initial information of the 5 drones is shown in the table below:

[0097]

[0098] The initial information of the 6 mission points is shown in the table below:

[0099]

[0100]

[0101] When the existing method is used for UAV task assignment, the UAV only selects the task with the largest advantage function within its own detection range, and there is no information interaction between UAVs. 5 UAVs perform 6 tasks. Trajectory such as Figure 6 Sh...

Embodiment 2

[0104] When adopting the method of the present invention to carry out 8 unmanned aerial vehicles to carry out 10 tasks, the initial state is as follows Figure 8 As shown, the detection radius of each UAV is 800 meters, and the communication radius is 1600 meters. For clarity, it is not marked in the figure. When the UAV is in the search mode, the guidance law guides the UAV to make the center of the circle at (2100, 2025), a circular motion with a radius of 800 meters. Among them, the initial information of 8 UAVs is shown in the following table:

[0105]

[0106]

[0107] The initial information of the 10 mission points is shown in the table below:

[0108]

[0109] When using the existing method for UAV task assignment, the UAV only selects the task with the largest advantage function within its own detection range, and there is no information interaction between UAVs. When 8 UAVs perform 10 target tasks, each has no Human-machine trajectory such as Figure 9 Sh...

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Abstract

The invention relates to a method for cooperative search and dynamic task allocation of unmanned aerial vehicle teams under an uncertain environment, and not only puts forward a cooperative dynamic task allocation strategy of unmanned aerial vehicle teams, but also designs a concrete guidance law. By adopting an arc air route as the air route by which unmanned aerial vehicles performing tasks, the method includes: step 1. determining the data structure of single unmanned aerial vehicle maintenance; step 2. determining the flight mode of unmanned aerial vehicles; step 3. determining the dominant function of task performing of the unmanned aerial vehicles; step 4. determining a dynamic task allocation process; and step 5. determining the guidance law of aerial vehicle search and task performing. Compared with modern optimization algorithm based task allocation methods, the method provided in the invention reduces the computation load of single unmanned aerial vehicle, and is suitable for conditions characterized by strong real-time performance and uncertain environment. Compared with market mechanism auction algorithm based task allocation methods, the method reduces the times of communication performed among unmanned aerial vehicles and the computation load of single unmanned aerial vehicle, and guarantees the real-time performance of broadcast unmanned aerial vehicles during task performing.

Description

technical field [0001] The invention relates to an unmanned aerial vehicle formation cooperative dynamic task allocation strategy in an uncertain environment, belongs to the technical field of flight formation guidance and control, and specifically relates to a method for unmanned aerial vehicle formation cooperative search and dynamic task allocation in an uncertain environment. Background technique [0002] At present, as many as 30 countries have invested a lot of manpower and financial resources in the research and production of drones. After two decades of development, this technology has become relatively mature and has played a role in various military and civilian fields. However, there are some problems when a single UAV performs tasks. For example, a single UAV may be affected by the number of sensors. Due to limitations, the target area cannot be observed in all directions from multiple angles. When faced with a large-scale search task, it cannot effectively cover...

Claims

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Application Information

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IPC IPC(8): G01C21/00G01C21/20
Inventor 吴森堂孙健胡楠希杜阳
Owner BEIHANG UNIV
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