Distribution method for aerial area reconnaissance tasks of unmanned aerial vehicle cluster

A task assignment, UAV technology, applied in non-electric variable control, instruments, control/regulation systems, etc., can solve the problems of scattered reconnaissance area, uncertainty, and inapplicability of optimal task decision-making methods

Pending Publication Date: 2021-07-30
BEIJING INSTITUTE OF TECHNOLOGYGY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Especially for widely scattered "low, slow and small" targets, after the reconnaissance mission, a large number of drones will need to consume a lot of energy to complete the return requirements, which will indirectly affect the start of the next reconnaissance mission, resulting in a significant drop in reconnaissance efficiency and revenue
[0005] On the other hand, considering the constraints of the total flight distance of the UAV cluster and the arrival distance of each UAV, there are optimal task decision-making methods in the prior art, but in the optimal task decision-making method, each The effect of drones tending to perform missions closer to where they take off is less pronounced
Especially in the face of "low, slow and small" targets, due to the wide spread of targets, the reconnaissance area is scattered and uncertain, and the traditional optimal task decision-making method is not applicable

Method used

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  • Distribution method for aerial area reconnaissance tasks of unmanned aerial vehicle cluster
  • Distribution method for aerial area reconnaissance tasks of unmanned aerial vehicle cluster
  • Distribution method for aerial area reconnaissance tasks of unmanned aerial vehicle cluster

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0141] 3 UAVs carry out target reconnaissance mission assignments to 9 areas where targets are likely to exist within the range of 2000*2000m. The 3 UAVs carry the same sensitive components, numbered U1, U2, and U3, and their flight speeds are all 20m / s, the initial coordinates are (930,1010), (1690,1610), (860,320) respectively.

[0142] Reconnaissance area coordinates and probability μ j As shown in Table 1.

[0143] Table I

[0144] scout area label Reconnaissance area coordinates / km Probability μ j

T 1 (120,1740) 0.95 T 2 (1970,1460) 0.95 T 3 (940,1590) 0.95 T 4 (1980,1970) 0.95 T 5 (1100,1320) 0.90 T 6 (1410,1770) 0.90 T 7 (170,1180) 0.90 T 8 (950,1210) 0.90 T 9 (1430,860) 0.90

[0145] The allocation method proceeds in the following steps:

[0146] S1. Establish a revenue model;

[0147] S2. Initialize the bundle set;

[0148] S3. Preliminary task allocation according to the income model; ...

Embodiment 2

[0174] 4 UAVs assign target reconnaissance tasks to 20 areas where targets are likely to exist within a range of 500*500km.

[0175] Among them, the sensitive components carried by the UAV and their speed and position are shown in Table 2; the type and probability of the reconnaissance area μ j As shown in Table 3, the coordinates of the reconnaissance area are shown in Table 4.

[0176] Table II

[0177]

[0178] Table three

[0179]

[0180] Table four

[0181] label task area type Mission point coordinates / km label task area type Mission point coordinates / km T 1 Type I (219,376) T 11 Type II (138,420) T 2 Type I (191,128) T 12 Type II (340,127) T 3 Type I (383,253) T 13 Type II (328,407) T 4 Type I (398,350) T 14 Type II (81,122) T 5 Type I (93,445) T15 Type II (99,465) T 6 Type I (245.480) T 16 Type II (249,175) T 7 Type I (283,284) T 17 Type II (480,98) T 8 Type ...

experiment example 1

[0220] The result of comparative example 1 ( figure 2 ) and the results of Comparative Example 1 ( image 3 ), it can be clearly found that in the task allocation scheme formed in embodiment 1, the task path of each drone is closer to forming a closed loop, which is more convenient for the recovery of the drone, and it is easier for the drone to perform other subsequent tasks.

[0221] The result of comparative example 2 ( Figure 4 ) and the results of Comparative Example 2 ( Figure 5 ), it can be clearly found that Example 2 forms a task allocation scheme, and the task path of each UAV is closer to forming a closed loop. Compared with the task allocation in Comparative Example 2, the low recovery consumption of the UAV cluster is more practical Reconnaissance task assignment, at the same time, inter-machine communication and update time stamps, avoid multiple drones from performing similar tasks separately, and avoid repeated execution of tasks.

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Abstract

The invention discloses a distribution method for air area reconnaissance tasks of an unmanned aerial vehicle cluster. The method comprises the following steps of establishing a revenue model, initializing a beam set, performing task distribution, and performing conflict resolution distribution. According to the distribution method for the aerial area reconnaissance tasks of the unmanned aerial vehicle cluster, the overall task time and recovery consumption are saved.

Description

technical field [0001] The invention relates to a method for assigning tasks, in particular to a method for assigning reconnaissance tasks in an air area of ​​an unmanned aerial vehicle cluster, and belongs to the field of unmanned aerial vehicle control. Background technique [0002] The essence of the distribution of reconnaissance tasks in the air area of ​​​​UAV clusters is to rationally allocate areas with different reconnaissance values ​​to multiple types of UAVs with different performance sensors, so that the efficiency of completing reconnaissance tasks is maximized and the cost is minimized. Optimization problem. [0003] In the prior art, there is a CBAA algorithm (consensus based auction algorithm) for single-task allocation problems, and it is improved to a CBBA algorithm (consensus based bundle algorithm) for multi-task allocation problems. These methods are based on distributed auction algorithms and consensus ideas, and can It is better to respond to the task...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05D1/10
CPCG05D1/104
Inventor 宋韬王坤韩煜李斌范世鹏张福彪郭凯阳
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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