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Unmanned aerial vehicle cluster cooperative landing sorting method and system

A sorting method and UAV technology, applied in neural learning methods, biological neural network models, other database retrievals, etc., can solve problems such as increasing the risk of crashes, priority landing of poor UAVs, and inability to guarantee reliability. Reduced probability of crash risk, simple to implement, easy-to-understand effects

Active Publication Date: 2020-07-03
NAT INNOVATION INST OF DEFENSE TECH PLA ACAD OF MILITARY SCI
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  • Summary
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  • Description
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  • Application Information

AI Technical Summary

Benefits of technology

This technology helps predict failures on drones used in various applications such as autonomous vehicles (ADVs). By analyzing their operational states from multiple dronies together, it becomes easier to determine which ones have failed first before they start flying out again. It does this without manually selecting specific locations where there may still exist another one at once. Additionally, if certain drons don't work well enough but stay around too long due to factors like environmental conditions, then these dronys could potentially crash into other droncies instead of just being able to return home safely after taking off-line. Overall, this system makes sure dronics run more efficiently while minimizing potential damage caused by human error.

Problems solved by technology

This patented describes an example where multiple drones form a group called a Swarov Group (SWG). These drons collaborate together for better performance on their mission area. When one unit crashes down due to another failure, there may also affect neighbor units' ability to coordinate actions effectively. To address this issue, certain dronies use self-driving technology to automatically execute predetermined sequences based upon specific flight paths. By doing these operations, they ensure reliable operation over time without any human intervention.

Method used

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  • Unmanned aerial vehicle cluster cooperative landing sorting method and system
  • Unmanned aerial vehicle cluster cooperative landing sorting method and system
  • Unmanned aerial vehicle cluster cooperative landing sorting method and system

Examples

Experimental program
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Effect test

Embodiment 1

[0055] A kind of unmanned aerial vehicle swarm cooperative landing sorting method, such as figure 1 shown, including:

[0056] Step 1: Each UAV in the UAV cluster calculates its own failure probability prediction value according to its own operating state data;

[0057] Step 2: Each UAV communicates with other UAVs in the UAV cluster to obtain the predicted failure probability values ​​of all UAVs;

[0058] Step 3: Determine the landing sequence number of each UAV in the UAV cluster according to the failure probability prediction value.

[0059] Step 1: Each UAV in the UAV cluster calculates its own failure probability prediction value according to its own operating status data, including:

[0060] Each drone calculates its own health characteristic value based on its own operating status data;

[0061] Calculate its own failure probability prediction value according to the health characteristic value.

[0062] Specifically, the health feature value is the Euclidean distance...

Embodiment 2

[0111] Based on the same inventive concept, the present invention also provides a UAV cluster landing sorting system, such as figure 2 As shown, including: calculation module, communication module, selection module;

[0112]Calculation module: used for each UAV in the UAV cluster to calculate its own failure probability prediction value according to its own operating state data;

[0113] Communication module: used for each UAV to communicate with other UAVs to obtain the predicted value of failure probability of all UAVs;

[0114] Selection module: used to determine the landing sequence number of each UAV in the UAV cluster according to the failure probability prediction value.

[0115] The calculation module includes a first calculation sub-module and a second calculation sub-module; the first calculation sub-module: for each UAV to calculate its own health characteristic value according to its own operating state data; the second calculation sub-module: for The health cha...

Embodiment 3

[0139] An application scenario of UAV swarms, UAV swarms equipped with a swarm intelligent operating system perform tasks in a certain area and are in an assembly state before landing. There are 6 quadrotor UAVs in the UAV cluster, and the numbers are recorded as UAV_ID=1, UAV_ID=2, UAV_ID=3, UAV_ID=4, UAV_ID=5, UAV_ID=6.

[0140] UAV swarm cooperative landing sorting method, such as image 3 As shown, the overall architecture is based on the idea of ​​distributed edge computing. The UAVs in the UAV cluster calculate the predicted value of failure probability for a certain future operation cycle, and then broadcast it to other UAVs. The specific steps are as follows:

[0141] Step 1: Each UAV in the UAV cluster calculates its own failure probability prediction value according to its own operating state data;

[0142] Analyze the flight status data from the flight controller interface of the UAV in the cluster, obtain the rough eigenvalues ​​of performance degradation, and cal...

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Abstract

The cooperative landing sorting method of the unmanned aerial vehicle cluster comprises the steps that each unmanned aerial vehicle in the unmanned aerial vehicle cluster calculates a failure probability prediction value of the unmanned aerial vehicle according to respective operation state data; each unmanned aerial vehicle communicates with other unmanned aerial vehicles in the unmanned aerial vehicle cluster, and failure probability prediction values of all the unmanned aerial vehicles are obtained; and according to the failure probability prediction value, determining a landing sequence number of each unmanned aerial vehicle in the unmanned aerial vehicle cluster. According to the technical scheme provided by the invention, the reliability prediction method is adopted, the unmanned aerial vehicle clusters are sorted through the physical performance strength difference, and the subjectivity of manually specifying or randomly selecting the landing sequence of the unmanned aerial vehicles by a program is avoided.

Description

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Claims

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Application Information

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Owner NAT INNOVATION INST OF DEFENSE TECH PLA ACAD OF MILITARY SCI
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