A method and system for unmanned aerial vehicle swarm cooperative landing sorting
A sorting method and UAV technology, applied in neural learning methods, instruments, data processing applications, etc., can solve problems such as poor UAV landing priority, inability to guarantee reliability, and increased crash risk, achieving simple implementation, Ease of understanding, the effect of reducing the probability of a crash risk
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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: According to the predicted value of failure probability, determine the landing sequence number of each UAV in the UAV cluster.
[0059] Step 1: Each UAV in the UAV cluster calculates its own failure probability prediction value according to its own operating state data, including:
[0060] Each UAV calculates its own health characteristic value according to 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 dist...
Embodiment 2
[0111] Based on the same inventive concept, the present invention also provides a UAV cluster landing sequencing system, such as figure 2 shown, including: a calculation module, a communication module, and a 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 predicted value of failure probability.
[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 Th...
Embodiment 3
[0139] An application scenario of UAV swarms. UAV swarms equipped with swarm intelligent operating systems have completed tasks in a certain area and are in a state of assembly before landing. There are a total of 6 quadrotor UAVs in the UAV swarm, 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 collaborative landing sequencing method, such as image 3 As shown in the figure, the overall architecture is based on the idea of distributed edge computing. The UAVs in the UAV cluster calculate the predicted value of the failure probability of its own operation cycle in the future, 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] The flight status data is parsed from the flight controller interface of the UAV in the cluster, the coarse eigenvalu...
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