A collaborative management and control system and method for unmanned aerial vehicles (UAVs) based on the fusion of spatiotemporal uncertainty closed-loop perception and dynamic credibility.

By constructing a UAV collaborative management and control system based on the fusion of spatiotemporal uncertainty closed-loop perception and dynamic credibility, the problem of the uncertainty of UAV system perception results in complex environments has been solved. This system achieves decision-making flexibility in risk perception, enhanced credibility of multi-source collaboration, and closed-loop driven continuous system evolution, thereby improving task completion rate and system reliability.

CN122086085APending Publication Date: 2026-05-26王栋
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Patent Information

Application Number
CN202610272157.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-06
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

The uncertainty of perception results in complex environments by existing drone management systems has not been quantified, which makes it impossible for decision-makers to distinguish the credibility level of the identification results, resulting in blind decision-making and system rigidity, and making it unable to cope with the safety challenges of highly dynamic low-altitude environments.

Method used

Data is collected by an integrated sensing base station and multimodal sensors of UAVs. Second-order probabilistic feature maps are constructed using evidence deep learning. Uncertainty weights are dynamically adjusted, and multi-UAV collaborative verification is carried out. The data is mapped to airspace risk levels, and collaborative avoidance strategies are generated through risk mapping functions. A closed-loop feedback mechanism is established to optimize the perception model.

Benefits of technology

It has achieved enhanced decision-making flexibility in risk perception, enhanced credibility of multi-source collaboration, and continuous system evolution driven by closed loop, significantly improving task completion rate and system reliability, and reducing collision probability and false alarm rate.

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Abstract

This application provides a UAV collaborative management method based on the fusion of spatiotemporal uncertainty closed-loop perception and dynamic credibility, including: S1: using evidence deep learning to perform second-order probability modeling on the collected multidimensional heterogeneous data, and using Dirichlet distribution modeling for classification tasks to obtain a second-order probability feature map containing data uncertainty and model uncertainty, and encapsulating the positioning information, classification information, and the two types of uncertainty into node attributes of a dynamic semantic graph; extracting the target's positioning and classification information, and fusing the dual uncertainties through a dynamic weight adjustment mechanism based on environmental parameters, positioning accuracy, and historical performance to generate a comprehensive uncertainty measure; in a multi-UAV collaborative field... In this scenario, the credibility consistency of the perception results of each UAV is verified. Spatial consistency index, temporal jump penalty factor and historical reputation score are calculated and fused to generate credibility consistency verification index. The fused credibility is mapped to airspace risk level. In the multi-UAV collaborative scenario, a potential game loss function is constructed with risk value as constraint, Nash equilibrium is solved and collaborative avoidance trajectory is generated, where the virtual collision radius is dynamically scaled according to the risk value. The real trajectory and predicted trajectory returned by the execution layer are obtained and the residual is calculated. When overconfidence of the perception model is detected, feedback gradient is generated through the edge lightweight large language model to correct the relevant parameters of the evidence deep learning network.
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Citation Information

Patent Citations

  • Unmanned aerial vehicle aerial photography target credible identification method based on data and model dual uncertainty perception

    CN116740587A

  • Low-altitude airspace management method and system based on communication and sensing integration

    CN118280168A