Beihang University

Wenbo Du

Xianbin Cao

Yumeng Li

Xi Bo Zhu

Peng Yang

Lei Zheng

Liang Xu

Xing Xi

Dapeng Wu

Biyue Li

Zhenyu Xiao

Yakun Zhao

Haichao An

Jintong Zhang

This application provides an airspace complexity evaluation method based on deep unsupervised learning for air traffic management, which includes the following parts. A stacked autoencoder is used to establish an airspace complexity evaluation model. Input the airspace complexity factors into the stacked autoencoder to obtain the low-dimensional embedded representations of the airspace complexity factors. Cluster the low-dimensional embedded points to capture the centroids of the airspace complexity data. The application utilizes the soft assignment distribution and real assignment distribution of the embedded representations to construct a training loss function which optimizes the airspace complexity evaluation model by gradient descent algorithm. The trained airspace complexity evaluation model and the three obtained cluster centroids describing the airspace complexity level are used to evaluate the current airspace complexity.

Deep unsupervised learning approach, device and storage medium for airspace complexity evaluation

A computer-based airplane flight path planning method based on the pigeon-inspired optimization (PIO) algorithm includes steps of establishing an uncertainty prediction model, determining the path to be optimized, and obtaining an optimal path using the PIO algorithm for a flight controller onboard to execute. The PIO algorithm treats a pigeon flock as a scale-free network, applies map and compass operators to the scale-free network, and performs landmark operations to obtain the optimal path. The device that performs the path planning includes an access module for obtaining the regional environment information and a flight controller onboard the airplane. The flight controller includes a building module for setting up the trajectory prediction model including uncertainties; a determining module to determine the trajectories which need optimization; an optimization module, which uses the PIO algorithm to optimize the flight path; and a computer memory module.

Airplane flight path planning method and device based on the pigeon-inspired optimization

This disclosure provides a cooperative path planning method and device for automatic control aerocraft and aerocraft system. The method includes: determining the current evaluation index of each selected point according to its current position, the target position of the aerocraft and the position of obstacles in the selected area; updating the adjustment displacement of each selected point according to its adjustment displacement, current position and historical optimal position, as well as the global optimal position; updating the current position of each selected point according to its current position and the updated adjustment displacement; after the current position is updated for a set number of times, selecting the position corresponding to the optimal evaluation index from the current positions and global optimal position of all selected points as the next waypoint of the aerocraft.

Cooperative path planning method and device for automatic control aerocraft and aerocraft system

The disclosure provides a dynamic recovery method and system for UAVs and storage medium. On the basis of obtaining the environmental information of UAVs, combined with different factors such as the following task quantity of each UAV, the distance from the current position of each UAV to the recovery point, current performance of each UAV, total cost function is constructed, and optimization is carried out through improved particle swarm optimization algorithm. Then on the premise of the lowest recovery cost, the priority of each UAV is obtained. For the ordered UAV, the route to the recovery point is determined. At the same time, it is necessary to consider conflict resolution of UAVs when encountering obstacles in the flight process, as well as the possible failure in line recovery. At this time, it is necessary to reorder some hovering UAVs to ensure flight safety of UAVs and robustness of the algorithm.

Dynamic recovery method and system for UAVs and storage medium

A sector situation (SS) evaluation framework is based on knowledge transfer and is specifically applicable for small-training-sample environment. The SS evaluation framework is able to effectively mine knowledge hidden within the samples of both target and non-target sectors, and properly handle the integration between the knowledge derived from different sectors. This framework includes three main steps: (1) sufficiently mine the knowledge within the samples of the target sector using the strategies of multi-factor subset generation and multi-base evaluator construction, and build target base evaluators; (2) precisely learn the knowledge in the samples of the non-target sectors using similar strategies for the target sector, together with a sample transformation, and build non-target base evaluators; and (3) efficiently integrate the target and non-target base evaluators based on evaluation confidence analysis of those base evaluators.

Knowledge-transfer-based learning framework for airspace situation evaluation

Provided is a multi-UAV continuous movement control method for energy efficient communication coverage. The method includes: determining observation information at a current moment, the observation information including one or more of the following: energy consumption information of a UAV, coverage indication information of users covered by a UAV network facilitated by the UAV or coverage fairness information of the UAV network; determining control information corresponding to the observation information using a DDPG model according to the observation information, wherein the DDPG model is generated from a DDPG algorithm with sample information as an input, the sample information indicating a mapping relationship between sampled observation information and sampled control information; and controlling the UAV to move according to the control information. The value of the mobile policy from the DDPG model is continuous, thus mitigating the imprecision in existing movement control due to the discreteness in evaluating the mobile policy.

Multi-UAV continuous movement control method, apparatus, device, and storage medium for energy efficient communication coverage

Provided are flight conflict resolution method and apparatus based on ultimatum game theory. The method includes: obtaining a first priority of a first aircraft and a second priority of a second aircraft when it is determined that a minimum distance between the first aircraft and the second aircraft within a preset time period is less than a preset distance; determining a first angle and a second angle of the first aircraft and a fourth angle and a fifth angle of the second aircraft according to the first priority, the second priority, and a preset limiting deflection angle; determining a third angle of the first aircraft and a sixth angle of the second aircraft; determining a first deflection angle of the first aircraft and a second deflection angle of the second aircraft according to the first and second priorities, the first, second, third, fourth, fifth and sixth angles.

Flight conflict resolution method and apparatus based on ultimatum game theory

Provided are a network selection method and an apparatus for integrated cellular and drone-cell networks, where the method includes: acquiring a dynamic network model and a dynamic user model, generating a random event vector according to the dynamic network model and the dynamic user model; generating an action vector according to the random event vector; obtaining an individual utility of each user according to the action vector and the random event vector; constructing a first selection model; obtaining the value of the action probability according the first selection model to determine networks that the users choose to access according to the value of the action probability.

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Beijing, China

Wenbo Du