Hefei University of Technology

Huawei Ma

Guoqiang Wang

Xiaoxuan Hu

Wei Xia

Peng Jin

He Luo

Moning Zhu

Xiaoduo Li

Shanlin Yang

Peng Zhang

Xiang Fang

Tianyang Gao

Haiquan Sun

Ya Li

Waiming Zhu

Yi Wu

Yunhui Wang

Ju Wang

Xin Cao

Zhengzheng Liang

Yanqiu Niu

The present invention discloses a partition satellite mission planning method for earliest completion of regional target coverage, which includes the following steps: 1. partitioning a to-be-observed rectangular region; 2. allocating observation resources to different regions and selecting certain coverage opportunities and coverage modes thereof to minimize completion time corresponding to a formed coverage solution. The present invention can obtain a satisfactory solution capable of finishing complete coverage of regional targets as early as possible with sufficient observation resources through suitable calculation resources and achieve balance between the consumption of calculation resources and optimality of solutions, so that satellite resources can be fully used to conduct the quick and effective coverage search for important regional targets in a real environment.

Partition satellite mission planning method for earliest completion of regional target coverage

An intelligent decision-making device and method for UAV formation information interaction topologies in communication interference, comprising: acquiring a three-dimensional UAV formation without communication interference, an initial communication network Dand an initial information interaction topology T; acquiring communication links Ainterrupted by UAV formation with communication interference; acquiring interrupted communication links Ain Tbased on Aand T; determining whether Aaffects T; if not, Tbeing the final information interaction topology; if yes, acquiring substitute reverse arcs of Aand substituting Awith them to obtain an information interaction topology T; determining whether Tis a three-dimensional persistent graph; if yes, Tbeing the final information interaction topology; if not, acquiring an undirected graph Rcorresponding to T; acquiring spare edges based on R; and adding an arc corresponding to a spare edge in Tbased on the spare edges to obtain a final information interaction topology.

Intelligent decision-making method and device for UAV formation information interaction topologies in communication interference

The embodiments of the present disclosure provide a method and system for patrolling an expressway by unmanned aerial vehicles (UAVs). The method is applied to the system including a control center, a plurality of UAVs and a plurality of advertising board charging base stations. The method firstly receives unmanned aerial vehicle (UAV) status information sent by the plurality of unmanned aerial vehicles and road section patrolling data through the control center, and receives advertising board charging base station information sent by the plurality of advertising board charging base stations, then classifies, stores and analyzes the received data and information, and finally generates and sends control commands for the UAVs and charging advertising boards. On one hand, the technical solutions of the present disclosure may comprehensively schedule an UAV formation, may also comprehensively control the advertising board charging base stations, and may further dispose and schedule task responses; and on the other hand, the technical solutions of the present disclosure may analyze situations of accidents, violations and the like and scheduling and usage of the UAVs and the advertising board charging base stations, thereby providing decision support data for the control center.

Method and system for patrolling an expressway by unmanned aerial vehicles

A method and a system for reoptimizing unmanned aerial vehicle formation communication topology based on minimum cost arborescence are used for optimizing reconstructed UAV formation communication topology after a communication failure occurs on the UAVs in the formation. The method includes calculating a first communication cost of the reconstructed UAV formation communication topology; comparing the first communication cost with a target communication cost for the minimum cost arborescence of the formation communication diagram under a predetermined state; when the first communication cost is greater than the target communication cost for the minimum cost arborescence of the formation communication diagram under the predetermined state, optimizing the reconstructed UAV formation communication topology through a predetermined strategy.

Method and system for reoptimizing unmanned aerial vehicle formation communication topology based on minimum cost arborescence

The embodiments of the present invention disclose a method and apparatus for joint optimization of multi-UAV task assignment and path planning. The method comprises: obtaining the location information of a plurality of UAVs and a plurality of target points, the dispersion of groundspeed course angle, and motion parameters of each UAV and wind field; constructing an initial population based on the location information, the dispersion of groundspeed course angle and a preset genetic algorithm; determining the flight status of each UAV and the flight time taken by each UAV to complete a path segment of the corresponding Dubins flight path based on the initial population and the motion parameters, obtaining the total time taken by all the UAVs corresponding to each chromosome to complete the task based on the flight time of the path segment; and subjecting the chromosomes in the initial population to crossover and mutation based on the genetic algorithm and, when a predetermined number of iterations is reached, selecting the optimal Dubins flight path as the joint optimization result. In the embodiments of the present invention, the UAV flight path planning problem is combined with the actual flight environment of the UAV, so that the optimal flight path obtained is superior to the solution in which the UAV speed is constant.

Method and apparatus for joint optimization of multi-UAV task assignment and path planning

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Anhui, China

Huawei Ma