Cooperative Multi-UAV Collision Avoidance Based on a Complex Network

Huang, Yang; Tang, Jun; Lao, Songyang

doi:10.3390/app9193943

Cited by 6 publications

(2 citation statements)

References 41 publications

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“…A collision avoidance method for cooperative UAV clusters can be formulated by employing the complex network algorithm. 60 An important advantage of the approach is that only one resolution is necessary at a time, instead of a sequence of manoeuvres. Conflicts are detected for a given scenario by path extrapolation, based on which the network model can be constructed.…”

Section: Literature Reviewmentioning

confidence: 99%

On the underlying dynamics of traffic conflicts related to stochastic behaviour

Oreg

Shin

Tsourdos

2022

Proceedings of the Institution of Mechanical Engineers, Part G:

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The aim of this paper is to analyse why and how air traffic conflicts occur as a result of the stochastic behaviour of both the ownship and the intruder and to show how system-level characteristics can be derived from such an analysis. Ensemble dynamics in a given traffic scenario have already been analysed using multi-agent simulations by many; however, such an analysis is hardly ever backed up and interpreted in terms of an analytical study. By making use of directional conflict probability maps, characteristics of integral, system-level quantities can be explained, providing further insight into the relationship of speed distribution parameters and system performance quantities, namely, safety and throughput.

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Section: Literature Reviewmentioning

confidence: 99%

On the underlying dynamics of traffic conflicts related to stochastic behaviour

Oreg

Shin

Tsourdos

2022

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…Huang et al applied complex network to conflict resolution of UAV swarm, identifying key UAV by edge weights and resolving them in the direction of reduced network robustness. [20]. However, these methods only consider the position information of the aircraft to build network edges, which cannot accurately reflect the conflict relationship between aircrafts.…”

Section: Introductionmentioning

confidence: 99%

Conflict Resolution Strategy Based on Flight Conflict Network Optimal Dominating Set

Yang

et al. 2022

International Journal of Aerospace Engineering

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Aiming at the problem that the air traffic flow is increasing year by year and the flight conflicts are difficult to be deployed, we take aircraft as the node and established a flight conflict network based on the flight conflict relationship between aircrafts. After that, we define the concept of an optimal dominating set. By removing the optimal dominating set nodes of the flight conflict network, the conflicts in the network can be quickly resolved and the complexity of the network is reduced. In the process of solving the optimal dominating set of the network, we introduce the immune mechanism based on the particle swarm algorithm (PSO) and ensure the priority deployment of a critical aircraft and high-risk conflicts by setting two types of antigens, nodes and connected edges. Compared with the traditional method, the conflict resolution strategy presented in this paper is able to quickly identify key aircraft nodes in the network and has better sensitivity to high-risk conflict edges, which can provide controllers and the control system with a more accurate and reliable suggestion to resolve the flight conflicts macroscopically.

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Exploring Dynamic Characteristics of Multi-State Air Traffic Flow: A Time Series Approach

Chao

Wang

2020

IEEE Access

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Understanding the dynamics of air traffic flow is important to achieving advanced air traffic management. This work explores the dynamic evolution and fluctuation characteristics of multistate air traffic time series from a complex network perspective, which is essential for understanding the nature of an air traffic system. With the application of the fundamental diagram (FD), we discover that the relative velocity, flight distance and trajectory similarity are the three key variables for interpreting the arrival traffic flow states of the Xiamen Gaoqi International Airport. According to these three variables, time series are classified into four traffic states based on the K-means algorithm: free flow (FF), transitional flow (TF), slightly congested flow (SCF) and heavily congested flow (HCF). The extracted time series in different states are converted into complex networks using the visibility graph method. We analyze and compare the statistical features of the networks in the four states in terms of indexes, such as the degree distribution and network structure. The results indicate that the complex network characteristics can be used to distinguish air traffic states from the original traffic flow. Our work may be helpful for scholars and engineers to better understand the intrinsic nature of air traffic and for the development of intelligent assistant decision-making systems for air traffic management. INDEX TERMS Air traffic flow, complex network, fundamental diagram, visibility graph, traffic states.

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Cooperative Multi-UAV Collision Avoidance Based on a Complex Network

Cited by 6 publications

References 41 publications

On the underlying dynamics of traffic conflicts related to stochastic behaviour

On the underlying dynamics of traffic conflicts related to stochastic behaviour

Conflict Resolution Strategy Based on Flight Conflict Network Optimal Dominating Set

Exploring Dynamic Characteristics of Multi-State Air Traffic Flow: A Time Series Approach

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