Abstract
The conflict resolution problem in cooperative unmanned aerial vehicle (UAV) clusters sharing a three-dimensional airspace with increasing air traffic density is very important. This paper innovatively solves this problem by employing the complex network (CN) algorithm. The proposed approach allows a UAV to perform only one maneuver—that of the flight level change. The novel UAV conflict resolution is divided into two steps, corresponding to the key node selection (KS) algorithm based on the node contraction method and the sense selection (SS) algorithm based on an objective function. The efficiency of the cooperative multi-UAV collision avoidance (CA) system improved a lot due to the simple two-step collision avoidance logic. The paper compares the difference between random selection and the use of the node contraction method to select key nodes. Experiments showed that using the node contraction method to select key nodes can make the collision avoidance effect of UAVs better. The CA maneuver was validated with quantitative simulation experiments, demonstrating advantages such as minimal cost when considering the robustness of the global traffic situation, as well as significant real-time and high efficiency. The CN algorithm requires a relatively small computing time that renders the approach highly suitable for solving real-life operational situations.
Highlights
Collision is an inherent problem in unmanned aerial vehicle (UAV) systems [1,2,3,4,5,6]
The computational efficiency of the complex network (CN) algorithm is higher than the other three algorithms, and it is worth mentioning that the average consuming time did not exponentially increase
The two algorithms jointly applied a state-of-the-art CN theory to provide a method for resolving threats that occur when a group of unmanned aircrafts meets, by analyzing various states of the UAV group in the local airspace
Summary
Collision is an inherent problem in unmanned aerial vehicle (UAV) systems [1,2,3,4,5,6]. CDR and the optimization of different resolutions have been discussed widely by many researchers and practitioners These methods can be classified into two primary types of algorithms: Geometric and path planning. A collision cone approach is employed by which the irregularly shaped moving objects can be modelled through general quadric surfaces and dynamic inversion-based avoidance strategies can be derived The limitations of this type of method is the need for information from the intruder UAV, as well as sensitivity to noise from the input data of the sensors. The collision avoidance based on a complex network (CACN) model grounds the generation of optimal direction changes of the objective function, which integrates the number of intruder UAVs of designated direction, the robustness of the network, and the connected components of the network.
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