Abstract
To solve the problem of intercepting a moving target by a multirotor unmanned aerial vehicle (UAV) swarm, an optimal guidance strategy is proposed. The proposed guidance law is based on the integration of the classic pure pursuit guidance law and Kuhn-Munkres (KM) optimal matching algorithm, and virtual force potential functions are used to avoid collision. The proposed optimal guidance strategy is demonstrated by simulation experiments. The simulation results indicate that with the proposed optimal guidance strategy, a UAV swarm can intercept a moving target while maintaining the predetermined formation, and during the formation flight, the collisions between UAVs or the target can be avoided. Through a comparative experiment, the proposed optimal matching algorithm is proven to significantly reduce the average per-sampling-period total flight distance of all the UAVs and accelerate the interception process, and the formation completion degree is improved.
Highlights
With the advancement of computer control technology, unmanned aerial vehicles (UAVs) have developed greatly
In this paper, a novel optimal guidance strategy is proposed for a UAV swarm to intercept a moving target, which includes a three-dimensional guidance law for UAVs and an optimal matching algorithm between UAVs and their flight destinations
It distinguishes from previous studies in that the target is allowed to be in a state of variable-velocity curvilinear motion instead of uniform rectilinear motion only; in addition, collision avoidance is considered
Summary
With the advancement of computer control technology, unmanned aerial vehicles (UAVs) have developed greatly. Based on the pure-pursuit guidance law, an enhanced guidance law is proposed to guide a UAV swarm to form a specific formation and intercept a moving target. The concept of virtual leader UAV is introduced to maintain the formation, and a formation strategy is designed according to the flight characteristics of multirotor UAVs. In addition, a virtual force-based algorithm is proposed for collision avoidance. Based on graph theory, combined with Kuhn–Munkres (KM) and Hungarian algorithms, a novel algorithm is designed to optimally match UAVs and their flight destinations Through this algorithm the average per-samplingperiod total flight distance of all the UAVs is significantly reduced, the interception process is accelerated, and the formation completion degree is improved, where the ‘‘formation completion degree’’ is used to evaluate the difference between the preset formation and the actual formation.
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