# Resilient Formation Reconfiguration for Leader-Follower Multi-UAVs
## 2 Related Works  
With the aim of improving the safety and autonomy of multi-rotor UAV swarm flight, a trajectory generation approach based on B-spline and its corresponding adaptive optimization approach were proposed [29]. In order to boost the rapidity and precision of task processing for UAV swarms, a modified rational clustering method (RCM) algorithm was developed with the help of intensive task neighbor information exchange and state learning [30].  
Aiming at the problems of formation and reconfiguration for the vertical take-off and landing multi-UAVs with dynamic topologies, the authors in [31] designed a cascade-formation control law based on the artificial potential function. The authors in [32] devised a time-optimal heuristic formation reconfiguration approach for multi-UAVs based on the gradient algorithm. Based on the assumption that the role of each swarm member is alterable, the authors in [33] developed a formation reconstruction policy with the help of weighted graph matching, by which the total amount of member movement during topology adjustment can be reduced.  
Aiming at the UAV formation moving target tracking problem, a fusion model was constructed with the help of a receding horizon control (RHC) algorithm and standoff algorithm [34], and compared with the general RHC algorithm, more satisfactory results were achieved in real-time collaborative trajectory planning and real-time obstacle avoidance. Nonetheless, the validity of the fusion model is on the premise that the swarm communication topology is fully connected and the swarm scale is fixed. With the expansion of the swarm scale, the complexity of the swarm communication can increase greatly, thus making the swarm communication topology reconfiguration more challenging. In [35], an intelligent formation controller based on the RHC algorithm, adaptive hybrid particle swarm optimization and differential evolution (AHPSODE) algorithm was proposed for a multi-rotor UAV swarm with a fixed scale, by which the formation reconfiguration can be achieved under the condition of minimizing the total movement distance and reducing the computation cost.  
In response to the changes in the requirements of battlefield missions, an alliance-based formation control framework was established for leader-follower multi-UAVs [16], where the ant colony pheromone partitioning algorithm (ACPPA) was adopted to divide the original leader-follower multi-UAVs into different subgroups. In terms of the leaderless subgroups, the information concentration competition mechanism (ICCM) was introduced to achieve flexible leader election. However, only the kinematic information of each UAV was utilized in this leader election scheme.  
From the aspect of formation reconfiguration for multi-UAVs with a cut-down scale, compared with swarm communication reconfiguration, the authors in [36] placed more emphasis on swarm geometry reconfiguration. In [36], a framework of UAV formation reconfiguration based on system resilience was constructed. The concept of resilience was defined to quantitatively evaluate the disaster-tolerant capability of multi-UAVs. Then, a parameter optimization model was established on the basis of system resilience. Meanwhile, an improved adaptive learning-based pigeon-inspired optimization (ALPIO) algorithm was designed to achieve formation reconfiguration of multi-UAVs after random attacks with maximal system resilience, reduced reconfiguration time and expanded coverage area.  
Compared with existing works, the innovation of the article is clarified in Table 1.