This article addresses the dynamic formation reconstruction and trajectory replanning problem in the air patrol task using multiple fixed-wing unmanned aerial vehicle formations. Unlike most of the formation flying related work, this article considers a more practical issue that some of the vehicles may break down during operation. In this case, a more reasonable coping strategy is proposed which is to reconstruct the formation such that the task objectives can be achieved optimally. To perform the patrol task, a virtual target is introduced which moves along the patrol path with a predetermined speed. Considering the fact that not all the vehicles have access to the patrol path information, a decentralized estimator is designed for each vehicle to estimate the virtual target state respectively based on which the individual reference trajectories can be generated. As these reference trajectories do not satisfy relevant physical constraints, including system model, control input limits, no-fly zone avoidance, and intervehicle collision avoidance, a novel model predictive trajectory replanning algorithm is developed to generate feasible reference trajectories for each vehicle in real time, which is computationally attractive by incorporating a situation-dependent mechanism. Simulations have been conducted to validate the effectiveness of our proposed algorithm.

中文翻译：

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多无人机巡逻的编队重建和弹道重新规划

本文解决了使用多个固定翼无人飞行器编队的空中巡逻任务中的动态编队重建和轨迹重新规划问题。与大多数与编队飞行有关的工作不同，本文考虑了一个更实际的问题，即某些车辆在运行过程中可能会发生故障。在这种情况下，提出了一种更合理的应对策略，该策略是重构队形，以便可以最佳地实现任务目标。为了执行巡逻任务，引入虚拟目标，该虚拟目标以预定速度沿着巡逻路径移动。考虑到并非所有车辆都可以访问巡逻路径信息，针对每个车辆设计分散估计器，以分别估计虚拟目标状态，基于该虚拟目标状态可以生成各个参考轨迹。由于这些参考轨迹不满足相关的物理约束，包括系统模型，控制输入限制，避飞区避免和车辆碰撞避免，因此，开发了一种新颖的模型预测轨迹重新规划算法以实时为每辆车生成可行的参考轨迹，通过结合一种情况相关的机制在计算上具有吸引力。已经进行了仿真以验证我们提出的算法的有效性。为了避免车辆碰撞，开发了一种新型的模型预测轨迹重新规划算法，以实时为每辆车生成可行的参考轨迹，该算法通过结合一种情况相关的机制而在计算上具有吸引力。已经进行了仿真以验证我们提出的算法的有效性。为了避免车辆碰撞，开发了一种新型的模型预测轨迹重新规划算法，以实时为每辆车生成可行的参考轨迹，该算法通过结合一种情况相关的机制而在计算上具有吸引力。已经进行了仿真以验证我们提出的算法的有效性。