多无人机协同四维航迹规划的改进的Tau-H策略

为了在满足多类约束条件的前提下进行多无人机（UAVs）协同飞行的航迹规划及优化，提出改进的Tau-H运动策略.在常用的Tau-H运动策略基础上，通过在运动间距表达式中引入初速度的相关项，克服了固有的仅能考虑始末速度为零的情况的缺陷，可以实现无人机始末非静止的到达时间和速度的四维运动匹配.利用粒子群算法（PSO）搜索各无人机的初始耦合系数，形成全局航迹预规划；建立各无人机间的通信拓扑，采用采样间隔与冲突判断双驱动的滚动优化方法，求解局部再规划问题，实现航迹不断更新.通过对算例的仿真表明，基于改进的Tau-H策略得到的航迹可飞性好，安全性高，能够满足连续型多无人机航迹规划及优化的应用需求.
Abstract: An improved Tau-H strategy was proposed for route planning and optimization of multiple unmanned aerial vehicles (UAVs) flight cooperation to satisfy various constraints. The improved Tau-H strategy overcame the original shortcomings of common-used Tau-H motion strategy by introducing the correlative term of initial velocity into the motion formula, which was only applicable to problems with initial and ending velocity of zero. Then the strategy can be employed to plan four-dimensional (4D) motion of UAVs with nonzero initial and ending velocity to match the arrival time and velocity. Global trajectories were generated by using particle swarm optimization (PSO) algorithm to search the initial coupling coefficient of UAVs. Then the trajectories were constantly updated by solving the local planning problem by the rolling optimization method with double drive of sampling interval and collision detection based on the established communication topology among UAVs. Simulation results of several examples indicate that the improved Tau-H strategy based method can satisfy relevant application requirements for receiving the continuous 4D cooperative route planning and optimization of multi-UAVs with great flight capability and high flight safety.