多无人机自适应编队协同航迹规划

针对多无人机(UAV)协同航迹规划中因编队队形约束而忽略部分较窄通道的问题,提出了一种基于自适应分布式模型预测控制的快速粒子群优化(ADMPC-FPSO)方法。该方法利用领航跟随法和虚拟结构法相结合的编队策略构造出虚拟编队引导点,以完成自适应编队协同控制任务。根据模型预测控制的思想,结合分布式控制方法,将协同航迹规划转化为滚动在线优化问题,且以最小距离等性能指标为代价函数。通过设计评价函数准则,使用变权重快速粒子群优化算法对问题进行求解。仿真结果表明,通过所提算法能够有效实现多无人机协同航迹规划,并可根据环境变化快速完成自适应编队变换,同时较传统编队策略代价更低。

Multi-unmanned aerial vehicle adaptive formation cooperative trajectory planning

Aiming at the problem of neglecting some narrow roads due to the formation constraints in the multi-UAV （Unmanned Aerial Vehicle） cooperative trajectory planning, a Fast Particle Swarm Optimization method based on Adaptive Distributed Model Predictive Control (ADMPC-FPSO) was proposed. In the method, the formation strategy combining leader-follower method and virtual structure method was used to construct adaptive virtual formation guidance points to complete the cooperative formation control task. According to the idea of model predictive control, combined with the distributed control method, the cooperative trajectory planning was transformed into a rolling online optimization problem, and the minimum distance and other performance indicators were used as cost functions. By designing the evaluation function criterion, the variable weight fast particle swarm optimization algorithm was used to solve the problem. The simulation results show that the proposed algorithm can effectively realize the multi-UAV cooperative trajectory planning, can quickly complete the adaptive formation transformation according to the environmental changes, and has lower cost than the traditional formation strategy.