复杂环境下多无人机协同目标跟踪路径规划

Path Planning for Multiple UAVs Cooperative Target Tracking in Complex Environment

为解决多无人机在复杂环境下协同目标跟踪的路径规划问题,提出基于模型预测控制(model predictive control,MPC)和先进自适应化学反应优化(advanced adaptive chemical reaction optimization,AACRO)算法相结合的方法。基于目标跟踪模型,使用集中式MPC作为路径规划问题的实时控制框架,设计5个指标成本函数在多种约束条件下优化跟踪性能,获取无人机的最优跟踪路径;针对上述多维问题的复杂程度,使用一种新型智能算法解算MPC控制策略。仿真结果表明:该方案具备有效性和可行性,对无人机群协同目标跟踪具有重要的应用价值。

In order to solve the path planning problem of multiple unmanned aerial vehicles(UAVs)cooperative target tracking in complex environment,a model predictive control(MPC)and advanced adaptive chemical reaction optimization(AACRO)algorithm is proposed.Based on the target tracking model,the centralized MPC is used as the real-time control framework of the path planning problem,and five index cost functions are designed to optimize the tracking performance under various constraints,and the optimal tracking path of the UAV is obtained.In view of the complexity of the above multi-dimensional problem,a new intelligent algorithm is used to solve the MPC control strategy.The simulation results show that the scheme is effective and feasible,and has important application value for cooperative target tracking of unmanned aerial vehicle group.