多无人机协同障碍规避控制方法

摘要

For solving the problem of multi-UAVs (multi-unmanned aerial vehicles) cooperative obstacle avoidance in dynamic environment, a method for controller design in combination with the extended Kalman filter (EKF) and model predictive control (MPC) was proposed. Firstly, distributed architecture for UAV cooperative obstacle avoidance, the motion model of UAV and the communication topology were established, respectively. Then the EKF algorithm was used to predict the trajectory of dynamic obstacle, and an information compensation rule was designed. Afterwards, based on the model predictive control (MPC) method, the controller for UAV obstacle avoidance was designed. The results show that the proposed EKF method can predict the trajectory of dynamic obstacle correctly, and that the cooperation between the UAVs can reduce the predictive errors effectively.%为有效地解决不确定动态环境下多无人机协同障碍规避问题，提出一种联合扩展卡尔曼滤波和模型预测控制的控制器设计方法。首先构建分布式无人机协同障碍规避体系架构、无人机的运动模型及其通信拓扑。采用扩展卡尔曼滤波(EKF)预测动态障碍物的轨迹，并设计一种信息补偿规则。然后，基于模型预测控制(MPC)方法，设计障碍规避控制器。仿真结果表明：EKF方法能够准确地预测动态障碍物的轨迹；无人机之间通过协作，可以有效地降低预测误差。