基于模型预测控制与改进人工势场法的多无人机路径规划

模型预测控制(model predictive control,MPC)已成功地应用于无人机集群的路径规划.但其存在计算量大及单步运算时间长等不足,在实时运行中往往难以获得较高的控制频率.而离线的MPC需要准确的地图信息,难以处理地图中无法预测的动态障碍物.对此,提出一种结合离线MPC全局规划与在线改进人工势场法局部规划的方法.在利用MPC方法生成安全、平滑轨迹的同时,提高无人机在动态障碍物影响下的避障能力.通过引入调节力来处理传统人工势场法的局部极小值问题,并将目标与无人机的相对距离引入斥力函数,同时改进引力函数,以改善无人机在目标点处低速徘徊的问题.此外,设计一种事件触发的无人机轨迹变更与轨迹恢复策略,使无人机仅在必要时实施动态避障行为.在此基础上,最大化利用原来的规划轨迹.仿真验证结果表明,所提出的路径规划方法能够使无人机集群安全飞行至目标点,并且具有良好的动态避障能力.

A multiple UAVs path planning method based on model predictive control and improved artificial potential field

The model predictive control(MPC) method has been applied in the path planning for unmanned aerial vehicle(UAV) swarm. However, it has some disadvantages, such as high computation consumption, long time single-step execution, et al. These disadvantages make the MPC method difficult for real-time implementation which requires high control updating frequency. The offline MPC method requires accurate map information and struggles with handling unpredictable dynamic obstacles. In this paper, a path planning strategy is proposed which combines offline MPC for the global planning with the online improved artificial potential field(APF) for the UAVs'' local planning. This approach enhances the UAV''s obstacle avoidance capability while ensuring safe and smooth trajectories generated by the MPC. This paper introduces an adjustment force to solve the local minimum problem in the traditional APF method. A repulsive function based on the relative distance between the target and UAVs, and an attractive function are designed to alleviate the UAVs'' low speed problem near the target point. An event-triggered UAV trajectory modification and recovery strategy is also designed, enabling the UAV to perform dynamic obstacle avoidance behaviors only when it is necessary, thus maximizing the utilization of the original planned trajectory. Simulation results demonstrate that the proposed method can make the UAVs reach the target point with excellent dynamic obstacle avoidance capabilities.