基于改进人工势场算法的无人机群避障算法研究

针对无人机运动避障人工势场算法本身存在的极小值问题和局部最小值问题,采用改进的人工势场算法,提出了一种新的路径规划方法。不同于目前的人工势场法,该模型从双机相互作用开始,在障碍物斥力的基础上,增加了无人机之间的斥力,同时定义集群的前置形心作为另一个引力源。算法分析表明,该方法能够有效避免无人机陷入局部最小值,并增强了无人机机群的控制和避障能力。基于该无人机控制模型,给出了路径规划设计并进行了仿真实验。实验结果表明,基于该模型的无人机机群控制具有更好的避障性能和追踪目标的能力。

Research on Obstacle Avoidance Algorithm of UAV Group Based on Improved Artificial Potential Field Algorithm

Aiming at the minimum and local minimum problems of the artificial potential field algorithm for obstacle avoidance of UAV motion, a new path planning method is proposed by using the improved artificial potential field algorithm. Unlike the current artificial potential field method, the model starts from the interaction between two aircraft, increases the repulsion between UAVs on the basis of obstacle repulsion, and defines the precentral center of cluster as another gravitational source. The algorithm analysis shows that this method can effectively avoid UAV falling into local minimum, and enhance the control and obstacle avoidance ability of UAV fleet. Based on the UAV control model, the path planning design is given and the simulation experiment is carried out. The experimental results show that UAV fleet control based on this model has better obstacle avoidance performance and tracking ability.