改进人工势场法无人机脱离局部极小值问题

Method of UAV separation from local minima based on improved artificial potential field method

针对现有人工势场法在无人机路径规划应用过程中存在无人机陷入受力平衡,导致目标不可达,且当无人机陷入局部极小值时,该方法无法根据环境信息使无人机脱离力平衡点的问题,提出一种基于自膨胀系数改进的人工势场法。针对无人机陷入局部极小值的问题,建立自膨胀函数;当无人机陷入局部极小值时,针对无人机与目标点的距离为膨胀提供方向。通过改变自膨胀系数增大最近障碍物对无人机产生的斥力从而改变无人机受力形式,解决无人机陷入最优解的问题。仿真实验结果表明,改进后的人工势场法多次改变了无人机陷入最优点的情况,能够解决人工势场法无人机陷入局部极小值无法自主脱离的问题。

In allusion to the problem of unmanned aerial vehicle(UAV)getting stuck in force balance during the application of existing artificial potential field methods in UAV path planning,resulting in unreachable targets,and the inability of this method to detach UAVs from the force balance point based on environmental information when UAVs get stuck in local minima,an improved artificial potential field method based on self expansion coefficient is proposed.A self expansion function is established to address the issue of drones getting stuck in local minima.When the drone falls into a local minimum,the distance between the drone and the target point can provide the direction for expansion.By changing the self expansion coefficient to expand the repulsive force generated by the nearest obstacle on the drone,the force form of the drone can be changed,solving the problem of the drone falling into the optimal solution.The simulation experimental results show that the improved artificial potential field method has repeatedly changed the situation of unmanned aerial vehicles being trapped in the optimal position,and can solve the problem of UAVs being trapped in local minima and unable to autonomously detach by means of the artificial potential field method.