一种基于人工势场的无人机航迹规划算法

为了改进传统的人工势场法不能适应复杂环境、容易陷入最小值和在终点附近徘徊的情况,提出一种基于混沌理论的人工势场法的无人机航迹规划算法。在传统人工势场法原理的基础上,将混沌理论的搜索算法引入人工势场法中的斥力场、引力场的函数公式中,改变了各个障碍物斥力系数和目标点的引力系数,将改变后的系数代入计算,搜索出斥力场和引力场的最优系数组。本算法有如下优点:第一,考虑了障碍物对寻优过程的影响,排除了合力为零的情况。第二,通过迭代的方法,具有适应不同地图的能力。第三,适用于无人机的航迹规划。仿真实验结果和理论分析表明,混沌理论的人工势场法不仅解决了无人机在航迹规划中容易陷入最小值和在终点附近徘徊等问题,而且可以实现无人机在复杂环境下的航迹规划,缩短了飞行成本,节约了计算时间,提高了三维空间无人机航迹规划的速度和精度。

An improved route planning algorithm for unmanned aerial vehicle based on artificial potential field

In order to improve the precision and accuracy of artificial potential field, and avoid the situation of hovering on the end point and being caught in minimum value, a path planning algorithm based on artificial potential algorithm for unmanned aerial vehicle is presented. The traditional artificial potential field method is improved: chaos theory is used to improve artificial potential field calculation formula, which changes the potential field coefficients of each barrier and target point, resulting in the best screened out route. The simulation experimental analysis and result show that the optimized algorithm considers the influence of obstacles to the optimization process, ruling out the situation of hovering on the end point in unmanned aerial vehicle route planning, and through iterative method, the algorithm has the ability to adapt to different maps. The improved artificial potential field method is better than the traditional artificial potential field method in speed and precision aspects.