一种无人机三维航迹规划算法研究

Research for an algorithm of UAV three-dimensional path planning

在研究无人机三维航迹规划问题时,针对基于传统人工蜂群算法易陷入局部最优值、后期收敛速度变慢、寻优效率低的问题,提出了一种改进人工蜂群算法的无人机航迹规划方法。首先,在建立包括经纬度、海拔高度信息的三维飞行区域模型后,加入了地形约束模型,并引入新的综合航迹代价评价方式。然后,在算法中引入自适应搜索策略、新型概率选择策略与Logistic混沌搜索算子来增强其对原始信息的开采能力,提高其收敛速度以及加强其鲁棒性。最后,通过三维航迹规划仿真和面对突发威胁的局部航迹再规划仿真对所提算法的有效性进行了验证。结果表明,改进后的算法提高了全局收敛能力,在收敛速度和精度上优于遗传算法和传统人工蜂群算法,适合用来解决无人机的三维航迹规划问题。

Aiming at the basic artificial bee colony algorithm in the optimization process of the three-dimensional path planning for the unmanned aerial vehicles, the algorithm can be trapped in local optimum. And the rate of convergence or efficiency of the path planning would be reduced at the last stage. To overcome above problem, an improved ABC algorithm has been proposed in this paper. Firstly, with the model of planning space consisting of longitude, latitude and altitude information and terrain established, the cost value of the path planning has been obtained. Then, an adaptive search strategy is introduced to increase the speed of convergence. A new probability of selection strategy is introduced to keep the diversity of the population. And a chaotic sequence with Logistic map is adopted to iporove its robustness. Finally, the effectiveness of the improved algorithm has been verified through the three-dimensional path planning simulation and the local newly planning for the emergent threats. All the results show that the proposed algorithm has improved the global optimizing ability, and has a great advantage of convergence property and robustness compared with the genetic algorithm or traditional ABC algorithm. And the proposed algorithm is fit for solving path planning.