POMDP-APF:一种基于POMDP模型的APF无人机路径规划策略

POMDP-APF:UAV path planning strategy of APF based on POMDP model

针对无人机在路径规划过程中会遇到静态或者动态的障碍物,从而导致路径规划失败的问题,提出一种基于部分可观测马尔可夫决策过程(partially observable Markov decision process,POMDP)模型的人工势场(artificial potential field,APF)无人机路径规划策略(POMDP-APF)。首先使用传感器获得的障碍物信息结合POMDP模型预测障碍物的未来位置,为无人机的路径规划做准备;其次,提出一种新的基于障碍物的正方体外接球的模型,保障无人机在路径规划过程中的安全性;最后,结合改进的APF算法实现无人机的路径规划。仿真结果表明,POMDP-APF策略在无人机实时路径规划中具有良好的可行性和有效性,使无人机能够有效避开障碍物,同时路径长度以及耗费时间更短。

In order to solve the failure of path planning with static or dynamic obstacles in the process of path planning,this paper proposed an artificial potential field based on POMDP-APF path planning for UAV(unmanned aerial vehicle).Firstly,the obstacle information obtained by sensors was combined with POMDP model to predict the future locations of obstacles.Se-condly,it proposed a new circumscribed sphere of cube for obstacle to ensure the safety of UAV in path planning process.Finally,combined with the improved APF algorithm,it realized the path planning of UAV.Simulation results show that POMDP-APF strategy has good feasibility and effectiveness in the real-time path planning of UAV,so that UAV can effectively avoid obstacles with shorter path length and time.