一种动态不确定环境中机器人路径规划方法

摘要

针对动态不确定环境下的机器人路径规划问题,将部分可观察马尔可夫决策过程( POMDP)与人工势场法(APF)的优点相结合,提出一种新的机器人路径规划方法.该方法充分考虑了实际环境中信息的部分可观测性,并且利用APF无需大量计算的优点指导POMDP算法的奖赏值设定,以提高POMDP算法的决策效率.仿真实验表明,所提出的算法拥有较高的搜索效率,能够快速地到达目标点.%This paper introduces a new path planning in dynamic nondeterministic environments. We combine POMDP and APF into the new path planning which takes full account of the uncertainty of the information in real world. Based on the APF's advantage of avoiding the expensive computation, it guides the setting of POMDP's rewards value to improve the efficiency of decision making. The result of the simulation shows that the proposed algorithm has higher search efficiency and can make the robot reach the target faster.