期刊名称：International Journal of Hybrid Information Technology
印刷版ISSN：1738-9968
出版年度：2016
卷号：9
期号：11
页码：315-322
出版社：SERSC
摘要：To address the problem that reactive navigation is prone to local optimality under uncertain and complex environments, a POMDP-based global path planning algorithm is proposed for mobile robots. A 6-tuple model is constructed for path planning under complex dynamic environments, and the global optimality is realizes by maximizing the accumulative reward function. State transition function and observation function are used to handle unknown obstacles and noisy perception by modeling the error probability. Belief state space is introduced, and a value iteration algorithm using point-based policy tree pruning is developed to solve for real time planning policy, which effectively reduces the computational complexity. Simulation results show that using this algorithm the robot can automatically adapt to different probing granularities, avoid obstacles under complex uncertain environments, and achieve the optimal paths.
