期刊
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
卷 29, 期 8, 页码 1053-1068出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/0278364910369861
关键词
motion planning; motion planning with uncertainty; Markov decision process; partially observable Markov decision process
类别
Partially observable Markov decision processes (POMDPs) provide a principled, general framework for robot motion planning in uncertain and dynamic environments. They have been applied to various robotic tasks. However, solving POMDPs exactly is computationally intractable. A major challenge is to scale up POMDP algorithms for complex robotic tasks. Robotic systems often have mixed observability : even when a robot's state is not fully observable, some components of the state may still be so. We use a factored model to represent separately the fully and partially observable components of a robot's state and derive a compact lower-dimensional representation of its belief space. This factored representation can be combined with any point-based algorithm to compute approximate POMDP solutions. Experimental results show that on standard test problems, our approach improves the performance of a leading point-based POMDP algorithm by many times.
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