A POMDP Approach to Robot Motion Planning under Uncertainty
نویسندگان
چکیده
Motion planning in uncertain and dynamic environments is critical for reliable operation of autonomous robots. Partially observable Markov decision processes (POMDPs) provide a principled general framework for such planning tasks and have been successfully applied to several moderately complex robotic tasks, including navigation, manipulation, and target tracking. The challenge now is to scale up POMDP planning algorithms and handle more complex, realistic tasks. This paper outlines ideas aimed at overcoming two major obstacles to the efficiency of POMDP planning: the “curse of dimensionality” and the “curse of history”. Our main objective is to show that using these ideas— along with others—POMDP algorithms can be used successfully for motion planning under uncertainty for robotic tasks with a large number of states or a long time horizon. We implemented some of our algorithms as a software package Approximate POMDP Planning Library (APPL), now available for download at http://motion.comp.nus.edu.sg/ projects/pomdp/pomdp.html.
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