Abstract:
Motion planning in uncertain environments is an essential feature of autonomous
robots. Partially observable Markov decision processes (POMDP) provides a princi-
pled approach for such planning tasks and have been successfully employed for various
robotic applications. Offline planning algorithms for POMDPs have proved to achieve
optimal policies. However, these algorithms are computationally very expensive and
are not often applicable to accomplish realistic robot motion planning scenarios. As
an alternative to offline planning Monte Carlo algorithms for online planning were
developed, e.g. DESPOT and POMCP are implemented for public use within a set
of open-source POMDP libraries. The aim of this master thesis is to explore applica-
bility of available open-source POMDP libraries to real-time robot motion planning
tasks. We adopt these libraries for the POMDP models proposed in the literature
and replicate a number of realistic benchmark experiments.