  Mobile robot navigation in total or partially unknown environments is still
an open problem. The path planning algorithms lack completeness and/or
performance. Thus, there is the need for complete (i.e., the algorithm
determines in finite time either a solution or correctly reports that there is
none) and performance (i.e., with low computational complexity) oriented
algorithms which need to perform efficiently in real scenarios. In this paper
we evaluate the efficiency of two versions of the A star algorithm for mobile
robot navigation inside indoor environments with the help of two software
applications and the Pioneer 2DX robot. We demonstrate that an improved version
of the A star algorithm (we call this the fast A star algorithm) which (a
different version of this algorithm is widely used in video games) can be
successfully used for indoor mobile robot navigation. We evaluated the two
versions of the A star algorithm first, by implementing the algorithms in
source code and by testing them on a simulator and second, by comparing two
operation modes of the fast A star algorithm w.r.t. path planning efficiency
(i.e., completness) and performance (i.e., time need to complete the path
traversing) for indoor navigation with the Pioneer 2DX robot. The results
obtained with the fast A star algorithm are promising and we think that this
results can be further improved by tweaking the algorithm and by using an
advanced sensor fusion approach (i.e., combine the inputs of multiple robot
sensors) for better dealing with partially known environments.
