  This paper discusses opportunities to parallelize graph based path planning
algorithms in a time varying environment. Parallel architectures have become
commonplace, requiring algorithm to be parallelized for efficient execution. An
additional focal point of this paper is the inclusion of inaccuracies in path
planning as a result of forecast error variance, accuracy of calculation in the
cost functions and a different observed vehicle speed in the real mission than
planned. In this context, robust path planning algorithms will be described.
These algorithms are equally applicable to land based, aerial, or underwater
mobile autonomous systems. The results presented here provide the basis for a
future Research project in which the parallelized algorithms will be evaluated
on multi and many core systems such as the dual core ARM Panda board and the 48
core Single-chip Cloud Computer (SCC). Modern multi and many core processors
support a wide range of performance vs. energy tradeoffs that can be exploited
in energyconstrained environments such as battery operated autonomous
underwater vehicles. For this evaluation, the boards will be deployed within
the Slocum glider, a commercially available, buoyancy driven autonomous
underwater vehicle (AUV).
