  Path planning plays a crucial role in robot action execution, since a path or
a motion trajectory for a particular action has to be defined first before the
action can be executed. Most of the current approaches are iterative methods
where the trajectory is generated iteratively by predicting the next state
based on the current state. Moreover, in case of multi-agent systems, paths are
planned for each agent separately. In contrast to that, we propose a novel
method by utilising fully convolutional neural network, which allows generation
of complete paths, even for more than one agent, in one-shot, i.e., with a
single prediction step. We demonstrate that our method is able to successfully
generate optimal or close to optimal paths in more than 98\% of the cases for
single path predictions. Moreover, we show that although the network has never
been trained on multi-path planning it is also able to generate optimal or
close to optimal paths in 85.7\% and 65.4\% of the cases when generating two
and three paths, respectively.
