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Path planning plays a crucial role in robot action execution, since a path ora motion trajectory for a particular action has to be defined first before theaction can be executed. Most of the current approaches are iterative methodswhere the trajectory is generated iteratively by predicting the next statebased on the current state. Moreover, in case of multi-agent systems, paths areplanned for each agent separately. In contrast to that, we propose a novelmethod by utilising fully convolutional neural network, which allows generationof complete paths, even for more than one agent, in one-shot, i.e., with asingle prediction step. We demonstrate that our method is able to successfullygenerate optimal or close to optimal paths in more than 98\% of the cases forsingle path predictions. Moreover, we show that although the network has neverbeen trained on multi-path planning it is also able to generate optimal orclose to optimal paths in 85.7\% and 65.4\% of the cases when generating twoand three paths, respectively.

One-shot path planning for multi-agent systems using fully convolutional neural network