  Motion planning in the configuration space (C-space) induces benefits, such
as smooth trajectories. It becomes more complex as the degrees of freedom (DOF)
increase. This is due to the direct relation between the dimensionality of the
search space and the DOF. Self-organizing neural networks (SONN) and their
famous candidate, the Self-Organizing Map, have been proven to be useful tools
for C-space reduction while preserving its underlying topology, as presented in
[29]. In this work, we extend our previous study with additional models and
adapt the approach from human motion data towards robots' kinematics. The
evaluation includes the best performant models from [29] and three additional
SONN architectures, representing the consequent continuation of this previous
work. Generated Trajectories, planned with the different SONN models, were
successfully tested in a robot simulation.
