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

Comparing SONN Types for Efficient Robot Motion Planning in the Configuration Space