Self-organizing maps to generate state trajectories of manipulators

Abstract

This paper presents a self-organized artificial neural network model, called State Trajectory Generator (STRAGEN) capable of generating state trajectories. The model is incremental, it can grow and diminish dynamically during the training phase and adapt itself to the represented space. STRAGEN can consider different criteria to choose neighbors and to adapt to different domains or different characteristics of a same domain. This capacity enables STRAGEN with a representation strategy that can deal with heterogeneous information. Moreover, different criteria also allow STRAGEN to generate trajectories that optimize different measures of the problem space. The algorithm was tested to generate trajectories in a robotic manipulators domain, with two and three dimensions.