Modeling and production of robot trajectories using the Temporal Parametrized Self Organizing Maps.
In this paper we proposed an unsupervised neural architecture, called Temporal Parametrized Self Organizing Map (TEPSOM), capable of learning and reproducing complex robot trajectories and interpolating new states between the learned ones. The TEPSOM combines the Self-Organizing NARX (SONARX) network, responsible for coding the temporal associations of the robotic trajectory, with the Parametrized Self-Organizing (PSOM) network, responsible for an efficient interpolation mechanism acting on the SONARX neurons. The TEPSOM network is used to model the inverse kinematics of the PUMA 560 robot during the execution of trajectories with repeated states. Simulation results show that the TEPSOM is more accurate than the SONARX in the reproduction of the learned trajectories.
Padoan Junior
Antonio Carlos
AC
Departamento de Engenharia Elétrica, Universidade de São Paulo, Av. Trabalhador Sancarlense, 400, São Carlos, São Paulo, CEP 13566-590, Brasil. padoan@sel.eesc.sc.usp.br
De A  Barreto
Guilherme
G
Araújo
Aluizio F R
AF
D000465:Algorithms; D001185:Artificial Intelligence; D001696:Biomechanical Phenomena; D003198:Computer Simulation; D004532:Ego; D006801:Humans; D007858:Learning; D008377:Maps as Topic; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons; D017711:Nonlinear Dynamics; D012371:Robotics