Título del libro: 2017 Chilean Conference On Electrical, Electronics Engineering, Information And Communication Technologies, Chilecon 2017 - Proceedings
Título del capítulo: Towards learning contact states during peg-in-hole assembly with a dual-arm robot
ROMAN VICTORIANO OSORIO COMPARAN; JUAN MARIO PEÑA CABRERA;
Autores externos:
Idioma:
Español
Año de publicación:
2017
Palabras clave:
Motion planning; Neural networks; Robotic arms; Starters; Constrained forces; Dual arm; Fuzzy ARTMAP architecture; Neural network controllers; Peg-in-hole; Peg-in-hole assembly; Peg-in-hole tasks; Three categories; Robots
Resumen:
The use of dual arm robots for human like operations such as the peg-in-hole tasks can be facilitated by learning contact states during manipulation. In this paper, we propose a control scheme that includes the learning of contact states during operations. The approach includes the use of a Motoman SDA-20 dual-arm robot equipped with two threefingered gripper and a force/torque sensing capability. A real operation using the assembly of an automotive starter motor is used as a case of study. Several patterns are generated and classified during the stages of the starter assembly. Three categories were considered during operations: moving both arms simultaneously and moving the right arm while keeping the left arm static and vice versa. Contact states were generated using the robot and learned by an Artificial Neural Network (ANN) Fuzzy ARTMAP architecture. The output of the ANN has been envisaged to be a motion command to diminish the constrained forces while moving the arm in the assembly direction. Results during contact state classification have shown that manipulative forces can be recognized by the Neural Network Controller (NNC) so that a valid motion command can be issued to the arm robot favoring the assembly task. © 2017 IEEE.
Entidades citadas de la UNAM:
ISBN:
9781538631232
Editorial:
Institute of Electrical and Electronics Engineers Inc.
