DR. KENY ORDAZ HERNÁNDEZ

@article{Navarro-Gonzalez2015,

title = {On-line incremental learning for unknown conditions during assembly operations with industrial robots},

author = {Navarro-Gonzalez, Jose Luis and Lopez-Juarez, Ismael and Ordaz-Hernandez, Keny and Rios-Cabrera, Reyes },

url = {http://dx.doi.org/10.1007/s12530-014-9125-x},

doi = {10.1007/s12530-014-9125-x},

issn = {1868-6486},

year  = {2015},

date = {2015-01-01},

journal = {Evolving Systems},

volume = {6},

number = {2},

pages = {101--114},

abstract = {The assembly operation using industrial robots can be accomplished successfully in well-structured environments where the mating pair location is known in advance. However, in real-world scenarios there are uncertainties associated to sensing, control and modelling errors that make the assembly task very complex. In addition, there are also unmodeled uncertainties that have to be taken into account for an effective control algorithm to succeed. Among these uncertainties, it can be mentioned disturbances, backlash and aging of mechanisms. In this paper, a method to overcome the effect of those uncertainties based on the Fuzzy ARTMAP artificial neural network (ANN) to successfully accomplish the assembly task is proposed. Experimental work is reported using an industrial 6 DOF robot arm in conjunction with a vision system for part location and wrist force/torque sensing data for assembly. Force data is fed into an ANN evolving controller during a typical peg in hole (PIH) assembly operation. The controller uses an incremental learning mechanism that is solely guided by the sensed forces. In this article, two approaches are presented in order to compare the incremental learning capability of the manipulator. The first approach uses a primitive knowledge base (PKB) containing 16 primitive movements to learn online the first insertion. During assembly, the manipulator learns new patterns according to the learning criteria which turn the PKB into an enhanced knowledge base (EKB). During a second insertion the controller uses effectively the EKB and operation improves. The second approach employs minimum information (it contains only the assembly direction) and the process starts from scratch. After several operations, that knowledge base increases by including only the needed patterns to perform the insertion. Experimental results showed that the evolving controller is able to assemble the matting pairs enhancing its knowledge whenever it is needed depending on the part geometry and level of expertise. Our approach is demonstrated through several PIH operations with different tolerances and part geometry. As the robot's expertise evolves, the PIH operation is carried out faster with shorter assembly trajectories.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{NavarroGonzalez201578b,

title = {On-line knowledge acquisition and enhancement in robotic assembly tasks},

author = {Navarro-Gonzalez, Jose Luis and Lopez-Juarez, Ismael and Rios-Cabrera, Reyes and Ordaz-Hernandez, Keny},

url = {http://www.sciencedirect.com/science/article/pii/S073658451400074X},

doi = {http://dx.doi.org/10.1016/j.rcim.2014.08.013},

issn = {0736-5845},

year  = {2015},

date = {2015-01-01},

journal = {Robotics and Computer-Integrated Manufacturing},

volume = {33},

pages = {78 - 89},

abstract = {Abstract Industrial robots are reliable machines for manufacturing tasks such as welding, panting, assembly, palletizing or kitting operations. They are traditionally programmed by an operator using a teach pendant in a point-to-point scheme with limited sensing capabilities such as industrial vision systems and force/torque sensing. The use of these sensing capabilities is associated to the particular robot controller, operative systems and programming language. Today, robots can react to environment changes specific to their task domain but are still unable to learn skills to effectively use their current knowledge. The need for such a skill in unstructured environments where knowledge can be acquired and enhanced is desirable so that robots can effectively interact in multimodal real-world scenarios. In this article we present a multimodal assembly controller (MAC) approach to embed and effectively enhance knowledge into industrial robots working in multimodal manufacturing scenarios such as assembly during kitting operations with varying shapes and tolerances. During learning, the robot uses its vision and force capabilities resembling a human operator carrying out the same operation. The approach consists of using a MAC based on the Fuzzy ARTMAP artificial neural network in conjunction with a knowledge base. The robot starts the operation having limited initial knowledge about what task it has to accomplish. During the operation, the robot learns the skill for recognising assembly parts and how to assemble them. The skill acquisition is evaluated by counting the steps to complete the assembly, length of the followed assembly path and compliant behaviour. The performance improves with time so that the robot becomes an expert demonstrated by the assembly of a kit with different part geometries. The kit is unknown by the robot at the beginning of the operation; therefore, the kit type, location and orientation are unknown as well as the parts to be assembled since they are randomly fed by a conveyor belt.},

note = {Special Issue on Knowledge Driven Robotics and Manufacturing},

keywords = {},

pubstate = {published},

tppubtype = {article}

}