DR. REYES RIOS-CABRERA

@article{Aviles-Vi\~{n}as2016b,

title = {On-line learning of welding bead geometry in industrial robots},

author = {Aviles-Vi\~{n}as, Jaime F and Rios-Cabrera, Reyes and Lopez-Juarez, Ismael },

url = {http://dx.doi.org/10.1007/s00170-015-7422-6},

doi = {10.1007/s00170-015-7422-6},

issn = {1433-3015},

year  = {2016},

date = {2016-01-01},

journal = {The International Journal of Advanced Manufacturing Technology},

volume = {83},

number = {1},

pages = {217--231},

abstract = {In this paper, we propose an architecture based on an artificial neural network (ANN), to learn welding skills automatically in industrial robots. With the aid of an optic camera and a laser-based sensor, the bead geometry (width and height) is measured. We propose a real-time computer vision algorithm to extract training patterns in order to acquire knowledge to later predict specific geometries. The proposal is implemented and tested in an industrial KUKA KR16 robot and a GMAW type machine within a manufacturing cell. Several data analysis are described as well as off-line and on-line training, learning strategies, and testing experimentation. It is demonstrated during our experiments that, after learning the skill, the robot is able to produce the requested bead geometry even without any knowledge about the welding parameters such as arc voltage and current. We implemented an on-line learning test, where the whole experiments and learning process take only about 4 min. Using this knowledge later, we obtained up to 95 % accuracy in prediction.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Rios-Cabrera2016,

title = {Robotic GMAW online learning: issues and experiments},

author = {Rios-Cabrera, Reyes and Morales-Diaz, America B. and Aviles-Vi\~{n}as, Jaime F and Lopez-Juarez, Ismael },

url = {http://dx.doi.org/10.1007/s00170-016-8618-0},

doi = {10.1007/s00170-016-8618-0},

issn = {1433-3015},

year  = {2016},

date = {2016-01-01},

journal = {The International Journal of Advanced Manufacturing Technology},

volume = {87},

number = {5},

pages = {2113--2134},

abstract = {This paper presents three main contributions: (i) an experimental analysis of variables, using well-defined statistical patterns applied to the main parameters of the welding process. (ii) An on-line/off-line learning and testing method, showing that robots can acquire a useful knowledge base without human intervention to learn and reproduce bead geometries. And finally, (iii) an on-line testing analysis including penetration of the bead, that is used to train an artificial neural network (ANN). For the experiments, an optic camera was used in order to measure bead geometry (width and height). Also real-time computer vision algorithms were implemented to extract training patterns. The proposal was carried out using an industrial KUKA robot and a GMAW type machine inside a manufacturing cell. We present expermental analysis that show different issues and solutions to build an industrial adaptive system for the robotics welding process.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1108/IR-09-2014-0395,

title = {Acquisition of welding skills in industrial robots},

author = {Aviles-Vi\~{n}as, Jaime F and Lopez-Juarez, Ismael and Rios-Cabrera, Reyes },

url = {http://dx.doi.org/10.1108/IR-09-2014-0395},

doi = {10.1108/IR-09-2014-0395},

year  = {2015},

date = {2015-01-01},

journal = {Industrial Robot: An International Journal},

volume = {42},

number = {2},

pages = {156-166},

abstract = {Purpose \textendash The purpose of this paper was to propose a method based on an Artificial Neural Network and a real-time vision algorithm, to learn welding skills in industrial robotics. Design/methodology/approach \textendash By using an optic camera to measure the bead geometry (width and height), the authors propose a real-time computer vision algorithm to extract training patterns and to enable an industrial robot to acquire and learn autonomously the welding skill. To test the approach, an industrial KUKA robot and a welding gas metal arc welding machine were used in a manufacturing cell. Findings \textendash Several data analyses are described, showing empirically that industrial robots can acquire the skill even if the specific welding parameters are unknown. Research limitations/implications \textendash The approach considers only stringer beads. Weave bead and bead penetration are not considered. Practical implications \textendash With the proposed approach, it is possible to learn specific welding parameters despite of the material, type of robot or welding machine. This is due to the fact that the feedback system produces automatic measurements that are labelled prior to the learning process. Originality/value \textendash The main contribution is that the complex learning process is reduced into an input-process-output system, where the process part is learnt automatically without human supervision, by registering the patterns with an automatically calibrated vision system.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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{RIOSCABRERA2012742,

title = {Efficient multi-camera vehicle detection, tracking, and identification in a tunnel surveillance application},

author = {Rios-Cabrera, Reyes and Tuytelaars, Tinne and Van Gool, Luc J.},

url = {http://www.sciencedirect.com/science/article/pii/S1077314212000380},

doi = {http://dx.doi.org/10.1016/j.cviu.2012.02.006},

issn = {1077-3142},

year  = {2012},

date = {2012-01-01},

journal = {Computer Vision and Image Understanding},

volume = {116},

number = {6},

pages = {742 - 753},

keywords = {},

pubstate = {published},

tppubtype = {article}

}