Mario Peña-Cabrera

Machine vision approach for robotic assembly

Purpose – Outcome with a novel methodology for online recognition and classification of pieces in robotic assembly tasks and its application into an intelligent manufacturing cell.Design/methodology/approach – The performance of industrial robots working in unstructured environments can be improved using visual perception and learning techniques. The object recognition is accomplished using an artificial neural network (ANN) architecture which receives a descriptive vector called CFD&POSE as the input. Experimental results were done within a manufacturing cell and assembly parts.Findings – Find this vector represents an innovative methodology for classification and identification of pieces in robotic tasks, obtaining fast recognition and pose estimation information in real time. The vector compresses 3D object data from assembly parts and it is invariant to scale, rotation and orientation, and it also supports a wide range of illumination levels.Research limitations/implications – Provides vision guidance...

Using Object’s Contour, Form and Depth to Embed Recognition Capability into Industrial Robots

Robot vision systems can differentiate parts by pattern matching irrespective of part orientation and location. Somemanufacturers offer 3D guidance systems using robust vision and laser systems so that a 3D programmed point canbe repeated even if the part is moved varying its location, rotation and orientation within the working space. Despitethese developments, current industrial robots are still unable to recognize objects in a robust manner; that is, todistinguish an object among equally shaped objects taking into account not only the object’s contour but also its formand depth information, which is precisely the major contribution of this research. Our hypothesis establishes that it ispossible to integrate a robust invariant object recognition capability into industrial robots by using image features fromthe object’s contour (boundary object information), its form (i.e., type of curvature or topographical surfaceinformation) and depth information (from stereo disparity maps). These features can be concatenated in order to forman invariant vector descriptor which is the input to an artificial neural network (ANN) for learning and recognitionpurposes. In this paper we present the recognition results under different working conditions using a KUKA KR16industrial robot, which validated our approach.

An approach for intelligent fixtureless assembly: issues and experiments

Industrial manufacturing cells involving fixtureless environments require more efficient methods to achieve assembly tasks. This paper introduces an approach for Robotic Fixtureless Assembly (RFA). The approach is based on the Fuzzy ARTMAP neural network and learning strategies to acquire the skill from scratch without knowledge about the assembly system. The vision system provides the necessary information to accomplish the assembly task such as pose, orientation and type of component. Different ad-hoc input vectors were used as input to the assembly and the vision systems through several experiments which are described. The paper also describes the task knowledge acquisition and the followed strategies to solve the problem of automating the peg-in-hole assembly using 2D images. The approach is validated through experimental work using an industrial robot.

On the design of a multimodal cognitive architecture for perceptual learning in industrial robots

Robots can be greatly benefited from the integration of artificial senses in order to adapt to changing worlds. To be effective in complex unstructured environments robots have to perceive the environment and adapt accordingly. In this paper, it is introduced a biology inspired multimodal architecture called M2ARTMAP which is based on the biological model of sensorial perception and has been designed to be a more versatile alternative to data fusion techniques and non-modular neural architectures. Besides the computational overload compared to FuzzyARTMAP, M2ARTMAP reaches similar performance. This paper reports the results found in simulated environments and also the observed results during assembly operations using an industrial robot provided with vision and force sensing capabilities.

Learning and fast object recognition in robot skill acquisition: a new method

Invariant object recognition aims at recognising an object independently of its position, scale and orientation. This is important in robot skill acquisition during grasping operations especially when working in unstructured environments. In this paper we present an approach to aid the learning of manipulative skills on-line. We introduce and approach based on an ANN for object learning and recognition using a descriptive vector built on recurrent patterns. Experimental learning results using a fast camera are presented. Some simple parts (i.e. circular, squared and radiused-square) were used for comparing different connectionist models (Backpropagation, Perceptron and FuzzyARTMAP) and to select the appropriate model. Later during experiments, complex figures were learned using the chosen FuzzyARTMAP algorithm showing a 93.8% overall efficiency and 100% recognition rate with not so complex parts. Recognition times were lower than 1 ms, which clearly indicates the suitability of the approach to be implemented in robotic real-world operations.

A learning approach for on line object recognition tasks

The performance of industrial robots working in unstructured environment can be improved using visual perception and learning techniques. In this work, a novel approach that uses 2D data and simple image processing techniques is introduced. A unique image vector descriptor (CFD&POSE) containing also depth information is computed and then input to a Fuzzy ART MAP architecture for learning and recognition purposes. This vector compresses 3D object data from assembly parts and is invariant to scale, rotation and orientation. The approach in combination with the fast learning capability of ART networks indicates the suitability for industrial robot applications as it is shown in experimental results.

Fuzzy Logic for Omnidirectional Mobile Platform Control Based in FPGA and Bluetooth Communication

An Omni directional mobile platform control using the intelligence technique of Fuzzy Logic is showed in the article, the control allows a practical and reliable driving control of 4 Omni directional wheels, implemented in FPGA allowing to have an independent and autonomous single chip system out of a central computer dependence in order to be used with different applications like service robots platforms. An additional feature is performed by using Bluetooth communication with a cellular phone based on a smartphone OS Android as the handset control device. Driving movement for the mobile platform is limited for 8 directions, a Fuzzy Logic module controls the travelling of the platform with independent movement for each wheel, physical feedback is implemented by using electronic decoders.

Proceso de Aprendizaje con Algoritmo Robusto para la Obtención del POSE de Objetos en Líneas de Ensamble con Robots en Tiempo Real (RT)

En el articulo, se presenta la metodologia y el algoritmo utilizado para obtener posicion y orientacion en tiempo real (POSE) de objetos que van a ser ensamblados en linea y en procesos de manufactura robotizados. La informacion del POSE y caracteristicas del objeto son integrados en un vector descriptivo que es utilizado por una red neuronal del tipo FuzzyARTMAP, para aprender del objeto y posteriormente reconocerlo y localizarlo. Se obtiene asi un proceso de aprendizaje de la locacion de piezas para su posterior reconocimiento y manipulacion por el robot manipulador. Se presenta la explicacion del algoritmo utilizado y su simulacion en MatLab 5.0, con casos concretos de formas basicas de piezas de ensamble. Se presentan tambien resultados experimentales dentro de una celda de manufactura con un robot de seis grados de libertad Kuka R15

Mapping visual behavior to robotic assembly tasks

This paper shows a methodology for on-line recognition and classification of pieces in robotic assembly tasks and its application into an intelligent manufacturing cell. The performance of industrial robots working in unstructured environments can be improved using visual perception and learning techniques. The object recognition is accomplished using an Artificial Neural Network (ANN) architecture which receives a descriptive vector called CFD&POSE as the input. This vector represents an innovative methodology for classification and identification of pieces in robotic tasks, every stage of the methodology is described and the proposed algorithms explained. The vector compresses 3D object data from assembly parts and it is invariant to scale, rotation and orientation, and it also supports a wide range of illumination levels. The approach in combination with the fast learning capability of ART networks indicates the suitability for industrial robot applications as it is demonstrated through experimental results.