Mario Peña

Using Background and Segmentation Algorithms Applied in Mobile Robots

Abstract In this article a segmentation algorithm for detecting moving objects is presented. The aim of the research is to integrate the algorithm in applications such as car parking video surveillance systems. One of the techniques used in this paper to detect motion in a sequence of images is the use of the background model, which is widely used. The technique allows to detect which objects are moving (without identification) which is the first stage for further processing in tasks such as tracking and object recognition. The results from the segmentation algorithm using several parameters are presented that validate the approach.

Fuzzy Logic for Omni directional Mobile Platform Control Displacement using FPGA and Bluetooth

The article shows an omnidirectional mobile platform control using the artificial intelligence technique of Fuzzy Logic; the control allows a practical and reliable driving control of 4 omnidirectional wheels. The control module is implemented in FPGA allowing having an independent and autonomous single chip system out of a central computer dependence to be used in different applications like service robots platforms. An additional feature is performed by using Bluetooth communication with a cellular phone as the handset control device. Driving movements for the mobile platform is limited for eight directions, a Fuzzy Logic module controls the travelling of the platform with independent movements for each wheel, physical feedback is implemented by using electronic decoders, and experimental results were achieved with an additional feature of a handset device control based on a smartphone OS Android with Bluetooth communication. Not using external software and having a system using an artificial intelligence technique implemented in fast hardware, gives the system robust and reliable control capabilities.

System Administration and Monitoring Moving Objects Using Mobile Devices

Paper presents a multiplatform server application for service management of mobile devices. Mobile devices can use as an alternative for surveillance and location in real-time taking the advantage of Global Positioning System (GPS) and wireless networks (Wi-Fi) technologies. We propose a monitoring system for personal safety it proposes since monitoring evidence in real-time and storage of multimedia files (image and video information).

Surveillance System Mobile Object Using Segmentation Algorithms

In this paper a segmentation algorithm is used to detect moving objects and to integrate it to a supervision and surveillance systems, in a parking lot, as a first step. One of the way to moving detection in image sequences is the moving object segmentation by background model, very well-known technique that it permit to know what objects are moving. This can be employed, in the second stage, to identify and to follow objects.

Movement and Color Detection of a Dynamic Object: An application to a Mobile Robot

The paper describes the integration of several image processing algorithms necessary to recognize a particular color and the movement of an object. The main objective is to detect the object by its color and track it by a mobile robot. Mean filter is applied to soften and sharpen the input image. Then, RGB filter is applied to calculate the center of mass and area of the object and to locate its position in a real environment to develop the robot motion. These algorithms are applied to a mobile robot, in a tested scenario, tracking an object.

A learning approach for on-line robotic assembly tasks

Abstract The acquisition of assembly skills by robots is greatly supported by the efective use of contact force sensing and object recognition vision systems. In this paper, we describe the ability to invariantly recognize assembly parts at different scale, rotation and orientation within the work space. The 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. In this sense, the described technique for 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. 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.