Multi-object recognition and retrieval using Puma560 robot

The objective of the research described here is to develop efficient algorithm and software tools for multiobject recognition and retrieval. This research project addresses two major issues: The first issue is the identification of features and efficient methods for feature extraction which can completely describe an object. These features can be acquired using visual and ultra-sonic sensors. The second issue is the development of efficient algorithms for the retrieval of multi-objects based on their features; The methods and algorithms developed in this research are verified on a Unimation PUMA 560 robot. Non contact sensors (a vision and a range sensor) are employed for feature detection. The information from both sensors will be combined for feature extraction and feature mapping (sensor fusion). The sensors and the robot have been integrated for this purpose with a Pentium 133 Mhz Personal Computer

Object identification for robotic applications using expert systems

The objective of this project is to develop an intelligent machine vision system for robotic applications to identify engineering tools and components; The imaging system consists of a 2-D digital camera and an ultrasonic range sensor attached to the robot end effector. Images of the target objects are captured by the camera. The images are then processed to remove the signal noise and to extract the object boundary; One major objective is to develop object feature descriptions which are invariant to scaling, translation or orientation. Efficient data reduction to an array of fewer than 25 numbers is achieved by the use of Fourier and regional descriptors. One of the array elements, object thickness, is determined directly from ultrasound range measurement; An expert system was successfully developed to classify the objects based on their descriptors. The knowledge base consists of rules for searching and pattern matching. The sensors were integrated to form a working vision system for the PUMA 500 robot. The performance of the vision system was tested with a set of objects. The expert system was found to be efficient, successful, and reliable in identifying all tested objects even with signal noise being present

Desenvolvimento de um sistema computacional de apoio ao processo de seleção de garras de robos

Dissertação (mestrado) - Universidade Federal de Santa Catarina. Centro TecnologicoOs robôs vêm tendo uma importância cada vez maior na automação de processos industriais, e seu emprego tem resultado simultaneamente em uma grande melhoria na produtividade e na qualidade dos produtos. As garras ou efetuadores desempenham um papel fundamental em um sistema robótico, pois são os elementos responsáveis pelo manuseio da peça ou pelo trabalho executado sobre ela. Desta forma, seu projeto e utilização adequados são fundamenetais para o bom desempenho do sistema. A maioria dos robôs não vem equipada com garras. Considerando-se que a versatilidade e a programabilidade distinguem os robôs industriais modernos, a estratégia dos fabricantes, em muitos casos, é construir apenas o braço do manipulador, deixando para o usuário as tarefas de escolha, adaptação e desenvolvimento das garras. Desta forma, o trabalho de pesquisa e desenvolvimento de garras têm sido fragmentado, e de certa forma descoordenado. Assim, as pesquisas tem resultado em um número muito grande de variantes de garras. Devido à multiplicidade de exigências, as garras têm sido quase que exclusivamente projetadas para aplicações específicas