Publicação
A sensor classification strategy for robotic manipulators
| dc.contributor.author | Lima, Miguel | |
| dc.contributor.author | Machado, J. A. Tenreiro | |
| dc.contributor.author | Ferrolho, António | |
| dc.date.accessioned | 2014-03-06T10:43:48Z | |
| dc.date.available | 2014-03-06T10:43:48Z | |
| dc.date.issued | 2010 | |
| dc.description.abstract | In practice the robotic manipulators present some degree of unwanted vibrations. The advent of lightweight arm manipulators, mainly in the aerospace industry, where weight is an important issue, leads to the problem of intense vibrations. On the other hand, robots interacting with the environment often generate impacts that propagate through the mechanical structure and produce also vibrations. In order to analyze these phenomena a robot signal acquisition system was developed. The manipulator motion produces vibrations, either from the structural modes or from endeffector impacts. The instrumentation system acquires signals from several sensors that capture the joint positions, mass accelerations, forces and moments, and electrical currents in the motors. Afterwards, an analysis package, running off-line, reads the data recorded by the acquisition system and extracts the signal characteristics. Due to the multiplicity of sensors, the data obtained can be redundant because the same type of information may be seen by two or more sensors. Because of the price of the sensors, this aspect can be considered in order to reduce the cost of the system. On the other hand, the placement of the sensors is an important issue in order to obtain the suitable signals of the vibration phenomenon. Moreover, the study of these issues can help in the design optimization of the acquisition system. In this line of thought a sensor classification scheme is presented. Several authors have addressed the subject of the sensor classification scheme. White (White, 1987) presents a flexible and comprehensive categorizing scheme that is useful for describing and comparing sensors. The author organizes the sensors according to several aspects: measurands, technological aspects, detection means, conversion phenomena, sensor materials and fields of application. Michahelles and Schiele (Michahelles & Schiele, 2003) systematize the use of sensor technology. They identified several dimensions of sensing that represent the sensing goals for physical interaction. A conceptual framework is introduced that allows categorizing existing sensors and evaluates their utility in various applications. This framework not only guides application designers for choosing meaningful sensor subsets, but also can inspire new systems and leads to the evaluation of existing applications. Today’s technology offers a wide variety of sensors. In order to use all the data from the diversity of sensors a framework of integration is needed. Sensor fusion, fuzzy logic, and neural networks are often mentioned when dealing with problem of combing information from several sensors to get a more general picture of a given situation. The study of data fusion has been receiving considerable attention (Esteban et al., 2005; Luo & Kay, 1990). A survey of the state of the art in sensor fusion for robotics can be found in (Hackett & Shah, 1990). Henderson and Shilcrat (Henderson & Shilcrat, 1984) introduced the concept of logic sensor that defines an abstract specification of the sensors to integrate in a multisensor system. The recent developments of micro electro mechanical sensors (MEMS) with unwired communication capabilities allow a sensor network with interesting capacity. This technology was applied in several applications (Arampatzis & Manesis, 2005), including robotics. Cheekiralla and Engels (Cheekiralla & Engels, 2005) propose a classification of the unwired sensor networks according to its functionalities and properties. This paper presents a development of a sensor classification scheme based on the frequency spectrum of the signals and on a statistical metrics. Bearing these ideas in mind, this paper is organized as follows. Section 2 describes briefly the robotic system enhanced with the instrumentation setup. Section 3 presents the experimental results. Finally, section 4 draws the main conclusions and points out future work. | por |
| dc.identifier.doi | 10.5772/9321 | pt_PT |
| dc.identifier.isbn | 978-953-307-090-2 | |
| dc.identifier.uri | http://hdl.handle.net/10400.22/4114 | |
| dc.language.iso | eng | por |
| dc.peerreviewed | yes | por |
| dc.publisher | InTech | por |
| dc.relation.ispartofseries | Robot Manipulators New Achievements; Cap. 17 | |
| dc.relation.publisherversion | http://www.intechopen.com/books/robot-manipulators-new-achievements/a-sensor-classification-strategy-for-robotic-manipulators | por |
| dc.title | A sensor classification strategy for robotic manipulators | por |
| dc.type | book part | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 328 | por |
| oaire.citation.startPage | 315 | por |
| oaire.citation.title | Robot Manipulators New Achievements | por |
| person.familyName | Tenreiro Machado | |
| person.givenName | J. A. | |
| person.identifier.ciencia-id | 7A18-4935-5B29 | |
| person.identifier.orcid | 0000-0003-4274-4879 | |
| person.identifier.rid | M-2173-2013 | |
| person.identifier.scopus-author-id | 55989030100 | |
| rcaap.rights | openAccess | por |
| rcaap.type | bookPart | por |
| relation.isAuthorOfPublication | 82cd5c17-07b6-492b-b3e3-ecebdad1254f | |
| relation.isAuthorOfPublication.latestForDiscovery | 82cd5c17-07b6-492b-b3e3-ecebdad1254f |