Mechatronics for the Design of Inspection Robotic Systems

Recent trends show how mobile robots are being widely used in security and inspection tasks. This chapter reports the requirements, characteristics, and development of mobile robotic systems for security and inspection tasks to demonstrate the feasibility of mechatronic solutions for inspection of sites of interest. The development of such systems can be exploited as a modular plug-in kit to be installed on a mobile system, with the aim to be used for inspection and monitoring, introducing high efficiency, quality, and repeatability in the addressed sector. The interoperability of sensors with wireless communication constitutes a smart sensor toolkit and a smart sensor network with powerful functions to be used efficiently for inspection purposes. A tele-operated robot will be taken as case of study; it is controlled by mobile phone and equipped with internal and external sensors, which are efficiently managed by the designed mechatronic control scheme

analysis and mechanical design solutions for sit to stand assisting devices

Sit-to-stand can be considered the most common daily-life activity and it can be defined as a change of posture, in which the base of support is transferred from the seat to the feet. It requires both voluntary movement of different body segments that contribute to the equilibrium and control during an important displacement of the Centre Of Gravity of the body. This activity can be considered of crucial importance for elderly and people with reduced mobility to achieve minimal independence in daily-life at home. In this study, we present and compare three design solutions for the support mechanisms to be used in assisting mechatronic devices. The reported solutions and considerations are supported by experimental activity, which was carried out during trials to track and record trajectories and the orientation of the trunk of the body during the sit-to-stand

Cloud e-learning for mechatronics: CLEM

his paper describes results of the CLEM project, Cloud E-learning for Mechatronics. CLEM is an example of a domain-specific cloud that is especially tuned to the needs of VET (Vocational, Education and Training) teachers. An interesting development has been the creation of remote laboratories in the cloud. Learners can access such laboratories to support their practical learning of mechatronics without the need to set up laboratories at their own institutions. The cloud infrastructure enables multiple laboratories to come together virtually to create an ecosystem for educators and learners. From such a system, educators can pick and mix materials to create suitable courses for their students and the learners can experience different types of devices and laboratories through the cloud. The paper provides an overview of this new cloud-based e-learning approach and presents the results. The paper explains how the use of cloud computing has enabled the development of a new method, showing how a holistic e-learning experience can be obtained through use of static, dynamic and interactive material together with facilities for collaboration and innovation

Alignment of the CMS muon system with cosmic-ray and beam-halo muons

This is the Pre-print version of the Article. The official published version of the Paper can be accessed from the link below - Copyright @ 2010 IOPThe CMS muon system has been aligned using cosmic-ray muons collected in 2008 and beam-halo muons from the 2008 LHC circulating beam tests. After alignment, the resolution of the most sensitive coordinate is 80 microns for the relative positions of superlayers in the same barrel chamber and 270 microns for the relative positions of endcap chambers in the same ring structure. The resolution on the position of the central barrel chambers relative to the tracker is comprised between two extreme estimates, 200 and 700 microns, provided by two complementary studies. With minor modifications, the alignment procedures can be applied using muons from LHC collisions, leading to additional significant improvements.This work is supported by FMSR (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, and NSFC (China); COLCIENCIAS (Colombia); MSES (Croatia); RPF (Cyprus); Academy of Sciences and NICPB (Estonia); Academy of Finland, ME, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRT (Greece); OTKA and NKTH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); NRF (Korea); LAS (Lithuania); CINVESTAV, CONACYT, SEP, and UASLP-FAI (Mexico); PAEC (Pakistan); SCSR (Poland); FCT (Portugal); JINR(Armenia, Belarus, Georgia, Ukraine, Uzbekistan); MST and MAE (Russia); MSTDS (Serbia); MICINN and CPAN (Spain); Swiss Funding Agencies (Switzerland); NSC (Taipei); TUBITAK and TAEK (Turkey); STFC (United Kingdom); DOE and NSF (USA)

Alignment of the CMS muon system with cosmic-ray and beam-halo muons

This is the Pre-print version of the Article. The official published version of the Paper can be accessed from the link below - Copyright @ 2010 IOPThe CMS muon system has been aligned using cosmic-ray muons collected in 2008 and beam-halo muons from the 2008 LHC circulating beam tests. After alignment, the resolution of the most sensitive coordinate is 80 microns for the relative positions of superlayers in the same barrel chamber and 270 microns for the relative positions of endcap chambers in the same ring structure. The resolution on the position of the central barrel chambers relative to the tracker is comprised between two extreme estimates, 200 and 700 microns, provided by two complementary studies. With minor modifications, the alignment procedures can be applied using muons from LHC collisions, leading to additional significant improvements.This work is supported by FMSR (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, and NSFC (China); COLCIENCIAS (Colombia); MSES (Croatia); RPF (Cyprus); Academy of Sciences and NICPB (Estonia); Academy of Finland, ME, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRT (Greece); OTKA and NKTH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); NRF (Korea); LAS (Lithuania); CINVESTAV, CONACYT, SEP, and UASLP-FAI (Mexico); PAEC (Pakistan); SCSR (Poland); FCT (Portugal); JINR(Armenia, Belarus, Georgia, Ukraine, Uzbekistan); MST and MAE (Russia); MSTDS (Serbia); MICINN and CPAN (Spain); Swiss Funding Agencies (Switzerland); NSC (Taipei); TUBITAK and TAEK (Turkey); STFC (United Kingdom); DOE and NSF (USA)