Recent research on highly distributed control methods for complex systems have produced a series of philosophies based on negotiation, that bring together process engineering with computer science. Among these control philosophies, the ones based on multi-agent systems (MAS) have become especially relevant. Furthermore, among a number of MAS implementations, those that use different types of agents at different conceptual/ managerial levels are commonly applied. However, these MAS models have the drawback of an excessive dependence on up-to-date information about the products and other elements that move within the system. A new technology has come out that can help solve this problem: radio-frequency identification enhanced information management systems (RFID-IMS). One of these is Auto-ID/EPCglobal technology. This paper shows how a MAS model can be used for controlling a machining system incorporating RFID-IMS technology. The resulting system becomes an RFID-enhanced intelligent manufacturing system (RFID-IMS II).

The Integral Traceability System for tracking and tracing the milk samples used in quality control was checked for one year while monitoring 526 milk samples from sheep's, goats' and cows'. This system includes a customized automated cooler for carrying samples with a smart sensor inside to store the data collected during the process, and a dongle to transfer the collected data to a computer to be further analysed. The technologies combined to record and trace milk samples on trips from farms to the laboratory (e.g. microcontrollers, sensors, radio frequency identification and global positioning system) were linked. This system allowed us to objectively know the duration of the sampling route and the temperature and time conditions of samples travelled in until they were analysed in an official dairy laboratory. These conditions ensured that the baseline milk quality was preserved, and was therefore adequate according to both European regulations and the price set to be paid for quality. Hardware and software prototypes worked successfully under the real study conditions, and this system may be proposed to become a reference method in the dairy sector.ARTICLE HISTORY

Recent research on highly distributed control methods has produced a series of new philosophies based on negotiation, which bring together the process engineering with computer science. Among these control philosophies, the ones based on Multi-agent Systems (MAS) have become especially relevant to address complex tasks and to support distributed decision making in asset management, manufacturing, and logistics. However, these MAS models have the drawback of an excessive dependence on up-to-date field information. In this work, a theoretical and experimental MAS, called MAS-DUO, is presented to test new strategies for managing handling operations supported by feedback coming from radio frequency identification (RFID) systems. These strategies have been based on a new distributed organization model to enforce the idea of division between physical elements and information and communication technologies (ICT) in the product scheduling control. This division in two platforms simplifies the design, the development, and the validation of the MAS, allowing an abstraction and preserving the independency between platforms. The communication between both platforms is based on sharing the parameters of the Markov reward function. This function is mainly made up of the field information coming from the RFID readers incorporated as the internal beliefs of the agent. The proposed MAS have been deployed on the Ciudad Real Central Airport in Spain in order to dimension the ground handling resources.

A current trend in automotive research is autonomous driving. For the proper testing and validation of automated driving functions a reference vehicle state is required. Global Navigation Satellite Systems (GNSS) are useful in the automation of the vehicles because of their practicality and accuracy. However, there are situations where the satellite signal is absent or unusable. This research work presents a methodology that addresses those situations, thus largely reducing the dependency of Inertial Navigation Systems (INSs) on the SatNav. The proposed methodology includes (1) a standstill recognition based on machine learning, (2) a detailed mathematical description of the horizontation of inertial measurements, (3) sensor fusion by means of statistical filtering, (4) an outlier detection for correction data, (5) a drift detector, and (6) a novel LiDAR-based Positioning Method (LbPM) for indoor navigation. The robustness and accuracy of the methodology are validated with a state-of-the-art INS with Real-Time Kinematic (RTK) correction data. The results obtained show a great improvement in the accuracy of vehicle state estimation under adverse driving conditions, such as when the correction data is corrupted, when there are extended periods with no correction data and in the case of drifting. The proposed LbPM method achieves an accuracy closely resembling that of a system with RTK.

No abstract