This paper presents a feed-forward approach to reduce sloshing dynamics in liquid handling robotic systems. According to our solution, the dynamics of a liquid into an open vessel manipulated by a robot can be described by means of a spherical pendulum mechanical model. By doing this, the sloshing problem can be addressed as a vibration suppression problem for a second order system. More in details, the pendulum model is utilized to tune an exponential filter which shapes the reference trajectory for the robot, thus achieving a sloshing-free motion of the liquid inside the vessel.

A remark related to our recent paper is made. Using a counterexample we show that we need more strict assumptions with respect to those used to prove that if a system is diagnosable in a decentralized setting, then it is also diagnosable in a centralized one. Specifically, we require that the central diagnoser sees all the events that can be seen by all the local diagnosers, and that the central diagnoser can distinguish all observable events that the local diagnosers can distinguish on their own.

This work proposes a reactive supervisory control for a team of heterogeneous robots capable of autonomous navigation and cooperation in unstructured and hostile environment. The supervisor is built using classical theories of discrete event systems (DES), but it differs for its reactive capabilities. This allows to have an online evaluation and optimization for dynamic events and environments. The supervisor (high level control) interacts directly with low level control, taking into account dynamic and kinematic of the agents. The specific robotic platform includes both aerial (UAV) and ground robots but the results and applications are absolutely general.

In this paper, we propose an approach to the diagnosis of Petri nets in a decentralized setting that combines the decentralized scheme for automata presented by Debouk with the diagnosis approach for Petri nets based on the notion of basis markings and justifications presented by some of the authors of this paper. The decentralized architecture that we use is composed of a set of sites communicating their diagnosis information with a coordinator that is responsible for detecting the occurrence of failures in the system. In particular, we define three protocols that differ in the amount of information exchanged between the local sites and the coordinator and the rules adopted by the coordinator to compute the global diagnosis states. Finally, we prove that, as in the case of automata, diagnosability is strictly related to the presence of failure ambiguous strings.

In the past years within the steel industry a growing emphasis has been put on fully automatic production lines with a high degree of reliability. The main challenge in designing of these systems is to foster the production process efficiency while improving operators working condition and maintaining the same proper safety level. The CESAR project aims at elaborating a Reference Technology Platform for the cost effective development and validation of safety related embedded systems. In the CESAR project Danieli Automation SpA, in collaboration with the University of Bologna and University of Trieste, has put focus on the development process of a safety system for steel industry robotic applications. In this paper the achieved results related to the exploiteness and usability of the RTP during the development process are reported.

Our objective is to develop a general and versatile approach for building structured formal models of complex automated systems in order to facilitate their control and diagnosis. For this purpose, we present a methodology that builds the complete model of a system by composing models of the individual hardware components, their physical coupling, and the associated control logic. We choose to employ a hierarchical decomposition that separates the control logic into a high level that manages the sequence of control actions and a low level that implements the control actions. The low level is composed of control logic and physical components (sensors and actuators) grouped into a device. In order to capture the physical constraints between the components in a device, we propose the notion of a physical constraint automaton, which is composed with the generic component automata to generate the complete model of the device. We also show how the methodology allows the introduction of component faults into the overall model. The effectiveness of the proposed approach is demonstrated on a micro flexible manufacturing system.

In this work a general partitioning of logic control design strategies into two main approaches, cooperative and hierarchical, is proposed and some lines for a comparison are drawn. In the authors' opinion, the cooperative approach basically collects methods inspired by IEC 61499 and agent paradigms while, the hierarchical approach, generally collects design procedures inspired by IEC 61131. Among the elements of the latter category, particular attention is devoted to the design methodology based on the Generalized Actuator framework, recently proposed by the authors. A case-study is considered to derive some starting considerations on the properties of the considered approaches.

A new methodology for the unsupervised classification of hyperspectral images is proposed. Based on swarm intelligence, it addresses simultaneously two different issues which are: 1) the estimation of the cluster parameters; and 2) the detection of the best discriminative bands. For such purpose, it optimizes jointly two different criteria, which are the log likelihood function and the Bhattacharyya statistical distance between classes. Experimental results show that, despite the completely unsupervised nature of the proposed methodology, very encouraging performances in terms of classification accuracy can be achieved.

In this paper, we present a new methodology for clustering hyperspectral images. It aims at simultaneously solving the following three different issues: 1) estimation of the class statistical parameters; 2) detection of the best discriminative bands without requiring thea priorisetting of their number by the user; and 3) estimation of the number of data classes characterizing the considered image. It is formulated within a multiobjective particle swarm optimization (MOPSO) framework and is guided by three different optimization criteria, which are the log-likelihood function, the Bhattacharyya statistical distance between classes, and the minimum description length (MDL). A detailed experimental analysis was conducted on both simulated and real hyperspectral images. In general, the obtained results show that interesting classification performances can be achieved by the proposed methodology despite its completely unsupervised nature.

Aim of this work is to present a rapid-prototyping procedure for logic control of automated manufacturing systems. The presented approach relies on the Generalized Actuator concept, by which, during the design phase, it is possible to keep policies separated from actuation mechanisms. This approach is helpful in rapid prototyping because it allows the design of modular/hierarchical control algorithms, leading also to the possibility of validating the controller following a modular and hierarchical strategy. The effectiveness of the proposed approach has been shown on a micro flexible manufacturing system.