Stefano Panzieri

An outdoor navigation system using GPS and inertial platform

The use of global positioning system (GPS) in outdoor localization is quite a common solution in large environments where no other reference is available and there are not so demanding positioning requirements. Of course, fine motion without the use of an expensive differential device is not an easy task, even now that available precision has been greatly improved as the military encoding has been removed. In this paper we present a localization algorithm based on Kalman filtering that tries to fuse information coming from an inexpensive single GPS with inertial data and map-based data. The algorithm is able to produce an estimated configuration for the robot that can be successfully fed back in a navigation system. Some experiments show difficulties and possible solutions of this sensor fusion problem.

Urban traffic flow forecasting through statistical and neural network bagging ensemble hybrid modeling

In this paper we show a hybrid modeling approach which combines Artificial Neural Networks and a simple statistical approach in order to provide a one hour forecast of urban traffic flow rates. Experimentation has been carried out on three different classes of real streets and results show that the proposed approach outperforms the best of the methods it puts together.

Agent-based input–output interdependency model

The modeling and analysis of critical infrastructures and their interdependencies are essential to discovering hidden vulnerabilities and the related threats to national and international security. Over the past few years, several approaches have been proposed to address this problem. The so-called holistic approaches are relatively abstract, but are easily validated using real economic data. Other approaches based on agent-based models provide deeper views of the interdependencies existing between subsystems of different infrastructures. However, agent-based models are often difficult to validate because quantitative data of the appropriate granularity may not be available. This paper presents an agent-based input–output inoperability model designed to overcome the limitations of the holistic and agent-based paradigms. In order to provide a detailed and expressive framework, the exchange of resources between infrastructures is explicitly modeled while inoperability becomes an internal parameter. Nevertheless, the model is easily transformed into a fine-grained, input–output inoperability model whose coefficients can be obtained based on real data.

Fuzzy dynamic input–output inoperability model

Abstract This paper describes an extension of the input–output inoperability model (IIM) that accommodates uncertain and vague data. In the resulting “fuzzy version” of the dynamic IIM model (FD-IIM), the level of inoperability of each infrastructure and the Leontief coefficients are specified using fuzzy variables that express expert knowledge about infrastructure interdependences. An important result is that, under certain conditions, solution convergence for the fuzzy model can be inferred based on the stability properties of the “crisp” (non-fuzzy) version of the system of interest. A case study involving the Italian critical infrastructure is used to demonstrate the feasibility and utility of the approach.

An Iterative Learning Controller for Nonholonomic Mobile Robots

We present an iterative learning controller that applies to nonholo nomic mobile robots, as well as other systems that can be put in chained form. The learning algorithm exploits the fact that chained- form systems are linear under piecewise-constant inputs. The pro posed control scheme requires the execution of a small number of experiments to drive the system to the desired state in finite time, with nice convergence and robustness properties with respect to model ing inaccuracies as well as disturbances. To avoid the necessity of exactly reinitializing the system at each iteration, the basic method is modified so as to obtain a cyclic controller, by which the system is cyclically steered through an arbitrary sequence of states. As a case study, a carlike mobile robot is considered. Both simulation and experimental results are reported to show the performance of the method.

Improving Resilience of Interdependent Critical Infrastructures via an On-Line Alerting System

This paper illustrates the activities under development within the FP7 EU MICIE project. The project is devotedto design and implement an on-line alerting system, able toevaluate, in real time, the level of risk of interdependent Critical Infrastructures (CIs). Such a risk is generated by undesired events and by the high level of interconnection of the different infrastructures. Heterogeneous models are under development to perform short term predictions of the Quality of Service (QoS) of each CI according to the QoS of the others, to the level of interdependency among the Infrastructures, and according to the undesired events identified in the reference scenario.

Modelling critical infrastructure via a mixed holistic reductionistic approach

Infrastructure systems and their interaction mechanisms can be interpreted following a multitude of different approaches, depending on different perspectives and levels of abstraction. In this paper, we propose a mixed approach, in which different representations of the same infrastructure is used in order to define the interdependencies and interactions among infrastructures and their elements. We explicitly introduce into our model the three concepts of: infrastructure, component and interaction; and model, on one hand, intra-domain dependencies with a reductionistic approach and, on the other hand, inter-domain dependencies mainly using a holistic vision.

Repositioning control of a two-link flexible arm by learning

A finite dimensional algorithm is presented which by trial searches for a control law is able to move a two-link flexible arm between two given equilibrium configurations in a finite time interval. Using a singular perturbation analysis, the possibility of considering the system as a linear perturbed one is shown, allowing the use of a linear learning algorithm which is able to reject all the nonlinear disturbances. The effectiveness of this algorithm is proven by theoretical arguments and experimental results. Robustness with respect to unmodeled high frequency dynamics is also addressed.

Distributed synchronization under uncertainty: A fuzzy approach

In this paper the synchronization of a network of identical systems with fuzzy initial conditions is introduced, as a convenient framework to obtain a shared estimation of the state of a system based on partial and distributed observations, in the case where such a state is affected by ambiguity and/or vagueness. After discussing the synchronization of crisp systems, providing a criteria to find the information sharing law that lets the network converge to a shared trajectory, Discrete-Time Fuzzy Systems (DFSs) are introduced as an extension of scalar fuzzy difference equations. Besides providing a stability condition for a general DFS, in the linear case it is proven that, under a non-negativity assumption for the coefficients of the system, the fuzzyfication of the initial conditions does not compromise the stability of the crisp system. A framework for the synchronization of arrays of linear DFS is then introduced, proving that the crisp synchronization is a particular case of the proposed approach. Finally, a case study in the field of Critical Infrastructure Protection is provided.

A joint routing and localization algorithm for emergency scenario

Wireless Sensor Networks (WSNs) have been adopted in a wide range of industrial and consumer applications such as environmental or industrial monitoring, health control, or military systems, for a decade. Main issues in WSN are represented by the large number of sensor nodes, multi-hop communication approach, and needs for an efficient use of available limited sensor energy. In this work an emergency scenario is considered and a WSN is used as a communication infrastructure. To limit the power consumption, the WSN is activated only when the emergency occurs. Few mobile nodes (the rescuers) are moving in the area. In these critical network scenarios, adaptive routing is essential to ensure reliable communication with first responders. At the same time, localizing both WSN nodes and moving nodes is an essential issue for routing the rescuers towards victims. In this paper, the authors jointly tackle routing and localization problems for reducing the network signaling communication as much as possible, which is the most power-consuming operation in WSNs. In particular, it is proposed a distributed localization algorithm, based on a ranging technique, designed by mapping the localization into a stochastic estimation problem for systems with uncertainties.