Ruggero Frezza’s research while affiliated with University of Padua and other places

Method for coordinating a plurality of sensors, wherein a plurality of networked computers control the sensors for the purpose of executing one or more services requested through the network. In order to coordinate with one another, the computers exchange information over the network in a manner such that each networked computer knows the characteristics of and the services executed by the other computers. Subsequently, the computers providing the same service execute the same configuration algorithm that defines the tasks to be carried out by each computer as a function of the characteristics of the computers providing said same service. Thus, the computers get to the same conclusions as to the tasks to be carried out, and control the sensors based on the tasks defined by the configuration algorithm.

The detection of events in video streams is a central task in the automatic vision paradigm, and spans heterogeneous fields of application from the surveillance of the environment, to the analysis of scientific data. Actually, although well captured by intuition, the definition itself of event is somewhat hazy and depending on the specific application of interest. In this work, the approach to the problem of event detection is different in nature. Instead of defining the event and searching for it within the data, a normality space of the scene is built from a chosen learning sequence The event detection algorithm works by projecting any newly acquired image onto the normality space so as to calculate a distance from it that represents the innovation of the new frame, and defines the metric for triggering an event alert.

We address the problem of following an unknown planar contour with a nonholonomic vehicle based on visual feedback. The control task is to keep a point of the vehicle as close as possible to the contour for a choice of norm. A camera mounted on-board the vehicle provides measurements of the contour. We formulate the problem and compute the control law in a moving reference frame modeling the evolution of the contour as seen by an observer sitting on the vehicle. The result is an on-line path planning strategy and a predictive control law which leads the vehicle to land softly on the unknown curve. Depending on the choice of the tracking criterion, the controller can exhibit non-trivial behaviors including automatic maneuvering.

The development of a motorcycle driver for virtual prototyping applications is discussed. The driver is delivered with a commercial multibody code as a tool for performing closed-loop maneuvers with virtual motorcycle models. The closed-loop controller is developed with a qualitative analysis of how a human rider controls a motorcycle. The analysis concerns handling and maneuverability, which are relevant for real and virtual vehicle performance evaluation. A motorcycle model for control design and a controller structure are developed. The model is based on a mathematical representation of common-sense rules of motorcycle riding. The virtual rider is then tested in various operating conditions to assess whether the control requirements are achieved. Criteria for evaluating driver models are briefly discussed

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We introduce a novel perspective for viewing the ego-motion reconstruction problem as the estimation of the state of a dynamical system having an implicit measurement constraint and unknown inputs. Such a system happens to be linear, but it is defined on a space (the Essential Manifold) which is not a linear (vector) space. We propose two recursive schemes for performing the estimation task: the first consists in flattening the space and solving a nonlinear estimation problem on the flat (euclidean) space. The second consists in viewing the system as embedded in a larger euclidean space, and solving at each step a linear estimation problem on a linear space, followed by a projection onto the Essential Manifold. Both schemes output motion estimates together with the joint second order statistics of the estimation error, which can be used by any structure from motion module which incorporates motion error [18, 22] in order to estimate 3D scene structure. Experiments are presented with real and synthetic image sequences.

We explore the solutions of the driven inverted pendulum system l#0308=gsinφ-a l (t)cosτ where a l (·) is a bounded lateral acceleration. We show that, for lateral accelerations that are constant before some initial time, an inverted trajectory always exists and remains within a diamond shaped region in the state space. Functional analytic techniques are also developed to provide further insight into the nature of the inverted pendulum trajectories. Associated to the driven inverted pendulum is a time varying linear system. We show that this system always possesses an exponential dichotomy, allowing for the development of a successive approximation algorithm for finding the desired inverted pendulum trajectory. We show that the curve obtained from one iteration of this algorithm is a very good estimate of the required inverted trajectory. As that curve is obtained by filtering the quasi-static angle trajectory by a noncausal time varying low pass filter with weighting function with a shape similar to h(t) = exp−α 0 |t|, we find that the current pendulum angle is influenced by the values of the lateral acceleration within only a few seconds of the current time. These results are important as the driven inverted pendulum is a common susbsystem in systems ranging from motorcycles and bicycles to rockets and aircaft.

Methods for solving multi target tracking and data association problems in presence of clutter and occlusions are based on models that describe the target dynamics and the measurements statistics. Most often the dynamics of the targets are assumed to be independent from each other. In many applications, however, the motion of the targets may be coordinated. We introduce a statistical concept of shape, or coordination, in terms of invariants w.r.t. the motion of the targets. Assuming that the rules of coordination may slowly change over time, we study the interplay among the shape and the target dynamics.

In this paper, we study the trajectory space of the PVTOL aircraft. We show that, due to the non-minimum phase nature of the system, more aggressive trajectories may be tracked with respect to the simplified differentially flat model. Given bounded C²trajectories of the center of gravity, we show that there exists a bounded roll trajectory which implements them. We compute an approximation of such roll trajectory using a Newton method for nonlinear optimization based on a trajectory tracking projection operator.

Recent experiments on frogs and rats, have led to the hypothesis that sensory-motor systems are organized into a finite number of linearly combinable modules; each module generates a motor command that drives the system to a predefined equilibrium. Surprisingly, in spite of the infiniteness of different movements that can be realized, there seems to be only a handful of these modules. The structure can be thought of as a vocabulary of "elementary control actions". Admissible controls, which in principle belong to an infinite dimensional space, are reduced to the linear vector space spanned by these elementary controls. In the present paper we address some theoretical questions that arise naturally once a similar structure is applied to the control of nonlinear kinematic chains. First of all, we show how to choose the modules so that the system does not loose its capability of generating a "complete" set of movements. Secondly, we realize a "complete" vocabulary with a minimal number of elementary control actions. Subsequently, we show how to modify the control scheme so as to compensate for parametric changes in the system to be controlled. Remarkably, we construct a set of modules with the property of being invariant with respect to the parameters that model the growth of an individual. Robustness against uncertainties is also considered showing how to optimally choose the modules equilibria so as to compensate for errors affecting the system. Finally, the motion primitive paradigm is extended to locomotion and a related formalization of internal (proprioceptive) and external (exteroceptive) variables is given.