20XX
S. Toigo, B. Kasi, D. Fornasier, A. Cenedese.
A Flexible Machine/Deep Learning Microservice Architecture for Industrial Vision-Based Quality Control on a Low-Cost Device. SPIE Journal of Electronic Imaging [accepted], 20XX
Abstract:
This paper aims to delineate a comprehensive method that integrates machine vision and deep learning for quality control within an industrial setting. The innovative approach proposed in this solution leverages a microservice architecture that ensures adaptability and flexibility to different scenarios while focusing on the employment of affordable, compact hardware, and achieves exceptionally high accuracy in performing the quality control task keeping a minimal computational time. Consequently, the developed system operates entirely on a portable smart camera, eliminating the need for additional sensors like photocells and external computation, which simplifies setup and commissioning phases and reduces the overall impact on the production line. By leveraging the integration of the embedded system with the machinery, this approach offers real-time monitoring and analysis capabilities, facilitating swift detection of defects and deviations from desired standards. Moreover, the low-cost nature of the solution makes it accessible to a wider range of manufacturing enterprises, democratizing quality processes in Industry 5.0. The system has been successfully implemented and is fully operational in a real industrial environment and the experimental results obtained from this implementation are also presented in the work.
[ abstract ] [
BibTeX]
J. Giordano, A. Cenedese, A. Serrani.
A Natural Indirect Adaptive Controller for a Satellite-Mounted Manipulator. arXiv preprint,
American Control Conference (ACC) [accepted], 20XX
Abstract:
The work considers the design of an indirect adaptive controller for a satellite equipped with a robotic arm manipulating an object. Uncertainty on the manipulated object can considerably impact the overall behavior of the system. In addition, the dynamics of the actuators of the base satellite are non-linear and can be affected by malfunctioning. Neglecting these two phenomena may lead to excessive control effort or degrade performance. An indirect adaptive control approach is pursued, which allows consideration of relevant features of the actuators dynamics, such as loss of effectiveness. Furthermore, an adaptive law that preserves the physical consistency of the inertial parameters of the various rigid bodies comprising the system is employed. The performance and robustness of the controller are first analyzed and then validated in simulation.
[ abstract ] [
url] [
BibTeX]
L. Varotto, A. Cenedese, A. Cavallaro.
Active Sensing for Search and Tracking: A Review. arXiv preprint, 20XX
Abstract:
Active Position Estimation (APE) is the task of localizing one or more
targets using one or more sensing platforms. APE is a key task for search and
rescue missions, wildlife monitoring, source term estimation, and collaborative
mobile robotics. Success in APE depends on the level of cooperation of the
sensing platforms, their number, their degrees of freedom and the quality of
the information gathered. APE control laws enable active sensing by satisfying
either pure-exploitative or pure-explorative criteria. The former minimizes the
uncertainty on position estimation; whereas the latter drives the platform
closer to its task completion. In this paper, we define the main elements of
APE to systematically classify and critically discuss the state of the art in
this domain. We also propose a reference framework as a formalism to classify
APE-related solutions. Overall, this survey explores the principal challenges
and envisages the main research directions in the field of autonomous
perception systems for localization tasks. It is also beneficial to promote the
development of robust active sensing methods for search and tracking
applications.
[ abstract ] [
url] [
BibTeX]
B. Pozzan, G. Michieletto, M. Mesbahi, A. Cenedese.
An Active-Sensing Approach for Bearing-based Target Localization. American Control Conference (ACC) [accepted], 20XX
Abstract:
Characterized by a cross-disciplinary nature, the bearing-based target localization task involves estimating the position of an entity of interest by a group of agents capable of collecting noisy bearing measurements. In this work, this problem is tackled by resting both on the weighted least square estimation approach and on the active-sensing control paradigm. Indeed, we propose an iterative algorithm that provides an estimate of the target position under the assumption of Gaussian noise distribution, which can be considered valid when more specific information is missing. Then, we present a seeker agents control law that aims at minimizing the localization uncertainty by optimizing the covariance matrix associated with the estimated target position. The validity of the designed bearing-based target localization solution is confirmed by the results of an extensive Monte Carlo simulation campaign.
[ abstract ] [
url] [
BibTeX]
M. Fabris, G. Fattore, A. Cenedese.
Optimal Time-Invariant Distributed Formation Tracking for Second-Order Multi-Agent Systems. arXiv preprint, 20XX
Abstract:
This paper addresses the optimal time-invariant formation tracking problem with the aim of providing a distributed solution for multi-agent systems with second-order integrator dynamics. In the literature, most of the results related to multi-agent formation tracking do not consider energy issues while investigating distributed feedback control laws. In order to account for this crucial design aspect, we contribute by formalizing and proposing a solution to an optimization problem that encapsulates trajectory tracking, distance-based formation control, and input energy minimization, through a specific and key choice of potential functions in the optimization cost. To this end, we show how to compute the inverse dynamics in a centralized fashion by means of the Projector-Operator-based Newton's method for Trajectory Optimization (PRONTO) and, more importantly, we exploit such an offline solution as a general reference to devise a stabilizing online distributed control law. Finally, numerical examples involving a cubic formation following a straight path in the 3D space are provided to validate the proposed control strategies.
[ abstract ] [
url] [
BibTeX]
G. Michieletto, R. Oboe, A. Cenedese, .. Et al.
Tag-based Visual Odometry Estimation for Indoor UAVs Localization. arXiv preprint, 20XX
Abstract:
The agility and versatility offered by UAV platforms still encounter obstacles for full exploitation in industrial applications due to their indoor usage limitations. A significant challenge in this sense is finding a reliable and cost-effective way to localize aerial vehicles in a GNSS-denied environment. In this paper, we focus on the visual-based positioning paradigm: high accuracy in UAVs position and orientation estimation is achieved by leveraging the potentials offered by a dense and size-heterogenous map of tags. In detail, we propose an efficient visual odometry procedure focusing on hierarchical tags selection, outliers removal, and multi-tag estimation fusion, to facilitate the visual-inertial reconciliation. Experimental results show the validity of the proposed localization architecture as compared to the state of the art.
[ abstract ] [
url] [
BibTeX]
M. Perin, M. Bertoni, G. Michieletto, R. Oboe, A. Cenedese.
Trajectory Tracking for Tilted Hexarotors with Concurrent Attitude Regulation. American Control Conference (ACC) [accepted], 20XX
Abstract:
Tilted hexarotors embody a technology that remains partially unexploited in terms of its potential, especially concerning precise and concurrent position and attitude control. Focusing on these aerial platforms, we propose two control architectures that can tackle the trajectory tracking task, ensuring also the attitude regulation: one is designed resting on the differential flatness property of the system, which is investigated in the paper, and the other is a hierarchical nonlinear controller. We comparatively discuss the performance of the two control schemes, in terms of the accuracy of both the tracking control action and the attitude regulation, the input effort, and the robustness in the presence of disturbances. Numerical results reveal both the robustness of the hierarchical approach in the case of external disturbance and the accuracy of the differential flatness-based controller in unwindy conditions.
[ abstract ] [
url] [
BibTeX]
2024
M. Fabris, M.D. Bellinazzi, A. Furlanetto, A. Cenedese.
Adaptive Consensus-based Reference Generation for the Regulation of Open-Channel Networks. IEEE Access, vol. 12pp. 14423 - 14436, 2024
Abstract:
This paper deals with water management over open-channel networks (OCNs) subject to water height imbalance. The OCN is modeled by means of graph theory tools and a regulation scheme is designed basing on an outer reference generation loop for the whole OCN and a set of local controllers. Specifically, it is devised a fully distributed adaptive consensus-based algorithm within the discrete-time domain capable of (i) generating a suitable tracking reference that stabilizes the water increments over the underlying network at a common level; (ii) coping with general flow constraints related to each channel of the considered system. This iterative procedure is derived by solving a guidance problem that guarantees to steer the regulated network - represented as a closed-loop system - while satisfying requirements (i) and (ii), provided that a suitable design for the local feedback law controlling each channel flow is already available. The proposed solution converges exponentially fast towards the average consensus thanks to a Metropolis-Hastings design of the network parameters without violating the imposed constraints over time. In addition, numerical results are reported to support the theoretical findings, and the performance of the developed algorithm is discussed in the context of a realistic scenario.
[ abstract ] [
url] [
BibTeX]
2023
S. Toigo, A. Cenedese, D. Fornasier, B. Kasi.
Deep-learning based industrial quality control on low-cost smart cameras. Proc. SPIE 12749 - 16th International Conference on Quality Control by Artificial Vision (QCAV23), 2023
Abstract:
This paper aims to describe a combined machine vision and deep learning method for quality control in an
industrial environment. The innovative approach used for the proposed solution leverages the use of low-cost
hardware of reduced size, and yields extremely high evaluation accuracy and limited computational time. As a
result, the developed system works entirely on a portable smart camera. It does not require additional sensors,
such as photocells, nor is it based on external computation.
[ abstract ] [
url] [
BibTeX]
B. Pozzan, G. Giacomuzzo, M. Bruschetta, R. Carli, A. Cenedese.
Motor-level Nonlinear Model Predictive Control for a Tilting Quadrotor. IEEE Conference on Control Technology and Applications (CCTA 2023), pp. 281--286, 2023
Abstract:
This work presents a novel motor-level Nonlinear
Model Predictive Control trajectory tracking controller for an
over-actuated quadrotor with tilting propellers. The proposed
controller directly provides the motor-level commands for both
the tilting and the spinning of the propellers. Moreover, it
optimally solves the control allocation problem arising from
the system’s over-actuation taking into account the physical
constraints of the platform. Leveraging a look-ahead strategy
combined with the knowledge of the actuation limits, the
proposed solution fully exploits the vehicle capabilities and
accurately tracks the desired reference. Simulation results
show that the solution proposed outperforms a state-of-the-art
controller based on Feedback Linearization, in terms of both
trajectory tracking and robustness to unmodeled dynamics.
[ abstract ] [
url] [
BibTeX]
N. Lissandrini, L. Battistella, M. Ryll, G. Michieletto, A. Cenedese.
NAPVIG: Local Generalized Voronoi Approximation for Reactive Navigation in Unknown and Dynamic Environments. American Control Conference (ACC), pp. 28-33, 2023
Abstract:
In this paper, we propose a novel online approach
for reactive local navigation of a robotic agent, based on a
fast approximation of the Generalized Voronoi Diagram in a
neighborhood of the robot’s position. We consider the context
of an unknown environment characterized by some narrow pas-
sages and a dynamic configuration. Given the uncertainty and
unpredictability that affect the scenario, we aim at computing
trajectories that are farthest away from every obstacle: this is
obtained by following the Voronoi diagram. To ensure full au-
tonomy, the navigation task is performed relying only upon on-
board sensor measurement without any a-priori knowledge of
the environment. The proposed technique builds upon a smooth
free space representation that is spatially continuous and based
on some raw measurements. In this way, we ensure an efficient
computation of a trajectory that is continuously re-planned
according to incoming sensor data. A theoretical proof shows
that in ideal conditions the outlined solution exactly computes
the local Generalized Voronoi Diagram. Finally, we assess the
reactiveness and precision of the proposed method with realistic
real-time simulations and with real-world experiments.
[ abstract ] [
url] [
BibTeX]
C.B. Bran, P. Iob, A. Cenedese, M. Schiavo.
Non-Terminal Sliding Mode Control for a Three-Link Manipulator with Variable Mass. IEEE Conference on Control Technology and Applications (CCTA 2023), pp. 333--338, 2023
Abstract:
Robotic manipulators represent one of the most
useful tools to address repetitive tasks in the industrial en-
vironment. In the primary aluminum industry, for example,
manipulators mounted on vehicles and directly controlled by
human operators are routinely used to feed the potcells with
fluoride and improve the metal generation process. In order
to automatize these operations and limit human presence in
hazardous environments, manipulators can be equipped with
automatic motion control algorithms to perform the requested
tasks. In this work, a solution to this problem is proposed, based
on a non-singular terminal sliding mode control approach and
compared with a classical sliding mode control algorithm. The
devised solution turns out to be efficient and robust, and in the
specific case, able to take into account a non-measurable mass
variation in the manipulator links itself. Numerical simulations
with a multibody tool are employed as a first assessment and
validation of the designed controller.
[ abstract ] [
url] [
BibTeX]
J. Giordano, A. Cenedese.
Quaternion-Based Non-Singular Terminal Sliding Mode Control for a Satellite-Mounted Space Manipulator. IEEE Control Systems Letters, vol. 7pp. 2659-2664, 2023
Abstract:
In this letter, a robust control solution for a
satellite equipped with a robotic manipulator is presented.
First, the dynamical model of the system is derived based
on quaternions to describe the evolution of the attitude
of the base satellite. Then, a non-singular terminal sliding
mode controller that employs quaternions for attitude con-
trol, is proposed for concurrently handling all the degrees
of freedom of the system. Moreover, an additional adaptive
term is embedded in the controller to estimate the upper
bounds of disturbances and uncertainties. The result is
a resilient solution able to withstand unmodelled dynam-
ics and interactions. Lyapunov theory is used to prove the
stability of the controller and numerical simulations allow
assessing performance and fuel efficiency.
[ abstract ] [
url] [
BibTeX]
2022
B. Pozzan, B. Elaamery, A. Cenedese.
Non-Linear Model Predictive Control for autonomous landing of a UAV on a moving platform. IEEE Conference on Control Technology and Applications (CCTA 2022), pp. 1240-1245, 2022
Abstract:
This work proposes a real-time Model Predictive Control (MPC) solution for the landing problem of a quadrotor
on an moving platform whose dynamics is unknown.
The aerial vehicle is capable of acquiring only bearing measurements and of retrieving its attitude and elevation; its objective is to autonomously track the target and safely land over it. To perform the design of the control strategy, a fast prototyping approach is proposed, in which MATLAB is used in conjunction with ACADO toolbox in order to attain both a low development time and a computationally efficient MPC solution suitable for the on-board deployment on resource constrained hardware.
Performances are assessed by laboratory experiments with an indoor aerial platform in which the controller is implemented on an embedded device (Raspberry Pi 4) with limited computational power, carried on-board. The obtained results show that even in this scenario, the adopted approach and the ACADO generated MPC solver are able to attain real-time performances and safely completing the required task
[ abstract ] [
url] [
BibTeX]
D. Cunico, A. Cenedese, L. Zaccarian, M. Borgo.
Nonlinear modeling and feedback control of boom barrier automation. IEEE Transactions on Mechatronics, vol. 27(6), pp. 4752-4763, 2022
Abstract:
We address modeling and control of a gate access automation system. A model of the mechatronic system is derived and identified. Then, an approximate explicit feedback linearization scheme is proposed, which ensures almost linear response between the electronic driver duty cycle input and the delivered torque. A nonlinear optimization problem is solved offline to generate a feasible trajectory associated with a feedforward action, and a low-level feedback controller is designed to track it. The feedback gains can be conveniently tuned by solving a set of convex linear matrix inequalities, performing a multiobjective tradeoff between disturbance attenuation and transient response. The proposed control strategy is tested on an industrial device. The experiments show that it can effectively meet the requirements in terms of robustness, load disturbance rejection, and tracking performance.
[ abstract ] [
url] [
BibTeX]
G. Michieletto, F. Formaggio, A. Cenedese, S. Tomasin.
Robust Localization for Secure Navigation of UAV Formations under GNSS Spoofing Attack. IEEE Transactions of Automation Science and Engineering [early access], 2022
Abstract:
Nowadays, aerial formations are frequently employed in outdoor scenarios to cooperatively explore and monitor wide areas of interest. In these applications, the vehicles are often exposed to relevant security vulnerabilities, as, for instance, the alteration of navigation signals from an attacker with map counterfeiting (if not even hijacking) purposes. In this work, we focus on an Unmanned Aerial Vehicle (UAV) formation that monitors an area, wherein navigation spoofing attacks may occur. Letting the UAVs cooperate and exploiting the redundancy in the available sensing information, a distributed procedure is proposed to i) detect spoofing attacks, and ii) support the navigation in adverse conditions. The validity of the designed approach is confirmed by numerical results. Aerial vehicles for outdoor operation are generally endowed with inertial measurements, relative ranging, and GNSS sensing capability. In this work, two cascaded estimation algorithms for concurrent GNSS spoofing detection and localization in a multi-UAV scenario is proposed, to attain robust navigation in areas subject to GNSS spoofing attacks. The attack detection leverages on information theoretic tools to provide a practical threshold test by checking the multimodal measurement consistency. The localization procedures exploit a decision logic relying on measurement reliability to combine information sources that are different in nature, for UAV self-localization in both safe and under-attack conditions.
[ abstract ] [
url] [
BibTeX]
D. Cunico, A. Cenedese, L. Zaccarian, M. Borgo.
Two-degree-of-freedom Robust Feedback Control of a Sliding Gate Automation. IEEE International Conference on Advanced Motion Control (AMC 2022), pp. 370--375, 2022
Abstract:
A control strategy consisting of a feedforward action
and a robust feedback for a gate automation is presented, where
a low-cost and non-regenerative motor drive is used. A model
of the system is developed and feedback linearization is used
to compensate for the highly nonlinear dynamics of the electric
drive. To achieve good motion tracking performance we design a
smooth reference associated with a feedforward action, based on
the nominal model of the system. In addition, based on a model of
the uncertainties a robust feedback controller is tuned by solving
a set of linear matrix inequalities, combining the optimization of
a LQR cost with some pole placement constraints. Finally, we
test the proposed control strategy on an experimental device,
obtaining satisfactory results.
[ abstract ] [
url] [
BibTeX]
L. Varotto, M. Fabris, G. Michieletto, A. Cenedese.
Visual sensor network stimulation model identification via Gaussian mixture model and deep embedded features. Engineering Applications of Artificial Intelligence, vol. 114pp. 105096, 2022
Abstract:
Visual sensor networks (VSNs) constitute a fundamental class of distributed sensing systems, with unique complexity and appealing performance features, which correspondingly bring in quite active lines of research. An important research direction consists in the identification and estimation of the VSN sensing features: these are practically useful when scaling with the number of cameras or with the observed scene complexity. With this context in mind, this paper introduces for the first time the idea of Stimulation Model (SM), as a mathematical relation between the set of detectable events and the corresponding stimulated cameras observing those events. The formulation of the related SM identification problem is proposed, along with a proper network observations model, and a solution approach based on deep embedded features and soft clustering. In detail: first, the Gaussian Mixture Modeling is employed to provide a suitable description for data distribution, while an autoencoder is used to reduce undesired effects due to the so-called curse of dimensionality emerging in case of large scale networks. Then, it is shown that a SM can be learnt by solving Maximum A-Posteriori estimation on the encoded features belonging to a space with lower dimensionality. Numerical results on synthetic scenarios are reported to validate the devised estimation algorithm.
[ abstract ] [
url] [
BibTeX]
J. Giordano, M. Lazzaretto, G. Michieletto, A. Cenedese.
Visual Sensor Networks for Indoor Real-time Surveillance and Tracking of Multiple Targets. Sensors, vol. 22(7), pp. 1--28, 2022
Abstract:
The recent trend toward the development of IoT architectures has entailed the transformation of the standard camera networks into smart multi-device systems, capable of acquiring, elaborating, exchanging data and, often, dynamically adapting to the environment. Along this line, this work proposes a novel distributed solution that guarantees the real-time monitoring of 3D indoor structured areas and also the tracking of multiple targets, by employing an heterogeneous visual sensor network composed of both fixed and Pan-Tilt-Zoom (PTZ) cameras. Specifically, the fulfilment of the twofold mentioned goal is ensured through the implementation of a suitable optimization procedure regarding the PTZ devices controllable parameters, inspired by game theory. Numerical simulations in realistic scenarios confirm the capability of the outlined strategy of securing the simultaneous tracking of several targets, maintaining the total coverage of the surveilled area. In particular, the proposed solution results to be effective in dealing with conflicting goals like achieving a good tracking precision while obtaining high resolution frames of the tracked subjects.
[ abstract ] [
url] [
BibTeX]
2021
M. Fabris, G. Michieletto, A. Cenedese.
A General Regularized Distributed Solution for System State Estimation from Relative Measurements. IEEE Control Systems Letters, vol. 6pp. 1580--1585, 2021
Abstract:
This work presents a novel general regularized distributed solution for the state estimation problem in networked systems. Resting on the graph-based representation of sensor networks and adopting a multivariate least-squares approach, the designed solution exploits the set of the available inter-sensor relative measurements and leverages a general regularization framework, whose parameter selection is shown to control the estimation procedure convergence performance. As confirmed by the numerical results, this new estimation scheme allows (i) the extension of other approaches investigated in the literature and (ii) the convergence optimization in correspondence to any (undirected) graph modeling the given sensor network.
[ abstract ] [
url] [
BibTeX]
G. Michieletto, A. Cenedese, D. Zelazo.
A Unified Dissertation on Bearing Rigidity Theory. IEEE Transactions on Control of Network Systems, vol. 8(4), pp. 1624--1636, 2021
Abstract:
This work focuses on bearing rigidity theory, namely the branch of knowledge investigating the structural properties necessary for multi-element systems to preserve the inter-unit bearings under deformations. The contributions of this work are twofold. The first one consists in the development of a general framework for the statement of the principal definitions and properties of bearing rigidity. We show that this approach encompasses results existing in the literature, and also provides a systematic approach for studying bearing rigidity on any differential manifold in SE(3)^n, where n is the number of agents.The second contribution is the derivation of a general form of the rigidity matrix, a central construct in the study of rigidity theory. We provide a necessary and sufficient condition for the infinitesimal rigidity of a bearing framework as a property of the rank of the rigidity matrix. Finally, we present two examples of multi-agent systems not encountered in the literature and we study their rigidity properties using the developed methods
[ abstract ] [
url] [
BibTeX]
R. Opromolla, F. Branz, A. Francesconi, A. Cenedese, R. Antonello, P. Iob, Z. Pavanello, D. Vertuani, A. Et.
Chaser-Robotic Arm Combined Control and Optical Relative Navigation for Space Target Capture. 26th Conference of the Italian Association of Aeronautics and Astronautics (AIDAA 2021), pp. 1-11, 2021 [
BibTeX]
Z. Pavanello, F. Branz, A. Francesconi, A. Cenedese, R. Antonello, F. Basana, P. Iob, A. Et.
Combined control and navigation approach to the robotic capture of space vehicles. 72nd International Astronautical Congress (IAC), pp. 1-13, 2021
Abstract:
The potentialities of In-Orbit Servicing (IOS) to extend the operational life of satellites and the need to implement
Active Debris Removal (ADR) to effectively tackle the space debris problem are well known among the space community. Research on technical solutions to enable this class of missions is thriving, also pushed by the development of
new generation sensors and control systems. Among private companies, space agencies and universities, the European
Space Agency (ESA) has been developing technologies in this field for decades. Several solutions have been proposed
over the years to safely capture orbital objects, the majority relying on robotic systems. A promising option is the
employment of an autonomous spacecraft (chaser) equipped with a highly dexterous robotic arm able to perform the
berthing with a resident space object. This operation poses complex technical challenges both during the approach
phase and after contact. In this respect, the design of an effective, reliable, and robust Guidance, Navigation and
Control (GNC) system, for which several algorithmic architectures and hardware configurations are possible, plays a
key role to ensure safe mission execution.
This work presents the outcomes of a research activity performed by a consortium of universities under contract
with ESA with the goal to develop the navigation and control subsystems of a GNC system for controlling a chaser
equipped with a redundant manipulator. Both the final approach until capture and the target stabilization phase after
capture are considered in the study. The proposed solution aims at the implementation of a combined control strategy.
Robust control methods are adopted to design control laws for the uncertain, nonlinear dynamics of the chaser and
of the complete chaser–target stack after capture. Visual–based solutions, i.e., relying on active/passive electro–
optical sensors, are selected for relative navigation. A complete sensor suite for relative and absolute navigation
is part of the GNC system, including transducers for robot joint measurements. To properly validate the proposed
solutions, a complete numerical simulator has been developed. This software tool allows to thoroughly assess the
system performance, accounting for all the relevant external disturbances and error sources. A realistic synthetic
image generator is also used for relative navigation performance assessment. This paper presents the design solutions
and the results of preliminary numerical testing, considering three mission scenarios to prove the flexibility of the
solution and its applicability to a wide range of operational cases.
[ abstract ] [
url] [
BibTeX]
B. Pozzan, G. Michieletto, A. Cenedese, D. Zelazo.
Heterogeneous Formation Control: a Bearing Rigidity Approach. IEEE Conference on Decision and Control (CDC2021), pp. 6451--6456, 2021
Abstract:
This work proposes a formation control law for multi-agent systems whose components are heterogeneous in terms of actuation capabilities, but at the same time are all able to retrieve bearing information w.r.t. some neighbors in the group. The designed controller exploits the results of the bearing rigidity theory deriving from the modeling of heterogeneous formations as generalized frameworks. The outlined solution is compared with a leader-follower combination of existing rigidity based homogeneous formation controllers in order to highlight the easy tuning, the flexibility w.r.t. the formation composition, and the increased efficiency of the new proposed control approach. A sufficient condition ensuring the convergence of the designed controller is also given.
[ abstract ] [
url] [
BibTeX]
R. Fantinel, A. Cenedese, G. Fadel.
Hybrid Learning Driven by Dynamic Descriptors for Video Classification of Reflective Surfaces. IEEE Transactions on Industrial Informatics, vol. 17(12), pp. 8102--8111, 2021
Abstract:
Visual inspection has recently gained increasing importance in the manufacturing industry and is often addressed by means of learning methodologies applied to data obtained from specific lighting and camera system setups. The industrial scenario becomes particularly challenging when the inspection regards reflective objects, which may affect both the data acquisition and the classification decision process, thus limiting the overall performance. In this context, we observe that the dynamics of the reflected light is the key aspect to characterize these surfaces and needs to be accurately exploited to improve the performances of the learning algorithms. To this aim, we propose a combined model-based and data-driven approach designed to detect defects on the reflective surfaces of industrial products, captured as video sequences under coaxial structured illumination. Specifically, a tunable spatial-temporal descriptor of the evolution of the reflected light (Dynamic Evolution of the Light, DEL) is designed and employed within a Hybrid Learning (HL) framework, where the learning process of a Convolutional Neural Network (CNN) is driven by the model-based descriptor. This approach is also extended by adopting the similar in nature descriptor Dynamic Image. The proposed HL solutions are validated against a whole spectrum of state-of-the-art learning procedures and different descriptors. Experiments run on a dataset coming from an actual industrial scenario confirm the ability of DEL to accurately characterize reflective surfaces and the validity of the HL method, which shows remarkably better performance in fault detection even with respect to modern 3D- CNNs with comparable computational effort.
[ abstract ] [
url] [
BibTeX]
B. Elaamery, M. Pesavento, T. Aldovini, N. Lissandrini, G. Michieletto, A. Cenedese.
Model Predictive Control for Cooperative Transportation with Feasibility-Aware Policy. Robotics, vol. 10(3), pp. 84, 2021
Abstract:
The transportation of large payloads can be made possible with Multi-Robot Systems(MRS) implementing cooperative strategies. In this work, we focus on the coordinated MRS trajectory planning task exploiting a Model Predictive Control (MPC) framework addressing both the actingrobots and the transported load. In this context, the main challenge is the possible occurrence of a temporary mismatch among agents’ actions with consequent formation errors that can cause severe damage to the carried load. To mitigate this risk, the coordination scheme may leverage a leader–follower approach, in which a hierarchical strategy is in place to trade-off between the task accomplishment and the dynamics and environment constraints. Nonetheless, particularly in narrow spaces or cluttered environments, the leader’s optimal choice may lead to trajectories that are infeasible for the follower and the load. To this aim, we propose a feasibility-aware leader–follower strategy, where the leader computes a reference trajectory, and the follower accounts for its own and the load constraints; moreover, the follower is able to communicate the trajectory infeasibility to the leader, which reacts by temporarily switching to a conservative policy. The consistent MRS co-design is allowed by the MPC formulation, for both the leader and the follower: here, the prediction capability of MPC is key to guarantee a correct and efficient execution of the leader–follower coordinated action. The approach is formally stated and discussed, and a numerical campaign is conducted to validate and assess the proposed scheme, with respect to different scenarios with growing complexity.
[ abstract ] [
url] [
BibTeX]
C. Favaretto, S. Spadone, C. Sestieri, V. Betti, A. Cenedese, S. Della Penna, M. Corbetta.
Multi-band MEG signatures of BOLD connectivity reorganization during visuospatial attention. Neuroimage, 2021
Abstract:
The functional architecture of the resting brain, as measured with the blood oxygenation level-dependent functional connectivity (BOLD-FC), is slightly modified during task performance. In previous work, we reported behaviorally relevant BOLD-FC modulations between visual and dorsal attention regions when subjects performed a visuospatial attention task as compared to central fixation (Spadone et al., 2015).
Here we use magnetoencephalography (MEG) in the same group of subjects to identify the electrophysiological correlates of the BOLD-FC modulation found in our previous work. While BOLD-FC topography, separately at rest and during visual attention, corresponded to neuromagnetic Band-Limited Power (BLP) correlation in the alpha and beta bands (8-30 Hz), BOLD-FC modulations evoked by performing the visual attention task (Spadone et al. 2015) did not match any specific oscillatory band BLP modulation. Conversely, following the application of an orthogonal spatial decomposition that identifies common inter-subject co-variations, we found that attention–rest BOLD-FC modulations were recapitulated by multi-spectral BLP-FC components. Notably, individual variability of alpha connectivity between Frontal Eye Fields and visual occipital regions, jointly with decreased interaction in the Visual network, correlated with visual discrimination accuracy. In summary, task-rest BOLD connectivity modulations match multi-spectral MEG BLP connectivity.
[ abstract ] [
url] [
BibTeX]
L. Varotto, A. Cenedese.
Online and Adaptive Parking Availability Mapping: An Uncertainty-Aware Active Sensing Approach for Connected Vehicles. IEEE Intelligent Vehicles (IV2021) - workshop on Online Map Validation and Road Model Creation, pp. 31--36, 2021
Abstract:
Research on connected vehicles represents a continuously evolving technological domain, fostered by the emerging Internet of Things (IoT) paradigm and the recent advances in intelligent transportation systems. Nowadays, vehicles are platforms capable of generating, receiving and automatically act based on large amount of data. In the context of assisted driving, connected vehicle technology provides real-time information about the surrounding traffic conditions. Such information is expected to improve drivers' quality of life, for example, by adopting decision making strategies according to the current parking availability status. In this context, we propose an online and adaptive scheme for parking availability mapping. Specifically, we adopt an information-seeking active sensing approach to select the incoming data, thus preserving the onboard storage and processing resources; then, we estimate the parking availability through Gaussian Process Regression. We compare the proposed algorithm with several baselines, which attain inferior performance in terms of mapping convergence speed and adaptivity capabilities; moreover, the proposed approach comes at the cost of a very small computational demand.
[ abstract ] [
url] [
BibTeX]
L. Varotto, A. Cenedese, A. Cavallaro.
Probabilistic Radio-Visual Active Sensing for Search and Tracking. European Control Conference (ECC2021), pp. 417--422, 2021
Abstract:
Active Search and Tracking for search and rescue missions or collaborative mobile robotics relies on the actuation of a sensing platform to detect and localize a target. In this paper we focus on visually detecting a radio-emitting target with an aerial robot equipped with a radio receiver and a camera. Visual-based tracking provides high accuracy, but the directionality of the sensing domain often requires long search times before detecting the target. Conversely,radio signals have larger coverage, but lower tracking accuracy. Thus, we design a Recursive Bayesian Estimation scheme that uses camera observations to refine radio measurements. To regulate the camera pose, we design an optimal controller whose cost function is built upon a probabilistic map. Theoretical results support the proposed algorithm, while numerical analyses show higher robustness and efficiency with respect to visual and radio-only baselines.
[ abstract ] [
url] [
BibTeX]
L. Varotto, A. Cenedese.
Probabilistic RF-Assisted Camera Wake-Up through Self-Supervised Gaussian Process Regression. Mediterranean Conference on Control and Automation (MED2021), 2021
Abstract:
Research on wireless sensors represents a continuously evolving technological domain thanks to their high flexibility and scalability, fast and economical deployment, pervasiveness in industrial, civil and domestic contexts. However, the maintenance costs and the sensors reliability are strongly affected by the battery lifetime, which may limit their use. In this paper we consider a wireless smart camera, equipped with a low-energy radio receiver, and used to visually detect a moving radio-emitting target. To preserve the camera lifetime without sacrificing the detection capabilities, we design a probabilistic energy-aware controller to switch on/off the camera. The radio signal strength is used to predict the target detectability, via self-supervised Gaussian Process Regression combined with Recursive Bayesian Estimation. The automatic training process minimizes the human intervention, while the controller guarantees high detection accuracy and low energy consumption, as numerical and experimental results show.
[ abstract ] [
url] [
BibTeX]
L. Varotto, A. Cenedese.
RaViPAS - Radio-Visual Probabilistic Active Sensing. R2T2: Robotics Research for Tomorrow's Technology, 2021 [
url] [
BibTeX]
L. Varotto, A. Cenedese.
Transmitter Discovery through Radio-Visual Probabilistic Active Sensing. 25th International Conference on Methods and Models in Automation and Robotics (MMAR 2021), 2021
Abstract:
Multi-modal Probabilistic Active Sensing (MMPAS) uses sensor fusion and probabilistic models to control the perception process of robotic sensing platforms. MMPAS is successfully employed in environmental exploration, collaborative mobile robotics, and target tracking, being fostered by the high performance guarantees on autonomous perception. In this context, we propose a bi-Radio-Visual PAS scheme to solve the transmitter discovery problem. Specifically, we firstly exploit the correlation between radio and visual measurements to learn a target detection model in a self-supervised manner. Then, the model is combined with antenna radiation anisotropies into a Bayesian Optimization framework that controls the platform. We show that the proposed algorithm attains an accuracy of 92%, overcoming two other probabilistic active sensing baselines.
[ abstract ] [
url] [
BibTeX]
A. Fabris, L. Parolini, S. Schneider, A. Cenedese.
Use of probabilistic graphical methods for online map validation. IEEE Intelligent Vehicles (IV2021) - workshop on Online Map Validation and Road Model Creation, pp. 43--48, 2021
Abstract:
In the world of autonomous driving high resolution maps play a fundamental role. Such maps are highly accurate representations of the environment and are essential for all the algorithms of strategy and path planning operations.
Unfortunately it is not always possible to guarantee the total reliability of these maps and therefore it is necessary to introduce a system for its validation. In this paper we introduce a framework for validating map data at run-time based on probabilistic graphical models. Results from simulations show the capabilities of the proposed approach and highlight the need to find an appropriate balance between model accuracy and complexity.
[ abstract ] [
url] [
BibTeX]
2020
M. Fabris, G. Michieletto, A. Cenedese.
A Proximal Point Approach for Distributed System State Estimation. IFAC World Congress (IFAC2020), pp. 2702--2707, 2020
Abstract:
System state estimation constitutes a key problem in several applications involving
multi-agent system architectures. This rests upon the estimation of the state of each agent in
the group, which is supposed to access only relative measurements w.r.t. some neighbors state.
Exploiting the standard least-squares paradigm, the system state estimation task is faced in this
work by deriving a distributed Proximal Point-based iterative scheme. This solution entails the
emergence of interesting connections between the structural properties of the stochastic matrices
describing the system dynamics and the convergence behavior toward the optimal estimate. A
deep analysis of such relations is provided, jointly with a further discussion on the penalty
parameter that characterizes the Proximal Point approach.
[ abstract ] [
url] [
pdf] [
BibTeX]
A. Morato, S. Vitturi, F. Tramarin, A. Cenedese.
Assessment of Different OPC UA Implementations for Industrial IoT-based Measurement Applications. IEEE Transactions of Instrumentation and Measurements, (Early access), 2020
Abstract:
The Industrial IoT (IIoT) paradigm represents an attractive opportunity for new generation measurement applications, which are increasingly based on efficient and reliable communication systems to allow the extensive availability of continuous data from instruments and/or sensors, thus enabling real-time measurement analysis. Nevertheless, different communication systems and heterogeneous sensors and acquisition systems may be found in an IIoT-enabled measurement application, so that solutions need to be defined to tackle the issue of seamless, effective, and low-latency interoperability. A significant and appropriate solution is the Open Platform Communications (OPC) Unified Architecture (UA) protocol, thanks to its object–oriented structure that allows a complete contextualization of the information. The intrinsic complexity of OPC UA, however, imposes a meaningful performance assessment to evaluate its suitability in the aforementioned context. To this aim, this paper presents the design of a general yet accurate and reproducible measurement setup that will be exploited to assess the performance of the main open source implementations of OPC UA. The final goal of this work is to provide a characterization of the impact of this protocol stack in an IIoT-enabled Measurement System, in particular in terms of both the latency introduced in the measurement process and the power consumption.
[ abstract ] [
url] [
BibTeX]
A. Morato, S. Vitturi, F. Tramarin, A. Cenedese.
Assessment of Different OPC UA Industrial IoT solutions for Distributed Measurement Applications. International Instrumentation and Measurement technology Conference (I2MTC), 2020
Abstract:
The Industrial IoT scenario represents an interesting opportunity for distributed measurements systems, that are typically based on efficient and reliable communication systems, as well as the widespread availability of data from measurement instruments and/or sensors. The Open Platform Communications (OPC) Unified Architecture (UA) protocol is designed to ensure interoperability between heterogeneous sensors and acquisition systems, given its object-oriented structure allowing a complete contextualization of the information. Stemming from the intrinsic complexity of OPC UA, we designed an experimental measurement setup to carry out a meaningful performance assessment of its main open source implementations. The aim is to characterize the impact of the adoption of this protocol stack in a DMS in terms of both latency and power consumption, and to provide a general yet accurate and reproducible measurement setup.
[ abstract ] [
url] [
BibTeX]
A. Fabris, L. Parolini, S. Schneider, A. Cenedese.
Correlation-based approach to online map validation. IEEE Intelligent Vehicles (IV2020) - workshop on Online Map Validation and Road Model Creation, pp. 51--56, 2020
Abstract:
High-definition (HD) maps are one of the key
technologies supporting autonomous-driving vehicles (ADV).
Especially in urban scenarios, the field of view of sensors
is often limited, and HD map provides critical information
about upcoming road environmental data. Maps used for ADV
are high resolution with centimeter-level accuracy and their
correctness is fundamental when analyzing safety of upcoming
maneuvers.
This paper proposes an approach for online map validation
(OMV) based on spatial and temporal correlation of smart-
sensors. Smart sensors are capable of analyzing the validity of
regions of the map independently from one another. Results
from the sensors are then fused together over multiple regions
and time samples for providing a unified view to software
components deciding on upcoming maneuvers which areas of
the maps are consistent with sensor data and which not.
[ abstract ] [
url] [
BibTeX]
N. Lissandrini, C.K. Verginis, P. Roque, A. Cenedese, D.V. Dimarogonas.
Decentralized Nonlinear MPC for Robust Cooperative Manipulation by Heterogeneous Aerial-Ground Robots. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2020), pp. 1531--1536, 2020
Abstract:
Cooperative robotics is a trending topic nowadays
as it makes possible a number of tasks that cannot be performed
by individual robots, such as heavy payload transportation
and agile manipulation. In this work, we address the problem
of cooperative transportation by heterogeneous, manipulatorendowed robots. Specifically, we consider a generic number of
robotic agents simultaneously grasping an object, which is to be
transported to a prescribed set point while avoiding obstacles.
The procedure is based on a decentralized leader-follower
Model Predictive Control scheme, where a designated leader
agent is responsible for generating a trajectory compatible with
its dynamics, and the followers must compute a trajectory for
their own manipulators that aims at minimizing the internal
forces and torques that might be applied to the object by
the different grippers. The Model Predictive Control approach
appears to be well suited to solve such a problem, because
it provides both a control law and a technique to generate
trajectories, which can be shared among the agents. The
proposed algorithm is implemented using a system comprised
of a ground and an aerial robot, both in the robotic Gazebo
simulator as well as in experiments with real robots, where the
methodological approach is assessed and the controller design
is shown to be effective for the cooperative transportation task.
[ abstract ] [
url] [
BibTeX]
G. Michieletto, N. Lissandrini, A. Antonello, R. Antonello, A. Cenedese.
Dual Quaternion Delay Compensating Maneuver Regulation for Fully Actuated UAVs. IFAC World Congress (IFAC2020), pp. 9316--9321, 2020
Abstract:
In aerial robotics, path following constitutes a popular
task requiring a vehicle to pursue a given trajectory.
Resting upon the fulfillment of a desired time law,
trajectory tracking techniques often turn out to be
ineffective in presence of external disturbances, favoring
the adoption of maneuver regulation strategies wherein the
desired trajectory is parameterized in terms of the
path-variable. In this scenario, this work proposes a new
delay-compensating maneuver regulation controller for fully
actuated aerial vehicles, whose aim is to guarantee the
perfect tracking of a given path in the shortest time
interval. The innovative aspect of such a solution relies
on the introduction of a recovery term that compensates for
possible delays in
the task execution. In addition, the dual-quaternion
formalism is adopted to model the dynamics of the aerial
platforms allowing feedback linearize the whole system,
including both position and attitude, with a single
controller. The tests conducted in Gazebo physic simulator
show that the proposed controller outperforms the popular
trajectory tracking PID regulators.
[ abstract ] [
url] [
BibTeX]
G. Michieletto, A. Cenedese, L. Zaccarian, A. Franchi.
Hierarchical non-linear control for multi-rotor asymptotic stabilization based on zero-moment direction. Automatica, vol. 1172020
Abstract:
We consider the hovering control problem for a class of multi-rotor aerial platforms with generically oriented propellers. Given
the intrinsically coupled translational and rotational dynamics of such vehicles, we first discuss some assumptions for the
considered systems to reject moment disturbances and to balance the gravity force, which are translated into a geometric
characterization of the platforms that is usually fulfilled by both standard models and more general configurations. Hence,
we propose a control strategy based on the identification of a zero-moment direction for the applied force and the dynamic
state feedback linearization around this preferential direction, which allows to asymptotically stabilize the platform to a static
hovering condition. Stability and convergence properties of the control law are rigorously proved through Lyapunov-based
methods and reduction theorems for the stability of nested sets. Asymptotic zeroing of the error dynamics and convergence to
the static hovering condition are then confirmed by simulation results on a star-shaped hexarotor model with tilted propellers.
[ abstract ] [
url] [
pdf] [
BibTeX]
R. Antonello, F. Branz, F. Sansone, A. Cenedese, A. Francesconi.
High Precision Dual-Stage Pointing Mechanism for Miniature Satellite Laser Communication Terminals. IEEE Transactions on Industrial Electronics, 2020
Abstract:
This paper presents an innovative mechatronic design of a high-accuracy pointing mechanism for orbital laser communication terminals. The system is based on a dual-stage architecture and is miniaturized to fit nanosatellite-class spacecraft, aiming to enable optical communication on small-size space platforms. The focus is on control design aspects and on the performance assessment of an experimental prototype under emulated external environmental disturbances.
[ abstract ] [
url] [
BibTeX]
A. Favrin, V. Nenchev, A. Cenedese.
Learning to falsify automated driving vehicles with prior knowledge. IFAC World Congress (IFAC2020), pp. 15122--15127, 2020
Abstract:
While automated driving technology has achieved a tremendous progress, the
scalable and rigorous testing and verification of safe automated and autonomous driving vehicles
remain challenging. This paper proposes a learning-based falsification framework for testing the
implementation of an automated or self-driving function in simulation. We assume that the
function specification is associated with a violation metric on possible scenarios. Prior knowledge
is incorporated to limit the scenario parameter variance and in a model-based falsifier to guide
and improve the learning process. For an exemplary adaptive cruise controller, the presented
framework yields non-trivial falsifying scenarios with higher reward, compared to scenarios
obtained by purely learning-based or purely model-based falsification approaches.
[ abstract ] [
url] [
BibTeX]
R. Fantinel, A. Cenedese.
Multistep hybrid learning: CNN driven by spatial–temporal features for faults detection on metallic surfaces. Journal of Electronic Imaging, vol. 4pp. 29, 2020
Abstract:
Solutions for the quality control of metallic surfaces are proposed. Specifically, we study a deflectometric apparatus based on coaxial structured light and the related algorithmic procedure, which is able to detect the faulty surface of a sample captured by a video sequence. First, by considering the metallic surface a dynamic scene illuminated under different light conditions, we develop the descriptor residuals of linear evolution of light (RLEL) that extracts the defectiveness information starting from the movement of the object without explicitly considering the physical characteristics of the light structure. Then, leveraging on RLEL, we present a hybrid learning (HL) technique capable of overcoming the data-driven approach used in classic deep learning (DL). By exploiting a multisteps training process, we combine the model-based descriptor RLEL and a classical data-driven convolutional neural network (CNN) to obtain an unconventional gray-box CNN, which exceeds the performance of popular DL solutions such as 3-D-inception and 3-D-residual DL networks. Remarkably, HL also shows its validity in comparing the performance of the same network structures trained not in a hybrid way, namely without the injection of the model-based information given by RLEL.
[ abstract ] [
url] [
BibTeX]
A. Colotti, A. Cenedese, S. Briot, I. Fantoni, A. Goldsztejn.
Stability Analysis and Reconfiguration Strategy for Multi-agent D-formation Control. 23rd CISM IFToMM Symposium on Robot Design, Dynamics and Control (ROMANSY2020), 2020
Abstract:
This paper introduces a new control approach to perform formation control tasks on multi-agent systems, called D-formation control. The D-formation controller is a gradient-descent control law that exploits a regularized potential function to efficiently achieve specific formations. Taking inspiration from the flocking of birds, this approach differentiates itself from the several formation control strategies that can be found in the literature thanks to its flexibility. In fact, the approach that is usually employed in formation control is to try to enforce a set of very strict constraints in order to achieve rigid, a priori defined structures. We will show that the D-formation approach greatly relaxes such conditions.
In this paper, the D-formation control problem is introduced, and the equilibrium configurations of the controller are characterized. Additionally, a strategy for switching from one stable equilibrium to another one, i.e. for changing the shape of the formation, is proposed.
[ abstract ] [
url] [
BibTeX]
2019
A. Masiero, F. Fissore, R. Antonello, A. Cenedese, A. Vettore.
A COMPARISON OF UWB AND MOTION CAPTURE UAV INDOOR POSITIONING. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-2/W13, pp. 1695--1699, 2019
Abstract:
The number of applications involving unmanned aerial vehicles (UAVs) grew dramatically during the last decade. Despite such incredible success, the use of drones is still quite limited in GNSS denied environment: indeed, the availability of a reliable GNSS estimates of the drone position is still fundamental in order to enable most of the UAV applications. Given such motivations, in this paper an alternative positioning system for UAVs, based on low cost ultra-wideband band (UWB) is considered. More specifically, this work aims at assessing the positioning accuracy of UWB-based positioning thanks to the comparison with positions provided by a motion capture (MoCap) system. Since the MoCap accuracy is much higher than that of the UWB system, it can be safely used as a reference trajectory for the validation of UWB estimates. In the considered experiment the UWB system allowed to obtain a root mean square error of 39.4?cm in 3D positioning based on the use of an adaptive extended Kalman filter, where the measurement noise covariance was adaptively estimated.
[ abstract ] [
url] [
BibTeX]
Y. Chen, M. Bruschetta, R. Carli, A. Cenedese, D. Varagnolo, .. Et al.
A computationally efficient model predictive control scheme for space debris rendezvous. IFAC Symposium on Automatic Control in Aerospace (ACA 2019), 2019
Abstract:
We propose a non-linear model predictive scheme for planning fuel efficient maneuvers of small spacecrafts that shall rendezvous space debris. The paper addresses the specific issues of potential limited on-board computational capabilities and low-thrust actuators in the chasing spacecraft, and solves them by using a novel MatLab-based toolbox for real-time non-linear model predictive control (MPC) called MATMPC. This tool computes the MPC rendezvous maneuvering solution in a numerically efficient way, and this allows to greatly extend the prediction horizon length. This implies that the overall MPC scheme can compute solutions that account for the long time-scales that usually characterize the low-thrust rendezvous maneuvers. The so-developed controller is then tested in a realistic scenario that includes all the near-Earth environmental disturbances. We thus show, through numerical simulations, that this MPC method can successfully be used to perform a fuel-efficient rendezvous maneuver with an uncontrolled object, plus evaluate performance indexes such as mission duration, fuel consumption, and robustness against sensor and process noises.
[ abstract ] [
url] [
BibTeX]
A. Cenedese, L. Varotto.
A Distributed Approach to 3D Reconstruction in Marker Motion Capture Systems. International Conference on Distributed Smart Cameras (ICDSC 2019), 2019
Abstract:
Optical motion capture systems have attracted much interest over
the past years, due to their advantages with respect to non-optical
systems. Moreover, with the technological advances on camera
systems, computer graphics and computational methodologies, it
becomes technically and economically feasible to consider motion
capture systems made of large networks of cameras with embedded
communication and processing units on board (i.e., smart cameras).
Nevertheless, the approaches relying on the classical 3D recon-
struction methods would become inefficient in this case, since their
nature is intrinsically centralized. For this reason, we propose a dis-
tributed 3D reconstruction algorithm, which exploits a specific cam-
era nodes organization to efficiently process the information and
to remarkably speed up the scene reconstruction task. Indeed, nu-
merical simulations show that the proposed computational scheme
overcomes the principal state of the art solutions in terms of recon-
struction speed. Furthermore, the high processing speed does not
compromise the accuracy of the final result, since the algorithm is
designed to be robust to occlusions and measurement noise.
[ abstract ] [
url] [
BibTeX]
N. Bargellesi, M. Carletti, A. Cenedese, G.A. Susto, M. Terzi.
A Random Forest-based Approach for Hand Gesture Recognition with Wireless Wearable Motion Capture Sensors. 5th IFAC International Conference on Intelligent Control and Automation Sciences, 2019
Abstract:
Gesture Recognition has a prominent importance in smart environment and home automation. Thanks to the availability of Machine Learning approaches it is possible for users to define gestures that can be associated with commands for the smart environment. In this paper we propose a Random Forest-based approach for Gesture Recognition of hand movements starting from wireless wearable motion capture data. In the presented approach, we evaluate different feature extraction procedures to handle gestures and data with different duration. To enhance reproducibility of our results and to foster research in the Gesture Recognition area, we share the dataset that we have collected and exploited for the present work.
[ abstract ] [
url] [
BibTeX]
A. Cenedese, F. Tramarin, S. Vitturi, A. Et.
Comparative assessment of different OPC UA open–source stacks for embedded systems. IEEE Conference on Emerging Technologies and Factory Automation (ETFA2019), pp. 1127-1134, 2019
Abstract:
With the rise of Industry 4.0 and of the Industrial Internet, the computing and communication infrastructures achieved an essential role within process and factory automation, and cyberphysical systems in general. In this scenario, the OPC UA standard is currently becoming a widespread opportunity to enable interoperability among heterogeneous industrial systems. Nonetheless, OPC UA is characterized by a complex protocol architecture, that may impair the scalability of applications and may represent a bottleneck for its effective implementation in resource-constrained devices, such as low-cost industrial embedded systems. Several different OPC UA implementations are available, which in some significant cases are released under an open source license. In this context, the aim of this paper is to provide an assessment of the performance provided by some of these different OPC UA implementations, focusing specifically on potential development and resource bottlenecks. The analysis is carried out through an extensive experimental campaign explicitly targeting general purpose low-cost embedded systems. The final goal is to provide a comprehensive performance comparisons to allow devising some useful practical guidelines.
[ abstract ] [
url] [
BibTeX]
N. Lissandrini, G. Michieletto, R. Antonello, M. Galvan, A. Franco, A. Cenedese.
Cooperative Optimization of UAVs Formation Visual Tracking. Robotics, vol. 8(3), pp. 1--22 (Article Number 52), 2019
Abstract:
The use of unmanned vehicles to perform tiring, hazardous, repetitive tasks, is becoming a reality out of the academy laboratories, getting more and more interest for several application fields from the industrial, to the civil, to the military contexts. In particular, these technologies appear quite promising when they employ several low-cost resource-constrained vehicles leveraging their coordination to perform complex tasks with efficiency, flexibility, and adaptation that are superior to those of a single agent (even if more instrumented). In this work, we study one of said applications, namely the visual tracking of an evader (target) by means of a fleet of autonomous aerial vehicles, with the specific aim of focusing on the target so as to perform an accurate position estimation while concurrently allowing a wide coverage over the monitored area so as to limit the probability of losing the target itself. These clearly conflicting objectives call for an optimization approach that is here developed: by considering both aforementioned aspects and the cooperative capabilities of the fleet, the designed algorithm allows controling in real time the single fields of view so as to counteract evasion maneuvers and maximize an overall performance index. The proposed strategy is discussed and finally assessed through the realistic Gazebo-ROS simulation framework.
[ abstract ] [
url] [
BibTeX]
L. Varotto, M. Fabris, G. Michieletto, A. Cenedese.
Distributed Dual Quaternion Based Localization of Visual Sensor Networks. European Control Conference (ECC 2019), 2019
Abstract:
In this paper we consider the localization problem for a visual sensor network. Inspired by the alternate attitude and position distributed optimization framework discussed in [1], we propose an estimation scheme that exploits the unit dual quaternion algebra to describe the sensors pose. This representation is beneficial in the formulation of the optimization scheme allowing to solve the localization problem without designing two interlaced position and orientation estimators, thus improving the estimation error distribution over the two pose components and the overall localization performance. Furthermore, the numerical experimentation asserts the robustness of the proposed algorithm w.r.t. the initial conditions.
[ abstract ] [
url] [
BibTeX]
M. Fabris, A. Cenedese.
Distributed Strategies for Dynamic Coverage with Limited Sensing Capabilities. Mediterranean Conference on Control and Automation (MED19), 2019
Abstract:
In this work, it is presented the development of a novel distributed algorithm performing robotic coverage, clustering and dispatch around an event in static-obstacle-structured environments without relying on metric information. Specifically, the aim is to account for the trade-off between local communication given by bearing visibility sensors installed on each agent involved, optimal deployment in closed unknown scenarios and focus of a group of agents on one point of interest. The particular targets of this study can be summarized as 1. the minimization of the number of nodes and links maintaining a distributed approach over a connected communication graph; 2. the identification of an activation cluster around an event with a radial decreasing intensity, sensed by each agent; 3. the attempt to send the agents belonging to the cluster towards the most intense point in the scenario by minimizing a weighted isoperimetric functional.
[ abstract ] [
url] [
BibTeX]
G. Michieletto, A. Cenedese, A. Franchi.
Force-Moment Decoupling and Rotor-Failure Robustness for Star-Shaped Generically-Tilted Multi-Rotors. IEEE Conference on Decision and Control (CDC2019), pp. 2132--2137, 2019
Abstract:
Aerial robotics is increasingly becoming an attractive field of research thanks to the peculiar mixture of theoretical issues to be solved and technological challenges to be faced. In particular, recent developments have seen the multiplication of multi-rotor platforms that aim at improving the maneuverability of classical quadrotors in standard and harsh flying conditions, thus opening the field to comprehensive studies over the structural multi-rotor properties of actuation, decoupling, and robustness, which strongly depend on the mechanical configuration of the systems. This work collocates along this line of research by considering star-shaped generically-tilted multi-rotors (SGTMs), namely platforms with more than four possibly tilted propellers (along two tilting orthogonal axes). For these platforms, we investigate how the structural choices over the number of propellers and the tilting angles affect the force-moment decoupling features and, by recalling the robustness definition that refers to the hovering capabilities of the platform, we provide a robustness analysis and an hoverability assessment for SGTMs having five to eight actuators against the loss of one and two propellers.
[ abstract ] [
url] [
BibTeX]
G. Michieletto, A. Cenedese.
Formation Control for Fully Actuated Systems: a Quaternion-based Bearing Rigidity Approach. European Control Conference (ECC 2019), 2019
Abstract:
This work deals with formations of mobile agents with six independently controllable degrees of freedom able to retrieve relative bearing measurements w.r.t. some neighbors in the group. Exploiting the bearing rigidity framework, two control objectives are here addressed: ( i) the stabilization of these fully actuated multi-agent systems towards desired configurations, and (i i) their coordinated motion along directions guaranteeing the system shape maintenance. The proposed approach relies on a new formulation of the bearing rigidity theory based on the adoption of the unit quaternion formalism to describe the agents attitude. Through this representation choice, the formation dynamics is linear w.r.t. the input control velocities and the rigidity theory suggests the design of a distributed control scheme for both control goals whose efficacy is confirmed by numerical simulations.
[ abstract ] [
url] [
BibTeX]
A. Razman, A.S.A. Ghani, A. Cenedese, F.A. Adnan, G.A. Susto, K.M. Ismail, R.M. Musa, Y. Mukai, Z. Taha, A. Majeed.
Hunger Classification of Lates Calcarifer by means of an automated feeder and image processing. Computers and Electronics in Agriculture, vol. 1632019
Abstract:
In an automated demand feeder system, underlining the parameters that contribute to fish hunger is crucial in order to facilitate an optimised food allocation to the fish. The present investigation is carried out to classify the hunger state of Lates calcarifer. A video surveillance technique is employed for data collection. The video was taken throughout the daytime, and the fish were fed through an automated feeding system. It was demonstrated through this investigation that the use of such automated system does contribute towards a higher specific growth rate percentage of body weight as well as the total length by approximately 26.00% and 15.00%, respectively against the conventional time-based method. Sixteen features were feature engineered from the raw dataset into window sizes ranging from 0.5?min, 1.0?min, 1.5?min and 2.0?min, respectively coupled with the mean, maximum, minimum and variance for each of the distinctive temporal window sizes. In addition, the extracted features were analysed through Principal Component Analysis (PCA) for dimensionality reduction as well as PCA with varimax rotation. The data were then classified using a Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Random Forest Tree models. It was demonstrated that the varimax based PCA yielded the highest classification accuracy with eight identified features. The prediction results based of the developed k-NN model on the selected features on the test data exhibited a classification rate of 96.5% was achieved suggesting that the features examined are non-trivial in classifying the fish hunger behaviour.
[ abstract ] [
url] [
BibTeX]
N. Trivellin, D. Barbisan, M. Pietrobon, D. Badocco, P. Pastore, A. Cenedese, G. Meneghesso, E. Zanoni, M. Meneghini.
Near-UV LED-based systems for low-cost and compact oxygen-sensing systems in gas and liquids. SPIE Conference - Photonics West Opto Proc. SPIE 10940, Light-Emitting Devices, Materials, and Applications, pp. 109400V, 2019
Abstract:
With this work we report on the design, development and testing of near UV LED-based systems for oxygen gas sensing. The design and developed system is an optoelectronic setup based on 405 nm LEDs which excites and measures the photoluminescence emitted from a porphyrin based luminophor. By means of an accurate optical and optoelectronic setup, the system is able to operate without the need of avalanche photodiodes, thus resulting in a compact and low energy structure. The optical setup is specifically designed to maximize both the LED light exciting the luminophor and converted light acquired from the sensor.
[ abstract ] [
url] [
BibTeX]
M. Fabris, G. Michieletto, A. Cenedese.
On the Distributed Estimation from Relative Measurements: a Graph-Based Convergence Analysis. European Control Conference (ECC 2019), 2019
Abstract:
The state estimation of a multi-agent system
resting upon noisy measurements constitutes a problem re-
lated to several applicative scenarios, such as, for example,
robotic localization and navigation, resource balancing and task
allocation, cooperative manipulation and coordinated control.
Adopting the standard least-squares approach, in this work
we derive both the (centralized) analytic solution to this issue
and two distributed iterative schemes, which allow to establish
a connection between the convergence behavior of consensus
algorithm towards the optimal estimate and the theory of the
stochastic matrices that describe the network system dynamics.
This study on the one hand highlights the role of the topological
links that define the neighborhood of agent nodes, while on the
other allows to optimize the convergence rate by easy parameter
tuning. The theoretical findings are validated considering dif-
ferent network topologies by means of numerical simulations.
[ abstract ] [
url] [
BibTeX]
M. Fabris, A. Cenedese, J. Hauser.
Optimal Time-Invariant Formation Tracking for a Second-Order Multi-Agent System. European Control Conference (ECC 2019), 2019
Abstract:
Given a multi-agent linear system, we formalize and solve a trajectory optimization problem that encapsulates trajectory tracking, distance-based formation control and input energy minimization. To this end, a numerical projection operator Newton's method is developed to find a solution by the minimization of a cost functional able to capture all these different tasks. To stabilize the formation, a particular potential function has been designed, allowing to obtain specified geometrical configurations while the barycenter position and velocity of the system follows a desired trajectory.
[ abstract ] [
url] [
BibTeX]
L. Varotto, A. Zampieri, A. Cenedese.
Street Sensor Set Selection through Map Segmentation and Observability Measures. Mediterranean Conference on Control and Automation (MED19), 2019
Abstract:
Nowadays, vehicle flow monitoring, model-based
traffic management, and congestion prediction are becoming
fundamental elements for the realization of the Smart City
paradigm. These tasks usually require wide sensor deploy-
ments, but, due to economical, practical, and environmental
constraints, they must be accomplished with a limited number
of sensors. Thus motivated, this work addresses the sensors
selection problem for urban street monitoring, by employing
a road map image as the basic information and considering
the placement of at most one sensor along each road with a
chosen number of available devices. To solve the problem, the
concept of system observability is exploited as the criterium for
optimal sensor placement, specifically related to the capability
of estimating the traffic flow in each road using the available
output measurements. In this framework, different integer non-
linear programming problems are proposed, whose solutions
are studied and analyzed by means of numerical simulations
on a real case scenario.
[ abstract ] [
url] [
BibTeX]
A. Morato, S. Vitturi, A. Cenedese, G. Fadel, F. Tramarin.
The Fail Safe over EtherCAT (FSoE) protocol implemented on the IEEE 802.11 WLAN. IEEE Conference on Emerging Technologies and Factory Automation (ETFA2019), pp. 1163-1170, 2019
Abstract:
Wireless networks are ever more deployed in industrial automation systems in various types of applications. A significant example in this context is represented by the transmission of safety data that, traditionally, was accomplished by wired systems. In this paper we propose an implementation of the Fail Safe over EtherCAT (FSoE) protocol on the top of IEEE 802.11 WLAN. The paper, after a general introduction of FSoE, focuses on the implementation of such protocol on commercial devices running UDP at the transport layer and connected via the IEEE 802.11 Wireless LAN. Then the paper presents some experimental setups and the tests that have been carried out on them. The obtained results are encouraging, since they show that good safety performance can be achieved even in the presence of wireless transmission media.
[ abstract ] [
url] [
BibTeX]
R. Fantinel, A. Cenedese.
Vision-based inspection system for metallic surfaces: CNN driven by features. Quality Control by Artificial Vision Conference (QCAV 2019) - Awarded for the "Most Innovative Application", 2019
Abstract:
We propose a novel approach for the inspection of metallic surfaces, integrable in the production phase. It consists of
a compact illumination and vision equipment that projects over a moving object a series of light bands. We developed a
specific feature extraction algorithms based on the dynamic evolution of the reflected light over the object surface, and we
built an Hybrid Learning System by feeding an Auto-Encoder CNN with this dynamic light features. The results obtained by
this novel approach reach higher performance respect classic Deep Learning networks and Machine Learning technique,
in critical light conditions too.
[ abstract ] [
url] [
BibTeX]
2018
A. Antonello, G. Michieletto, R. Antonello, A. Cenedese.
A Dual Quaternion Feedback Linearized Approach for Maneuver Regulation of Rigid Bodies. IEEE Control Systems Letters, vol. 2(3), pp. 327 -- 332, 2018
Abstract:
The adoption of the dual quaternion formalism to represent the pose (position and orientation) of a rigid body allows to design a single controller to regulate both its position and its attitude. In this work, we adopt such a pose representation to develop an exponentially stable maneuver regulation control law, ensuring robust path following in the presence of disturbances. The designed solution relies on the feedback linearized model of the dual quaternion based dynamics of the rigid body. Numerical results confirm the effectiveness of the proposed maneuver regulation approach when compared with trajectory tracking in a noisy scenario.
[ abstract ] [
url] [
BibTeX]
N. Normani, A. Urru, L. Abraham, M. Walsh, S. Tedesco, A. Cenedese, G.A. Susto, B. O'Flynn.
A Machine Learning Approach for Gesture Recognition with a Lensless Smart Sensor System. 15th International Conference on Wearable and Implantable Body Sensor Networks, pp. 136--139, 2018
Abstract:
Hand motion tracking traditionally re-
quires highly complex and expensive systems in terms
of energy and computational demands. A low-power,
low-cost system could lead to a revolution in this field
as it would not require complex hardware or additional
equipment. The present paper exploits the Multiple
Point Tracking algorithm developed at the Tyndall
National Institute as the basic algorithm to perform
a series of gesture recognition tasks. The hardware
relies upon the combination of a stereoscopic vision
of two novel Lensless Smart Sensors (LSS) combined
with IR filters and five hand-held LEDs to track. Track-
ing common gestures generates a six-gestures dataset,
which is then employed to train three Machine Learning
models: k-Nearest Neighbors, Support Vector Machine
and Random Forest. An offline analysis highlights how
different LEDs’ positions on the hand affect the clas-
sification accuracy. The comparison shows how the
Random Forest outperforms the other two models with
a classification accuracy of 90-91%.
[ abstract ] [
url] [
BibTeX]
S. Vitturi, A. Morato, A. Cenedese, G. Fadel, F. Tramarin, R. Fantinel.
An Innovative Algorithmic Safety Strategy for Networked Electrical Drive Systems. 16th International Conference on Industrial Informatics (INDIN18), pp. 368--373, 2018
Abstract:
In this paper we address the safety strategies for networked electrical drive systems, in the context of industrial automation. Specifically, it is considered the handling of errors and faults that may occur during the execution of safety related functions, on a set of electrical drives. Such devices, which operate in a coordinated way, are connected via an industrial communication network and use a safety industrial protocol. In this respect, a novel approach that exploits a distributed consensus algorithm to identify and possibly recover the aforementioned errors is devised and discussed in comparison with a traditional safe shut-down strategy. The theoretical performance figures and the effectiveness of the proposed approach are evaluated in a real industrial case study considering two different widespread network topologies.
[ abstract ] [
url] [
BibTeX]
F. Chiariotti, C. Pielli, A. Cenedese, A. Zanella, M. Zorzi.
Bike Sharing as a Key Smart City Service: State of the Art and Future Developments. 7th International Conference on Modern Circuits and Systems Technologies (MOCAST 2018), pp. 1--6, 2018
Abstract:
Bike-sharing has outgrown its first failures in the
’60s and ’70s and become ubiquitous around the world. This
rapid growth is strongly intertwined with the rise of Smart Cities:
the use of connected bikes makes the service more practical for
users, avoids thefts and provides a large amount of data for
system planners. Over the past few years, the research on bike-
sharing has bloomed, providing several innovative solutions to
improve the service and encourage citizens to use environmentally
friendly modes of transportation, reducing both traffic and
commuting times. In this work, we present the most promising
developments towards a tighter integration between Smart City
data and techniques and the operation and planning of bike-
sharing system, focusing on two model use-cases: New York City’s
CitiBike service, a large system with hundreds of stations, and
Padova’s GoodBike system, which has just 28 stations.
[ abstract ] [
url] [
BibTeX]
G. Michieletto, S. Milani, A. Cenedese, G. Baggio.
Improving Consensus-based Distributed Camera Calibration via Edge Pruning and Graph Traversal Initialization. Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3166--3170, 2018
Abstract:
Over the past few years, a huge number of distributed camera calibration strategies have been proposed for video surveillance and monitoring systems involving mobile terminals. Many of the proposed solutions rely on consensus-based algorithms, which aim at estimating the configuration of the network via a message passing protocol. In this paper we propose an improved consensus-based distributed camera calibration strategy that exploits a robust initialization, together with a pruning protocol to remove faulty links which could propagate excessively-noisy information through the network reducing the convergence time. The proposed solution seems to improve the state-of-the-art strategies in terms of accuracy, convergence speed, and computational complexity.
[ abstract ] [
url] [
BibTeX]
F. Branz, M. Duzzi, L. Olivieri, F. Sansone, G. Michieletto, R. Antonello, A. Cenedese, A. Francesconi.
Laboratory validation of close navigation, rendezvous and docking technologies for nanosats. Proceedings of the 4S Symposium, 2018
Abstract:
Over the last decades, small satellites have
become very appealing among the space community for their low complexity and
high flexibility. Many proposed mission concepts foresee the employment of
miniature spacecraft for a variety of applications, many of which are
economically unfeasible with traditional vehicles. This is due to the fact that
the development of miniaturized and standardized space vehicles may
considerably reduce the design, manufacturing and lunch costs involved. Furthermore,
the reduced unitary mass of small satellites allows launches of multiple
vehicles equipped with independent functionalities, thus achieving increased
flexibility and redundancy. In the future, one additional opportunity could be
given by the capability to assemble spacecraft in orbit to form reconfigurable
structures. This would further boost the number of possibilities in terms of
applications and operations. Nevertheless, the novelty of such concept and the
intrinsic complexity of its practical realization still require a considerable
research effort. In fact, only few navigation and docking technologies for nano- and micro satellites
have been designed and proved in relevant environment. In this framework, the
authors focus on the development and validation of critical technologies for
close navigation, rendezvous and docking suitable for nanosatellites.
This
paper presents a laboratory experiment for the validation on a complete rendezvous,
navigation and docking package compatible with the common CubeSat standard. The
experiment is conducted on a low friction table, with one free moving vehicle
(chaser) that approaches and docks to a fixed target interface. The test
facility allows three degrees of freedom to the nanosat mock-up. The vehicle is
equipped with an autonomous package that features (1) a camera-based vision
system for relative navigation, (2) a set of independent electro-magnetic coils
for final alignment and soft docking, (3) a single-actuator hard docking system
for structural connection between the chaser and the target, (4) a dedicated
electronics package for motion control and system status monitoring. The mobile
platform is also equipped with a set of flat air bearing with a dedicated
high-pressure pneumatic circuit for frictionless in-plane motion.
This paper
describes the docking package, the CubeSat mock-up and the test facility in
details, with reference to the main design considerations. Numerical
simulations have been conducted to foresee the dynamical behaviour of the
system and to select the appropriate control algorithm. An intensive
experimental campaign aims at the validation of numerical results and at the
functional verification and performance estimation for each subsystem and for
the system as a whole. The numerical and experimental results are presented and
compared, allowing to draw useful conclusions for the future development steps.
[ abstract ] [
BibTeX]
A. Antonello, G. Michieletto, R. Antonello, A. Cenedese.
Maneuver Regulation vs. Trajectory Tracking for Fully Actuated UAVs: A Dual Quaternion Approach. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2018), pp. poster 02/10 #13, 2018
Abstract:
Maneuver regulation emerges as an optimal strategy to perform robust path following in presence of disturbances, exploiting vehicle controllability and improving performances w.r.t. trajectory tracking. In this work we consider
maneuver regulation for a fully-actuated aerial platform in a
dual quaternion framework, which yields the additional benefit
of addressing the attitude and position control problem with
a single state controller. To this aim, the nonlinear dynamics
is first derived in a dual quaternion setup and then feedback
linearized to enable the design of a stable maneuver regulator.
This controller is compared with a standard PD scheme, w.r.t.
the capability of following a desired trajectory, and is then
further improved through the definition of a strategy to compensate for the cumulative delay due to external disturbances.
[ abstract ] [
url] [
BibTeX]
A. Cenedese, P. Bettini, M. Bonotto.
Model-based approach for magnetic reconstruction in axisymmetric nuclear fusion machines. IEEE Transactions on Plasma Science, vol. 46(3), pp. 636 - 644, 2018
Abstract:
This paper describes an approach for the magnetic
reconstruction in large scale tokamak devices that is suitable for a
real time employment in order to provide reference for an active
control action during the whole plasma evolution. This problem
can be seen as a free boundary problem, where the shape features
of the plasma are determined by the equilibrium with the external
sources, namely the active circuit currents and the eddy currents
flowing in the passive structures. In this respect, a dynamic model
is needed in order to estimate the induced currents and provide
a consistent representation of the whole system behavior during
the entire plasma discharge. Such a model is then coupled with
an iterative optimization procedure to provide a model of the
plasma that, superimposed with the external sources, minimizes
the error of the reconstructed magnetic map with reference to the
available sensor measurements. The analysis and the validation of
this approach are presented, resulting in a procedure that appears
to accurately follow the behavior of the system both during slow
varying evolution and during strongly dynamic events.
[ abstract ] [
url] [
BibTeX]
C. Favaretto, A. Cenedese.
Non-linear modeling and control of Mitochondrial Dynamics. 57th IEEE Conference on Decision and Control (CDC 2018), pp. 3491--3496, 2018
Abstract:
Mitochondrial Dynamics (MD) has recently
emerged as one of the most interesting topics in biology since
the intricate connection between energy production and MD
regulates cells development and function. On the other hand,
the impairment of such mechanism is strictly related to the
emergence of various diseases, among which neurodegenerative
disorders. In this work, we provide a simple, yet complete, and
well-posed mathematical model to describe the MD and the
related phenomena through a population-dynamics approach,
together with the ATP-energy turnover, which is an important
step to unravel the underlying dynamics of the whole cell system
and has a key role in its quality control. With the tools of
system theory, we highlight the positiveness of the system and
the presence of non-zero equilibria and compute bounds for
the involved system state quantities. Furthermore, we consider
a situation of impairment in the MD and design a control law,
based on input-output linearization and state-feedback control
able to allow a damaged system to compensate for the defect and
behave as a nominal one. In this scenario, we test two different
protocols that could be suggestive for treatment strategies.
[ abstract ] [
url] [
BibTeX]
M. Duzzi, M. Mazzucato, R. Casagrande, L. Moro, F. Trevisi, R. Vitellino, M. Vitturi, A. Cenedese, E.C. Lorenzini, A. Francesconi.
PACMAN experiment: a CubeSat-size integrated system for proximity navigation and soft-docking. Proceedings of the 4S Symposium, 2018
Abstract:
In the last years, international space-related
companies and agencies are manifesting great interest in on-orbit servicing.
Innovative solutions to perform on-orbit operations such as refuelling, payload
updating and maintenance, subsystems repairing and inspection are under study
and all the new ideas and technologies under development are perceived as
extremely functional and cost-effective, capable of increasing the operational
lifetime of a satellite and decreasing the costs related to its complete
replacement.
For these reasons, the development of an automatic,
standard and reliable docking system would simplify the accomplishment of
on-orbit servicing procedures. Presently, there has been an increasing interest
in developing different technologies for proximity navigation and rendezvous
manoeuvres but no competitive or commercial technologies are currently
available to perform autonomous rendezvous and docking between small-satellites.
One
promising solution is represented by relative magnetic navigation, where
the chaser relative position and attitude can be controlled thanks to magnetic
interactions with the target vehicle.
This
paper presents an overview of the PACMAN experiment:
PACMAN is a technology demonstrator developed by a team of university and PhD
students in the framework of ESA
Education Fly Your Thesis! 2017 programme and supported by the
University of Padova. The experiment has been selected
for the 68th ESA Parabolic Flight
Campaign that took place in December 2017. The main goal of the
project was to develop and validate in low-gravity conditions an integrated
system for proximity navigation and soft-docking based on magnetic
interactions, suitable for small-scale spacecraft. This has been accomplished
by launching a miniature spacecraft mock-up (1U CubeSat) towards a
free-floating target that generates a static magnetic field; a set of
actively-controlled magnetic coils on-board the spacecraft mock-up, assisted by
dedicated localization sensors, have been used to control its attitude and
position relative to the target. This experimental setup allowed to
study the behaviour of a miniature spacecraft subjected to controlled magnetic
interactions in low-gravity conditions and to validate the theoretical/numerical
models that describe such interactions.
The
paper describes the experiment design, realization and execution, from the initial
concept to the Parabolic Flight Campaign tests. The experiment working
principle is illustrated with particular attention towards the navigation and soft-docking
subsystems, and the analysis of retrieved scientific results is finally presented.
[ abstract ] [
BibTeX]
M. Duzzi, M. Mazzucato, R. Casagrande, L. Moro, F. Trevisi, R. Vitellino, M. Vitturi, A. Cenedese, E.C. Lorenzini, A. Francesconi.
PACMAN experiment: a Parabolic Flight Campaign student experience. Proceedings of the 2nd Symposium on Space Educational Activities, 2018
Abstract:
Presently, no competitive or commercial solution is
currently available to perform autonomous rendezvous and docking between
small-satellites. Therefore, in the last years there has been an increasing
interest in developing different technologies for proximity navigation and
rendezvous manoeuvres, addressing the main issues of fuel consumption and the
strong impact of close range navigation subsystems on satellites mass budget
and complexity. One promising solution is represented by relative magnetic
navigation, where the chaser relative position and attitude can be controlled
thanks to magnetic interactions with the target vehicle.
PACMAN experiment is a technology demonstrator that has been developed by
a team of university and PhDs students in the framework of ESA Education Fly Your Thesis! 2017
programme and supported by the University of Padova. The experiment has been selected to fly during the 68th ESA Parabolic Flight Campaign, currently scheduled to
take place this December. The main goal of the project is to
develop and validate in low-gravity conditions an integrated system for
proximity navigation and soft-docking based on magnetic interactions, suitable
for small-scale spacecraft. This will be accomplished by launching a miniature
spacecraft mock-up towards a free-floating target that generates a static
magnetic field; a set of actively-controlled magnetic coils on-board the
spacecraft mock-up, assisted by dedicated localization sensors, will be used to
control its attitude and position relative to the target.
The realization of PACMAN experiment will
allow to study the behaviour of a miniature spacecraft subjected to controlled
magnetic interactions in low-gravity conditions and to validate the
theoretical/numerical models that describe such interactions.
This
paper presents an overview from the concept and design of the experiment to the
Parabolic Flight Campaign tests. The experiment working principle will be
illustrated, along with the design and assembly phases. Particular attention
will be made towards the problem solving approach. Alternatives and backup
solutions are introduced as part of the lessons learned during the entire
programme. Finally, the analysis of retrieved scientific results will be
showed.
[ abstract ] [
url] [
BibTeX]
C. Favaretto, S. Spadone, S. Della Penna, A. Cenedese, M. Corbetta.
Spatio-temporal relationships between BOLD and MEG signals at rest or during visuospatial attention. in Organization for Human Brain Mapping (OHBM) Annual Meeting, pp. poster #1910, 2018
Abstract:
The relationship between fMRI and MEG signals between different cortical regions (functional connectivity, FC) has been extensively analyzed in the resting state (De Pasquale et al 2010; Brookes et al 2011; Hipp et al 2012). Much less is known about FC modulations from rest to task states, and how they appear respectively in these two imaging modalities. Previously we have shown task-specific alterations of FC in fMRI during a visuospatial attention task (Spadone et al., PNAS, 2015). Specifically, decrements of resting correlation in visual areas were coupled with increments of correlation between visual and dorsal attention regions. Here, we compared fMRI with band-limited power (BLP) correlation obtained with MEG on the same group of subjects. Aim (i) is to measure frequency specific task-related FC modulations in MEG. Aim (ii) is to compare fMRI- and MEG-FC modulations (task-rest).
[ abstract ] [
url] [
BibTeX]
N. Trivellin, D. Barbisan, D. Badocco, P. Pastore, G. Meneghesso, M. Meneghini, E. Zanoni, G. Belgioioso, A. Cenedese.
Study and development of a fluorescence based sensor system for monitoring oxygen in wine production: The WOW project. Sensors, vol. 18(4), pp. 1130, 2018
Abstract:
The importance of oxygen in the winemaking process is widely known, as it affects the chemical aspects and therefore the organoleptic characteristics of the final product. Hence, it is evident the usefulness of a continuous and real-time measurements of the levels of oxygen in the various stages of the winemaking process, both for monitoring and for control. The WOW project has focused on the design and the development of an innovative device for monitoring the oxygen levels in wine. This system is based on the use of an optical fiber to measure the luminescent lifetime variation of a reference metal/porphyrin complex, which decays in presence of oxygen. The developed technology results in a high sensitivity and low cost sensor head that can be employed for measuring the dissolved oxygen levels at several points inside a wine fermentation or aging tank. This system can be complemented with dynamic modeling techniques to provide predictive behavior of the nutrient evolution in space and time given few sampled measuring points for both process monitoring and control purposes. The experimental validation of the technology has been first performed in a controlled laboratory setup to attain calibration and study sensitivity with respect to different photo-luminescent compounds and alcoholic or non-alcoholic solutions, and then in an actual case study during a measurement campaign at a renown Italian winery.
[ abstract ] [
url] [
pdf] [
BibTeX]
G. Marchiori, A. Cenedese, .. Et al.
Study of a Plasma Boundary Reconstruction Method based on Reflectometric Measurements for Control Purposes. IEEE Transactions on Plasma Science, vol. 46(5), pp. 1285--1290, 2018
Abstract:
A purely geometric approach has been investigated to reconstruct the Demonstration Fusion Power Reactor (DEMO) plasma boundary for control purposes. The whole plasma boundary is reconstructed by using a deformable template method based on B-splines. The final curve shape is achieved by minimizing the distance between a limited number of estimated and measured (at present provided by an equilibrium code) plasma boundary points along the reflectometer lines of sight. The resulting unconstrained optimization problem is solved by a simulated annealing algorithm. The method is complemented by including the available plasma and poloidal field coil current measurements to refine the boundary reconstruction in the X-point region. The robustness with respect to random measurement random errors and to a reduction in the number of measurements is discussed. The main equilibrium and shape geometric quantities (such as plasma cross-sectional area, plasma center position, elongation, and triangularity) were computed and compared to the corresponding quantities of a DEMO reference equilibrium.
[ abstract ] [
url] [
BibTeX]