ALESSANDRO RIZZO

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Professore Associato (L.240)

Membro Centro Interdipartimentale (PIC4SeR - PoliTO Interdepartmental Centre for Service Robotics)

+39 0110907251 / 7251 (DET)

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Ambiti di ricerca
Gruppi di ricerca Automatica
Progetti di ricerca

Finanziati da bandi competitivi

  • Sustainable Mobility Center (Centro Nazionale per la Mobilità Sostenibile – CNMS) - Spoke 1, (2022-2025) - Responsabile Scientifico di Struttura

    PNRR – Missione 4

    ERC sectors

    PE8_1 - Aerospace engineering

    SDG

    Obiettivo 13. Promuovere azioni, a tutti i livelli, per combattere il cambiamento climatico*|Obiettivo 12. Garantire modelli sostenibili di produzione e di consumo

    Abstract

    The spoke will create a network of research centers and laboratories, large-scale demonstration environments, full-scale prototypal applications to achieve the following goals: • development of new technologies for green civil aviation for high efficiency and low carbon footprint, for the medium/short range transport, regional and public utility services;• identification of logistic alternatives based on airborne and multimodal services with high autonomy and deport infrastructures;• outline of the guidelines for the design of autonomous and single pilot systems in aeronautics (with particular emphasis on the Advanced / Urban Air Mobility) and evaluate market opportunities from new technologies.

    Paesi coinvolti

    • ITALIA

    Strutture interne coinvolte

  • Physics-Informed Machine Learning for Trustworthy Control of Autonomous Robots, (2022-2023) - Responsabile Scientifico

    Ricerca Internazionale non UE

    ERC sectors

    PE7_10 - Robotics

    SDG

    Obiettivo 9. Costruire un'infrastruttura resiliente e promuovere l'innovazione ed una industrializzazione equa, responsabile e sostenibile

    Abstract

    Trustworthy and long-term operations of autonomous robots in populated environments require the design of robust and interpretable control strategies. Two main kinds of controllers currently exist, on which the state of the art largely rests. On the one hand, data-driven controllers use machine learning in a black-box fashion, to provide robustness and generalization capabilities, thanks to the availability of large empirical data sets and the potentiality of Artificial Neural Networks (ANNs). At the other side of the spectrum, model-based controllers leverage the physical knowledge of the system, offering a more interpretable control action at the expense of typically high-dimensional models with limited granularity and adaptation capabilities. In this proposal, we devise a novel, robust and trustworthy control framework for autonomous robots, which seamlessly integrates the benefits of both data-driven and model-based approaches. Inspired to the emerging paradigm of Physics-Informed Machine Learning (PIML), the proposed control framework aims at attaining system robustness, as well as generalization capability and interpretability of learning-based models, with mathematically proved performance guarantees. The framework consists of three components: (i) a physics-informed surrogate model trained with an ad-hoc loss function to ensure physical consistency in the learning of the dynamical model of the robot, (ii) a residual Deep Neural Network (DNN) to provide real-time robustness and adaptability to unknown dynamics, and (iii) a nonlinear predictive controller with performance guarantees, which uses the predictions offered by the previous two models. The successful realization of this project will not only ensure trustworthy operations for safe-critical robotic systems (such as UAVs operating in densely populated areas), but will also attain the broader goal of advancing the state of the art in robust control of highly nonlinear and uncertain dynamical systems.

    Strutture interne coinvolte

  • Soluzioni Innovative per la Navigazione Autonoma Veloce – SINAV, (2021-2023) - Responsabile Scientifico

    Ricerca ASI

    Abstract

    La ricerca si propone l’obiettivo di sviluppare e verificare in unambiente rappresentativo un nuovo approccio di NavigazioneAutonoma Veloce (NAV) dei rover e di processamento dati autonomoe collaborativo di satelliti e droni potenzialmente utilizzabile durantefuture missioni di esplorazione robotica planetaria. I seguenti obiettiviprincipali e generale saranno perseguiti:- Decuplicare della velocità di percorrenza degli attuali rover spazialidagli attuali circa 10-20 m/ora fino ai 100-200 m/ora mediantesoluzioni di navigazione continua- Possibilità di proporre un sistema autonomo indipendente dallecondizioni di illuminamento e pertanto in grado di funzionare anche dinotte mediante utilizzo di sensori attivi, in via di qualificazione spaziale,per la navigazione autonoma (i.e. TOF camera o laser strips)

    Paesi coinvolti

    • ITALIA

    Enti/Aziende coinvolti

    • ALTEC S.P.A.

    Strutture interne coinvolte

  • Mac2Mic - Macro to Micro: uncovering the hidden mechanisms driving network dynamics , (2018-2020) - Responsabile Scientifico

    National Research

    Abstract

    Il progetto di ricerca si propone di inferire delle tecniche “universali” per inferire il modello di sistemi modellabili da una rete complessa, a partire da dati “grezzi”. Per garantire la validazione dei risultati della ricerca verrà sistematizzata la raccolta e l’organizzazione di un’ampia base di dati sperimentali, con particolare riferimento a sistemi epidemici, biologici, neuronali, finanziari. Uno dei prodotti della ricerca sarà la produzione di un software “open source” e gratuito per l’inferenza della dinamica di sistemi complessi a partire da dati sperimentali

    Paesi coinvolti

    • ITALIA

    Strutture interne coinvolte

  • Multi-modal crowd sensing to monitor buildings in smart cities, (2017-2018) - Responsabile Scientifico

    Ricerca Internazionale non UE

    Abstract

    In this project, we will put forward a monitoring system for urban areas using TIR, based on the synergic useof UAVs and cars, to minimize the downsides of both approaches and improving the quality and reliability ofmeasurements. Cars will be used as ground measurement and processing units, as well as ground stationsand relays for UAVs. UAVs collaborative clusters will be used to perform agile and accurate measurementsat the highest levels above the ground. The collected multi-rate, multi-resolution measurement sets will beinterpreted and fused on board of the cars in order to produce accurate analyses of the urban area,especially energetic analyses. This approach leverages the research team skillset, i.e., multi-agent modelingand control, networked control systems, robust optimization, development and testing of guidance,navigation and control systems of UAVs, multi-modal sensing and visualization, thermal analysis of builtenvironment, and smart cities analytics and design.

    Paesi coinvolti

    • ITALIA
    • STATI UNITI D'AMERICA

    Enti/Aziende coinvolti

    • MIT MASSACHUSETTS INSTITUTE OF TECHNOLOGY

    Strutture interne coinvolte

Finanziati da contratti commerciali