Dr. Andrea Motroni, Ph.D.
Dipartimento di Ingegneria dell’Informazione – University of Pisa
56122, Via G. Caruso 16, Pisa (PI), Italy
e-mail: andrea.motroni@unipi.it
phone: +39 0502217624

Assistant Professor
Associate Editor of IEEE Journal of Radio Frequency Identification
Join IEEE RFID-TA 2024 – Daytona Beach, December 18-20

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Teaching (contact me for any question!)

Tecnologie Elettromagnetiche per Sistemi Wireless (Ingegneria delle Telecomunicazioni, LT, III anno)

Additive manufacturing for electromagnetic sensing (Ingegneria delle Telecomunicazioni, LM, II anno)

Bachelor Thesis (Tesi di Laurea Triennale) and Master Thesis (Tesi di Laurea Magistrale) are available! Some proposals are here. These activities are mainly for Telecommunications Engineers, but are indeed multidisciplinary, so do not hesitate to contact me even if you are graduating in some other field.
Contact me (andrea.motroni@unipi.it) to know more!

Proposte di Tesi (ITA)

1. Data Fusion Lidar + RFID per Associazione Oggetti in Ambienti Dinamici
La tesi propone di studiare una tecnica di data fusion tra Lidar e RFID per l’associazione di oggetti in ambienti complessi. Dopo la mappatura con Lidar e clustering tramite DBSCAN, si utilizza la cross-correlazione temporale tra la fase RFID misurata e stimata dai movimenti dei cluster per associare ciascun cluster al suo tag RFID. L’obiettivo è identificare e tracciare oggetti mobili con ID univoci.

2. Integrazione Lidar + UWB per Tracciamento di precsione di oggetti
Il lavoro esplora la data fusion misure di ranging effettuate con sistemi UWB e dati Lidar per ottenere un’associazione robusta tra oggetti e dati di posizione (come nella tesi #1). Dopo il clustering su mappa Lidar, si effettua un matching tra le distanze rilevate dai tag UWB e quelle dei centroidi dei cluster per identificare in modo univoco gli oggetti tracciati.

3. Interfaccia IoT per Lettori RFID con MQTT
Questa tesi prevede lo sviluppo di un’interfaccia software in Python per lettori RFID compatibili con MQTT. I dati raccolti vengono analizzati e utilizzati per generare messaggi di allerta (warning/danger) in tempo reale, inviati a un server remoto secondo logiche personalizzabili. L’interfaccia è pensata per ambienti industriali o di monitoraggio in tempo reale.

4. Sistema di Inseguimento RFID con Manipolatore Robotico
L’obiettivo della tesi è progettare un sistema che consenta a un manipolatore robotico di seguire un tag RFID mobile. Utilizzando antenne montate sul braccio robotico, il sistema stima la direzione del tag e lo segue sia in ambienti 2D (scansione planare) che in configurazioni 3D, abilitando funzionalità avanzate di localizzazione e puntamento.

5. Fusione Computer Vision + RFID per Riconoscimento e Navigazione
La tesi analizza un sistema di data-fusion per la navigazione robotica o il riconoscimento oggetti mediante l’uso combinato di marker visivi (QR, ARUCO, marker infrarossi e altro) e tag RFID. I robot possono identificare oggetti “bi-tecnologia” (vision + RFID), anche all’interno di contenitori o in presenza di occlusioni parziali, migliorando l’affidabilità della percezione.

6. Integrazione di Lettori RFID in ROS2 per Robotica Cognitiva
Questo lavoro si focalizza sull’integrazione di un sistema di lettura RFID all’interno dell’ecosistema ROS2. Si svilupperanno nodi ROS per l’acquisizione, la pubblicazione e l’elaborazione dei dati RFID, abilitando il loro utilizzo in scenari robotici complessi come tracciamento oggetti, interazione uomo-robot o logistica.

Thesis Proposals (ENG)

1. Lidar + RFID Data Fusion for Object Association in Dynamic Environments
   This thesis proposes to study a data fusion technique between Lidar and RFID for object association in complex environments. After mapping with Lidar and clustering via DBSCAN, temporal cross-correlation between the measured RFID phase and the estimated phase (based on cluster motion) is used to associate each cluster with its corresponding RFID tag. The goal is to identify and track moving objects with unique IDs.
   
2. Lidar + UWB Integration for Accurate Object Tracking
   This work explores the fusion of UWB ranging measurements and Lidar data to achieve robust association between tracked objects and their positional data (as in Thesis #1). After clustering the Lidar map, the distances measured by UWB tags are matched with the distances of cluster centroids to uniquely identify the objects.
   
3. IoT Interface for RFID Readers with MQTT
   This thesis involves the development of a Python-based software interface for RFID readers compatible with MQTT. The collected data are analyzed to generate real-time alert messages (warning/danger), which are sent to a remote server following customizable logic. The interface is designed for use in industrial or real-time monitoring environments.
   
4. RFID-Based Tracking System with a Robotic Manipulator
   The goal of this thesis is to design a system that allows a robotic manipulator to follow a mobile RFID tag. Using antennas mounted on the robot arm, the system estimates the direction of the tag and follows it, both in 2D (planar scanning) and in 3D configurations, enabling advanced localization and pointing capabilities.
   
5. Computer Vision + RFID Fusion for Object Recognition and Navigation
   This thesis analyzes a data fusion system for robotic navigation or object recognition using a combination of visual markers (e.g., QR, ARUCO, infrared markers) and RFID tags. Robots can identify “dual-technology” objects (vision + RFID), even inside containers or in the presence of partial occlusions, improving the reliability of perception.
   
6. Integration of RFID Readers in ROS2 for Cognitive Robotics
   This work focuses on integrating an RFID reading system within the ROS2 ecosystem. ROS nodes will be developed for acquiring, publishing, and processing RFID data, enabling their use in complex robotic scenarios such as object tracking, human-robot interaction, or logistics.

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Bio: Andrea Motroni received the M.E. (with honors) degree in Telecommunication Engineering and the Ph.D. (with honors) degree in Information Engineering from the University of Pisa, Pisa, Italy, in 2017 and 2021, respectively, where he is currently an Assistant Professor in Electromagnetism. In 2020, he was a Visiting Ph.D. Student with the Graz University of Technology, Graz, Austria. Dr. Motroni was a Finalist at the IEEE CRFID Educational Mega Challenge (2018), a recipient of Best Paper Award and Best Student Paper Award at IEEE RFID-TA 2019, and a recipient of the Young Scientist Award from the International Union of Radio Science, Commission B in 2021 and 2023. In 2022, Dr. Motroni was awarded with the IEEE/ABB Italy Section award for PhD Thesis and with the “2021 Best PhD Dissertation in the field of Information and Industrial Engineering” from University of Pisa. He won the Best Poster Award at IEEE M\&N 2022 and at IEEE RFID 2023 conferences, and the Best Paper Award at IEEE GreenCom 2024. In 2024 he was also awarded with the “Barzilai Award” from SIEm (Italian Society of Electromagnetism). He currently serves as Associate Editor for the IEEE Journal of Radio Frequency Identification (IEEE JRFID), and he is an executive member of the IEEE CRFID’s Technical Committee on Motion Capture and Localization. He is the Publicity Chair of the IEEE CRFID. He has joined the Organizing Committee and has been Session Chair of several IEEE international conferences. In 2019, he was the President of the IEEE Student Branch of the University of Pisa, for which now serves as Counselor. His current research interests include indoor radiolocalization systems, with specific focus on UHF-RFID and UWB technology for robot and vehicle localization, the integration of robotic systems with RFID towards new systems for industry and logistics, UHF-RFID smart gates and other RFID-based applications for Internet of Things, Industry 4.0, and people safety in both indoor and outdoor environments. His research activity is evidenced by more than 70 scientific contributions in International Journals and Conferences, patents intellectual property and leading positions in research projects.