Francesca Bocconcelli

Development of an autonomous relative navigation system for space missions using sensor fusion. An Extended Kalman Filter will be implemented on a floating platform called REACSA to combine heterogeneous sensor data and estimate the relative position with respect to an orbiting target.

The aim of my project was to use sensors provided by SICK company to build a small robot capable of autonomous movement and voice command response. This project focused on integrating the sensors with a control system that allowed the robot to navigate its environment and execute tasks based on vocal instructions.

I managed the driverless department for the development of the software needed for the realization of a totally autonomous racing car.

I worked on the development of path tracking algorithms for F1Tenth racing car using ROS2 framework.

Implementation of an AI for the Tablut Competition, which consists in the realization of a player for an ancient Nordic strategy board game.

Development of an algorithm for recognition and tracking of cancer cells. Research carried out with the Rizzoli Orthopedic Institute.

I represented the Australia's country in this student-led simulation of a United Nations conference. In particular, I debated about climate change and about possible solutions of this problem.

Thesis: Safety-Constrained Autonomous Relative Navigation via Multi-Sensor Perception for the Floating Satellite Simulator REACSA - Pose Estimation via Multi-Sensor Fusion using an Extended Kalman Filter (EKF) with dual onboard sensing - Safety-constrained Adaptive Control Strategy ensuring collision avoidance while tracking a previously localized target

Tesi di Laurea: Implementazione di un Algoritmo di Riconoscimento e Tracciamento di Cellule Tumorali - Rilevamento di cellule tumorali in immagini in vitro tramite modello Faster R-CNN - Tracciamento delle cellule precedentemente identificate mediante algoritmo SORT, basato su Hungarian Algorithm, Intersection over Union (IoU) e filtro di Kalman