My first goal in my Ph.D. is to develop a Simultaneous Localization and Mapping (SLAM) system to be applied in an indoor environment that has to be fully-autonomous once started. We think that for mobile robot companions being able just to navigate an environment is not enough. They need to understand where they are, have a memory of the past visited locations and react to changes in the environment (moved objects, inaccessible areas, new obstacles...). Moreover, they must do this in an autonomous way once powered-on.
To achieve this we are combining a Visual-SLAM system with a Model Predictive Control (MPC) mechanism to obtain area coverage and relocalization of the mobile platform (i.e. Active SLAM).
Afterward, this system will be further developed for example by adding human-robot and robot-environment interactions capabilities or cooperations between multiple agents.
Within the Robot Perception Group, I am also working with other members of the team in Multi-agent systems for aerial motion capture using perception (AirCap) and Deep Reinforcement Learning (AircapRL).
I've obtained my B.Sc. in Computer Science Eng. (2013) and M.Sc. in ICT for Internet and Multimedia Eng. (2019) at the University of Padua in Italy. Shortly after I started working as a research intern at MPI-IS in Tuebingen, Germany where now I am pursuing my doctoral degree as a member of the Robot Perception Group (RPG).
Autonomous Systems Active SLAM Reinforcement Learning Robotics
Autonomous MoCap systems, like AirCap, rely on robots with on-board cameras that can localize and navigate autonomously. More importantly, these robots must detect, track and follow the subject (human or animal) in real time. Thus, a key component of such a system is motion planning and control of multiple...
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems