Project: AI4FOREST - An Artificial Intelligence Approach for Forestry Robotics in Environment
Survey and Inspection (PRIN 2022 - NextGenerationEU funded)
Role Focus: Developing AI-driven autonomous navigation and environmental modeling techniques for forestry robots, contributing to the creation of detailed forest Digital Twins.
Key Responsibilities & Achievements:
• Designing and developing multi-fidelity simulation environments (virtual models) to train and validate AI-based robot controllers for complex forest scenarios.
• Creating machine learning-based controllers focused on terrain traversability assessment and robust navigation decision-making in unstructured environments.
• Developing and implementing advanced SLAM-based navigation algorithms for precise
localization and mapping, enabling the creation of forest Digital Twins.

Project Name: SEDUCE - Designing Spatially Distributed Cyber-Physical Systems Under Uncertainty
Thesis Title: DSLs for Modeling, Coordinating, and Programming Multi-Robot Systems
Objective: Craft a high-level programming and modeling approach for Multi-Robot Systems (MRS) that bridges the gap between mission design and execution, simplifiying the intricacies of robotics software development.
Achievements:
• Developed a programming methodology tailored for coordinating MRS, enabling developers to focus on MRS behavior and achieve readable, reusable, and maintainable code.
• Introduced modeling techniques to represent the high-level design of MRS missions, using graphical representations to depict complex mission sequences and interactions.
• Consolidated paradigms that allowed a smooth transition from high-level design of MRS missions to concrete implementation, preserving the original logic and structure.
• Verified the scalability and effectiveness of the approach through rigorous testing across various MRS scenarios, even in increasingly complex scenarios in a Robot Operating System (ROS) environment.
• Contributed to improving the efficiency and quality of MRS software development, paving the way for more complex, scalable, and effective MRS missions in the future.
• Published research findings in various conferences and journals, collaborating with prominent professors and researchers in the field.
Additional Note: This thesis was supported by a full scholarship.

GSSI Scientific Collaboration on the topic "Robotic Mission Specification"
Research Focus: Enhancing Robotic Mission Specification through Advanced Coordination Languages.
Objective: Elevate the capabilities of robotic missions through the integration of advanced coordination
languages for mission specification and execution, ensuring a scalable, maintainable, and effective mission output.
Achievements:
• Leveraged coordination languages to streamline robotic mission specifications, emphasizing dynamic
planning and seamless process interaction.
• Pioneered scalable design patterns that serve as foundational blocks rooted in real-world requirements,
effectively mapping out robotic missions.
• Demonstrated exceptional flexibility in mission design by harnessing the power of pattern composability
and coordination languages.
• Validated methodologies through rigorous testing, proving efficiency, scalability, and adaptability in various
robotic mission scenarios.
• Fostered international collaboration and knowledge exchange, laying the groundwork for next-generation
multi-robot system missions and contributing to the future of robotic research.
Additional Note: This research visit was under guidance of Prof. Patrizio Pelliccione.

Project Name: Cap 20-25 for Innovation Initiative
Objective: Develop robust models and control mechanisms for robotic vehicles, specifically differential drive robots.
Achievements:
• Constructed formal mathematical models for differential drive robotic vehicles, serving as the foundation for subsequent control strategies.
• Conducted an exhaustive analysis on the controlability of non-linear systems, focusing on optimal control for obstacle avoidance and energy efficiency.
• Designed and implemented optimal control algorithms for these non-linear dynamical systems, enhancing practical applications of robot motion planning and control in complex environments.

Additional Note: This internship was supported by a competitive grant awarded through Challenge 2 of CAP 20–25,
focused on "Innovative systems and services for transport and production".

Responsibilities:
• Developed and delivered comprehensive mathematics instruction to middle school students, integrating innovative teaching methods to facilitate understanding and spark interest in the subject.
• Introduced students to fundamental concepts in computer science, fostering computational thinking and familiarizing them with basic coding principles.
• Created a conductive learning environment that catered to diverse learning styles and abilities, fostering a passion for learning and promoting academic success.