Papers
7
Total Citations
50
H-Index
6
About
Matteo Morelli is a leading researcher in model-driven engineering for robotics, with a focus on safety-critical autonomous systems. His work bridges the gap between high-level task planning and robust execution, pioneering the use of Behavior Trees to implement PDDL plans—a contribution that has reshaped how robots sequence actions in dynamic environments. Morelli’s most cited paper (11 citations) introduces a design-time safety assessment method using fault injection simulation, addressing the pressing need for verifiable safety in collaborative and autonomous robots. He has also advanced the development of drone software through the Papyrus for Robotics framework (8 citations), and demonstrated control-scheduling co-design for quadcopters (8 citations). His research consistently emphasizes early validation through simulation and formal verification, as seen in his work on automated generation of robotics applications from Simulink and SysML models. Morelli’s recent efforts toward a verifiable toolchain for robotics (2024) aim to improve robot autonomy in complex, unstructured environments. With a career marked by contributions to model-based design, safety analysis, and plan execution, Morelli is shaping the future of dependable, autonomous robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Designing Drone Systems with Papyrus for Robotics8 citations · 2021
- 3
- 4Optimized Execution of PDDL Plans using Behavior Trees7 citations · 2021
- 5
- 6
- 7Towards a Verifiable Toolchain for Robotics4 citations · 2024