Stefano Mutti
Papers
6
Total Citations
97
H-Index
5
About
Stefano Mutti is a leading researcher in industrial robotics and automation, with a focus on optimizing multi-robot cells, motion planning, and human-robot collaboration. His major contributions include pioneering the use of nested meta-heuristic swarm algorithms, such as Ant Colony optimization, to solve complex task positioning and kinodynamic motion planning problems for redundant robots in machining and additive manufacturing. His 2021 paper on optimal task positioning has garnered 28 citations, while his 2019 work on robot motion planning for manufacturing applications has 25 citations, both highlighting his impact on efficient, automated production. Mutti also advanced mobile robotics through UKF vision-based kinematics calibration for improved tracking and docking, and explored deformable object estimation for collaborative mobile transportation using depth imaging. His 2022 paper on hierarchical manipulability maximization for robotic work-cell design further underscores his expertise in enhancing robot dexterity and workspace utilization. With a growing citation record and a focus on bridging theoretical optimization with practical industrial applications, Mutti’s work is instrumental in advancing autonomous and collaborative robotic systems for modern manufacturing.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6