Paolo Righettini
Papers
17
Total Citations
93
H-Index
7
About
Paolo Righettini is a distinguished robotics and mechatronics researcher whose work has significantly advanced the design, analysis, and control of parallel kinematic robots (PKMs) and multi-axis machinery. His research spans modal and kinematic analysis, servo-axis design, dynamic control, and real-time optimization — areas where he has consistently pushed the boundaries of both theory and experimental validation. Righettini's most recognized contributions include a 2021 study on the modal kinematic analysis of parallel robots with low-stiffness transmissions (13 citations), foundational pneumatic PKM design work dating back to 2001 (11 citations), and innovative neural network approaches for real-time robot task-time optimization (10 citations). His experimental investigations into high-speed 4-DOF parallel robots using inverse dynamics control further demonstrate his commitment to bridging theoretical frameworks with practical industrial applications. Particularly noteworthy is his long-standing mechatronic design philosophy, evident across two decades of publications, integrating mechanical, electronic, and control engineering disciplines into cohesive robotic systems. More recent contributions — including MEMS-based real-time joint friction identification (7 citations) — highlight his embrace of emerging sensing technologies. With a steadily growing citation profile, Righettini represents a reliable and evolving voice in advanced robotics research.
Research Focus
Key Achievements
Top Papers
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- 9Machatronic design of a 3-DOF parallel translational manipulator4 citations · 2002
- 10Experimental Evaluation of Centralized Control Strategies on a 5R Robot3 citations · 2024