Papers
61
Total Citations
1,165
H-Index
19
About
Giovanni Legnani is a prominent Italian robotics researcher whose career has centered on industrial robot manipulators, control systems, and calibration methodologies. His work spans trajectory tracking control, geometric calibration, friction modeling, and force/velocity control, establishing him as a leading authority on improving the accuracy and performance of industrial robotic systems. Among his most influential contributions is his experimental investigation of trajectory tracking control strategies for SCARA robots (126 citations), alongside comparative studies of three distinct calibration algorithms for geometric parameter identification (76 citations). His geometrical interpretation of the AX=XB matrix equation for hand-to-sensor calibration (75 citations) offered an elegant and practically significant mathematical framework widely adopted by subsequent researchers. Legnani has made sustained contributions to understanding and compensating for joint friction — a notoriously complex challenge in robotics — developing increasingly sophisticated models that incorporate temperature effects (51 citations, 35 citations), a factor often overlooked in earlier literature. His iterative-learning hybrid force/velocity control approach (37 citations) further demonstrates his commitment to practical industrial applications. More recently, Legnani has extended his expertise into rehabilitation robotics, co-authoring research on powered exoskeletons for spinal cord injury patients (43 citations), reflecting a broader humanitarian dimension to his engineering legacy.
Research Focus
Key Achievements
Top Papers
- 1On the trajectory tracking control of industrial SCARA robot manipulators126 citations · 2002
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- 7Friction modeling with temperature effects for industrial robot manipulators51 citations · 2015
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- 9Iterative-Learning Hybrid Force/Velocity Control for Contour Tracking37 citations · 2010
- 10Modelling the temperature in joint friction of industrial manipulators35 citations · 2017