Albano Lanzutti
Papers
7
Total Citations
805
H-Index
6
About
Albano Lanzutti is a leading researcher in robotics, whose work has fundamentally advanced the field of trajectory planning for industrial manipulators. His primary research areas focus on optimizing robot motion through sophisticated path and trajectory planning algorithms, with a particular emphasis on minimizing both execution time and mechanical jerk—the rate of change of acceleration. Lanzutti’s most significant contribution is the development and experimental validation of minimum time-jerk algorithms, a body of work that has garnered over 800 citations. His seminal 2015 overview, "Path Planning and Trajectory Planning Algorithms: A General Overview," alone has been cited 427 times, serving as a foundational reference for the field. Through rigorous experimental validation on industrial robots, he demonstrated that his techniques could simultaneously achieve fast execution and smooth motion, directly improving manufacturing efficiency and robot longevity. Beyond industrial applications, Lanzutti has also explored the use of haptic systems in neurosurgery, showcasing the broader impact of his work in precision-critical fields. His research remains essential reading for any engineer or student seeking to understand the practical optimization of robotic motion.
Research Focus
Key Achievements
Top Papers
- 1Path Planning and Trajectory Planning Algorithms: A General Overview427 citations · 2015
- 2Trajectory Planning in Robotics139 citations · 2012
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- 7A Master-Slave Haptic System for Neurosurgery2 citations · 2011