Federico Vesentini
Papers
9
Total Citations
64
H-Index
5
About
Federico Vesentini is a robotics researcher whose work centers on trajectory planning, motion control, and collision avoidance for robotic systems—from industrial manipulators to mobile robots. His most impactful contribution, a novel inverse dynamic model for 3-DoF delta robots (24 citations), addresses a critical need for high-speed, precise pick-and-place operations in manufacturing. Vesentini has also advanced the application of Model Predictive Control (MPC) for mobile robot motion planning, integrating the velocity obstacle paradigm to enable real-time, constraint-aware navigation. His stochastic modeling of cart-like wheeled robots using a Brownian–Markov approach (8 citations) offers a probabilistic framework for understanding robot motion in uncertain environments. Beyond mobile platforms, Vesentini has extended velocity obstacle methods to anthropomorphic arms and planar manipulators, demonstrating the versatility of decentralized collision-avoidance strategies. His recent work on Dynamic Movement Primitives with control barrier functions (2025) pushes trajectory generation toward safer, constrained motion. With a growing citation record and contributions spanning state estimation, payload identification, and multi-agent coordination, Vesentini is establishing himself as a thoughtful engineer bridging theoretical control with practical robotics challenges.
Research Focus
Key Achievements
Top Papers
- 1A novel inverse dynamic model for 3-DoF delta robots24 citations · 2022
- 2
- 3A Brownian–Markov stochastic model for cart-like wheeled mobile robots8 citations · 2023
- 4Velocity obstacle-based trajectory planner for anthropomorphic arms7 citations · 2023
- 5
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- 8Gray-Box Model Identification and Payload Estimation for Delta Robots3 citations · 2020
- 9