Raffaele Giannattasio — accepted AI-conference papers

2026

Optimal Trajectory Planning for Human-Like Energy Efficient Motion of Lower Limb Exoskeletons

Raffaele Giannattasio, Nicoló Boccardo, Elena De Momi, Matteo Laffranchi

RA-L 2026

Motion planning is a critical aspect of lower-limb exoskeleton control. Conventional motion planning approaches compute reference trajectories in task space, leading to infeasible or uncomfortable joint level motions. Conversely, joint space planning methods ensure feasibility but may fail to reprod

Cited by 0 Source ↗Scholar ↗

2024

An adaptable ankle trajectory generation method for lower-limb exoskeletons by means of safety constraints computation and minimum jerk planning

Raffaele Giannattasio, Stefano Maludrottu, Gaia Zinni, Elena De Momi, Matteo Laffranchi, Lorenzo De Michieli

ICRA 2024poster

This paper presents a method to compute smooth ankle trajectories for lower limb exoskeletons with powered ankle joints. The proposed approach defines ankle trajectories using four polynomial functions, each representing one of the four primary phases of gait. These polynomials are computed accordin…

Cited by 6 Source ↗Scholar ↗