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Politecnico di Torino, Torino (TO), ITA
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A novel control architecture reduces steady-state attitude error in underactuated spacecraft by integrating model predictive control with a physics-informed neural network and a Lyapunov safety layer.
Ditch the features: VinePT-Map uses vine trunks and poles as persistent landmarks, enabling robust robot localization across seasons in challenging vineyard environments.
