LATIDIA · Robótica

Aprendizaje temporal para la estimación de la posición del efector final bajo perturbaciones aerodinámicas en la manipulación del continuo aéreo

arXiv:2609.28716v1 Tipo de anuncio: nuevo Resumen: Este documento investiga las redes neuronales temporales para \mbox{end-effector} posición \mbox{estimación} de un manipulador de continuo aéreo (ACM) que opera bajo eff aerodinámico

La noticia

arXiv:2609.28716v1 Announce Type: new Abstract: This paper investigates temporal neural networks for \mbox{end-effector} position \mbox{estimation} of an aerial continuum manipulator (ACM) operating under aerodynamic effects induced by the unmanned aerial vehicle (UAV). An experimental dataset is collected under stationary (\mbox{rotor-off}) and \mbox{free-hovering} conditions across continuum robot (CR) configurations and UAV altitudes, providing \mbox{end-effector} position measurements with and without aerodynamic residuals. To establish a nominal framework, \mbox{strain-parameterized} kinematic models with progressively richer strain bases are evaluated to balance model complexity and prediction accuracy. The selected nominal model then serves as the baseline for 3D position residual estimation using a \mbox{closed-form} \mbox{continuous-time} (CfC) neural network, with a multilayer perceptron (MLP) and a