Temporal Learning for End-Effector Position Estimation under Aerodynamic Disturbances in Aerial Continuum Manipulation

arXiv:2609.28716v1 Announce Type: cross 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 gated recurrent unit (GRU) used for comparison. On unseen test experiments, the CfC achieves an RMSE of (22.00pm1.70mm) over five random seeds, compared with (36.38pm3.58mm) for the MLP and (27.72pm2.92~mm) for the GRU, corresponding to reductions of (39.52%) and (20.62%), respectively. These results demonstrate the effectiveness of mbox{continuous-time} learning for mbox{end-effector} position estimation under aerodynamic disturbances relative to static and mbox{discrete-time} learning methods.

Source: arXiv cs.AI | 2026-09-25