Temporal neural networks improve drone arm position tracking under wind
Temporal Learning for End-Effector Position Estimation under Aerodynamic Disturbances in Aerial Continuum Manipulation
RoboticsArtificial IntelligenceMachine Learning
Summary
Estimating the position of a robotic arm attached to a drone is tricky because the drone's propellers create wind that pushes on the arm. The authors studied how to use special types of neural networks that learn from sequences of data over time to better guess where the arm's tip is. They found that their chosen continuous-time network reduced errors in position estimates compared to other methods. This helps improve control and safety for flying robots with flexible arms.
What this means in practice
- •For drone operators: Improve accuracy of robotic arm tip position tracking under airflow from drone rotors for better task execution.
- •For robotics engineers: Incorporate continuous-time learning models to enhance real-time position estimation of flexible aerial manipulators affected by aerodynamic forces.
Authors
Niloufar Amiri, Houman Masnavi, Farrokh Janabi-Sharifi
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 \(22.00\pm1.70~\mathrm{mm}\) over five random seeds, compared with \(36.38\pm3.58~\mathrm{mm}\) for the MLP and \(27.72\pm2.92~\mathrm{mm}\) 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.