Model-Free Magnetic Servoing for Pose Control of Capsule Robots

Abstract

This article proposes a model-free magnetic servoing technique for closed-loop pose control of capsule robots. The system employs an internal permanent magnet (IPM) inside the capsule and an external permanent magnet (EPM) manipulated by a robotic arm. Control feedback is directly derived from magnetic field measurements (via a sensor array) and optical tracking of the EPM’s pose. To enable model-free control, we develop a feature extraction method that condenses the IPM’s magnetic sensor data into a compact representation. Based on this magnetic feature, we derive a Jacobian matrix that directly maps the feature space to the robotic arm’s joint configuration space. To adaptively handle system nonlinearities, the Jacobian matrix is iteratively updated using an unscented Kalman filter, bypassing the need for an explicit dynamic model. Experimental results demonstrate the effectiveness of our approach, with average tracking errors of 0.62 mm in position and 0.75$^{\circ }$ in orientation during simultaneous pose control tasks.