π€ AI Summary
This work proposes a soft electronic skin based on a magnetosensitive mechanism to overcome the limitations of conventional soft robotic skins, which often suffer from large perceptual blind spots, low spatial resolution, and a trade-off between compliance and high-fidelity tactile sensing. The design integrates tunable porous lattice structures fabricated via selective laser sintering (SLS) 3D printing with embedded permanent magnets, enabling conformal deployment on complex curved surfaces. A Hall sensor array captures magnetic field perturbations induced by contact, and a convolutional neural network enables real-time, super-resolution reconstruction of both contact location and normal force. By co-optimizing mechanical compliance and magnetic transduction performance, the system achieves sub-centimeter positioning accuracy, substantially reduces perceptual blind zones, and demonstrates strong potential for scalable deployment as full-body skin in safe humanβrobot interaction scenarios.
π Abstract
This paper presents a magnet-based robotic skin that integrates a multilayer soft lattice with distributed Hall-effect sensor arrays and a tactile super-resolution model. External contact forces are converted to magnetic field changes by embedded permanent magnets, and the lattice spreads these changes across the sensing domain. This gives each sensor a large, overlapping receptive field and enables a large sensing area with minimal blind spots. Lattice parameters are tunable, enabling joint adjustment of mechanical compliance and transduction characteristics. An implicit modeling workflow and selective laser sintering (SLS) 3D printing support rapid fabrication of conformal, high-complexity structures. A convolutional neural network trained on experimental measurements estimates contact location and normal force in real time. Experiments validate localization accuracy and indicate scalability to larger surfaces, suggesting applicability to whole-body robotic skin and safe human-robot interaction.